IEEE Engineering in Medicine and Biology - vol 25 - nb 01 - January-February

IEEE ENGINEERING IN

MEDICINE AND BIOLOGY Magazine VOLUME 25 • NUMBER 1 ■ http://EMB-Magazine.bme.uconn.edu ■ JANUARY/FEBRUARY 2006

IEEE ENGINEERING IN VOLUME 25 • NUMBER 1 JANUARY/FEBRUARY 2006 http://EMB-Magazine.bme.uconn.edu

MEDICINE AND BIOLOGY Magazine

Communication Theory and Molecular Biology

Themes 28

Communication Theory and Molecular Biology at the Crossroads Elebeoba E. May

30

Claude Shannon: Biologist Thomas D. Schneider

34

Should Genetics Get an Information-Theoretic Education? Gérard Battail

© EYEWIRE

46

The Quest for Error Correction in Biology Manish K. Gupta

54

Digital Parity and the Composition of the Nucleotide Alphabet Dónall A. Mac Dónaill

62

Examining Coding Structure and Redundancy in DNA Gail L. Rosen

69

Detecting Structure in Parity Binary Sequences Diego Luis Gonzalez, Simone Giannerini, and Rodolfo Rosa

82

Finding Large Domains of Similarly Expressed Genes Daniel Nicorici, Olli Yli-Harja, and Jaakko Astola

90

Classification of Escherichia coli K-12 Ribosome Binding Sites Elebeoba E. May, Mladen A. Vouk, and Donald L. Bitzer

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

1

Departments 4 From the Editor Wishing the Best for Everyone in the New Year!

5 Letters to the Editor Readers’ Own Recommendations

6 A Look at... Shape and Function from Motion in Medical Imaging: Part 2

10 Society News Students Serving Shanghai’s Poor

12 Student’s Corner Shanghai, China: A City of Growth and Friendship

14 Book Reviews 19 BME Company Profiles A Microsystem Design House

22 Around the World The First International Conference on the Development of BME in Vietnam

24 Senior Design National Student Design Competitions for Biomedical Engineers

26 Point of View A Proposal for ABET Criterion 9

98 Issues in Ethics Moral Blindness and the Guidant Recall

100 Patents What a Difference a Day Made

101 Retrospectroscope Who Was the First Genetic Engineer?

102 Engineering in Genomics Cluster Formation as a Tool for Development in Medicon Valley

106 Cellular/Tissue Engineering Bone and Cartilage Tissue Engineering for Facial Reconstructive Surgery

110 Conference Calendar 114 2005 Annual Index

Mission Statement The Engineering in Medicine and Biology Society of the IEEE advances the application of engineering sciences and technology to medicine and biology, promotes the profession, and provides global leadership for the benefit of its members and humanity by disseminating knowledge, setting standards, fostering professional development, and recognizing excellence.

2

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

NOTES FOR CONTRIBUTORS IEEE Engineering in Medicine and Biology Magazine is a theme-article publication that covers the full range of fields within biomedical engineering (BME), with each issue covering one theme. Articles are written for technically knowledgeable readers who are not necessarily specialists in the theme topic. A sample list of theme topics of interest includes: biochemical engineering, biocontrols, bioinformatics, biomems, biomaterials, biomechanics, biosignal processing, biotechnology, cellular and tissue engineering, clinical engineering, imaging and image processing, information technology, instrumentation, sensors and measurements, micro- and nanotechnolgy, neural systems and engineering, physiological systems modeling, proteomics, radiology, rehabilitation engineerNOTES FORinCONTRIBUTORS ing, robotics surgery, and telemedicine. In addition to the theme articles, which are invited contributions, the magazine also publishes unsolicited features Coming Attractions that areissues of interest a broad segment IEEE Engineering in Medicine and Future of thetomagazine will haveofthe themes of Teaching Engineering Biology Magazine readers. Tissue Engineering, Biotechnology, and Wearable in Medicine and Biology. IEEE Engineering Medicine andonBiology also publishes 20 Sensors/Systems and in Their Impact BME. Magazine Contributions on themeover topics regularly scheduled columnsarticles for readers interested in industry, academia, and are invited. Other technical and feature stories of interest to biomedgovernment. are peer reviewed andare written by experts in the field. ical engineersAll arearticles also welcome. All articles submitted anonymously for On the magazine comprehensive, in-depth tutorial, peeroccasion, review. Letters to the publishes editor, notes, commentaries, and review, other pieces of and survey articles. Letters to the editor, notes, other related pieces personal opinion will be published as such. Wecommentaries, also seek pressand releases of personal in opinion will be published as such. We also seek press releases to activities your company, organization, or school. related to activities your company, school. Manuscripts areinONLY accepted organization, in electronic or format through Manuscript Manuscripts aresite ONLY accepted in electronic format through Manuscript Central at the Web http://embs-ieee.manuscriptcentral.com. Instructions for Central Web and site how http://embs-ieee.manuscriptcentral.com. Instructions for creatingatanthe account to electronically submit a manuscript are available creating an account to original electronically submit aormanuscript available at at the Web site. Doand nothow send submissions revisions are directly to the the Web site. Do Ifnot send or revisions directly to the editorEditor-in-Chief. you areoriginal unable submissions to submit your contribution electronically or in-chief. If you are to submit yourplease contribution or have queshave questions onunable manuscripts style, contactelectronically the Editor-in-Chief: Dr. tions on Enderle, manuscripts style, please contact the editor-in-chief: D. Enderle, John D. Biomedical Engineering Director, UniversityJohn of Connecticut, Program DirectorRoad, for Biomedical Connecticut, 260 Glenbrook Storrs, CTEngineering, 06269-2247.University Voice: +1of860 486 5521.Room Fax: 217, 260486 Glenbrook Road, Storrs, CT 06269-2247 USA. Voice: +1 860 486 5521. +1 860 2500. E-mail: [email protected]. Fax:As +1 860 2500. E-mail: [email protected]. per 486 IEEE policy on standards for publications, review and editorial As per policy are on part standards publications, editorial handling of IEEE manuscripts of the for paper submision review process and to guarantee handling of manuscripts are part of the paper submision process to guarantee quality control. quality control. Make the Deadline Make In orderthe to Deadline have your news published in the magazine in a timely fashion, In order to have news published in the thefollowing magazinedates: in a timely fashion, please submit youryour notices to the editor by please submit your notices to the editor by the following dates: Issue Deadline Issue Deadline Jan/Feb October 1 Jan/Feb September 11 Mar/April December Mar/Apr November1 1 May/June February May/June January July/Aug April 1 1 July/Aug March Sep/Oct June 1 1 Sep/Oct May 1 1 Nov/Dec August Nov/Dec July 1 IEEE Engineering in Medicine & Biology Magazine (ISSN 0739-5175) (IEMBDE) is published Engineering bimonthly by Institute Electrical and Electronics Engineers, Inc., IEEE IEEE in The Medicine andofBiology Magazine (ISSN 0739-5175) (IEMBDE) is Headquarters: 3 Park Ave., Floor, of New York, NY Telephone 212 published bimonthly by The17th Institute Electrical and10016-5997. Electronics NY Engineers, Inc.,+1IEEE 419 7900. NY3Telecopier 212 752 New 4929.York, NY Telex: 236-411 (International messages Headquarters: Park Ave., +1 17th Floor, NY 10016-5997. NY Telephone +1 212 only). IEEE Center (for752 orders, address(International changes, Educational 419 7900. NYService Telecopier +1 212 4929.subscriptions, NY Telex: 236-411 messages Activities, Region/Section/Student 445 Hoes Lane, Piscataway, 08854. NJ only). IEEE Service Center (for Services): orders, subscriptions, address changes,NJEducational Telephone: +1 732 981 0060. Price/Publication Information: Individual copies: IEEE Activities, Region/Section/Student Services): 445 Hoes Lane, Piscataway, NJ 08854. NJ Members $10.00 (first981 copy only), nonmembers $20.00 per copy. (Note: add $4.00 Telephone: +1 732 0060. Price/Publication Information: Individual copies:postage IEEE and handling charge to copy any order $1.00 to $50.00, orders). $5.00 per Members $20.00 (first only),from nonmembers $60.00 including per copy. prepaid Subscriptions: year (included in Society fee) for each member of the IEEE Engineering in Medicine and Biology Society.Nonmember Member and nonmember subscription available on inrequest. Biology Society. subscription prices available onprices request. Available microAvailable in microfiche and microfilm. Copyright and Reprint Permissions: Abstracting fiche and microfilm. Copyright and Reprint Permissions: Abstracting is permitted with is permitted creditLibraries to the source. Libraries are photocopy limcredit to thewith source. are permitted to permitted photocopytobeyond thebeyond limits the of U.S. its of U.S. Law Copyright Law for use of(1)patrons: (1) those post-1977 articles carry Copyright for private useprivate of patrons: those post-1977 articles that carry that a code at athecode at the bottom of the first page, provided the per-copy fee indicated in the code is bottom of the first page, provided the per-copy fee indicated in the code is paid through paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA(2) 01923 the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA; preUSA; (2) pre-1978 For allreprint, other or copying, reprint,information, or republication 1978 articles withoutarticles fee. Forwithout all otherfee. copying, republication write information, write CopyrightsDepartment, and Permission Publishing Services, to: Copyrights andto: Permission IEEEDepartment, Publishing IEEE Services, 445 Hoes Lane, 445 Hoes Lane, Piscataway, NJ 08854 USA. Copyright © 2003 by the Institute of Piscataway, NJ 08854 USA. Copyright © 2006 by the Institute of Electrical and Electronics Electrical Engineers, Inc. All rights reserved. Printed in U.S.A. Periodicals Engineers,and Inc.Electronics All rights reserved. Printed in U.S.A. Periodicals postage paid at New York, postage at New York, NY and at additional mailing offices. Postmaster: Sendtoaddress NY andpaid at additional mailing offices. Postmaster: Send address changes IEEE changes to IEEE Engineering in Medicine & Biology Hoes Lane, Engineering in Medicine and Biology Magazine, IEEE,Magazine, 445 HoesIEEE, Lane, 445 Piscataway, NJ Piscataway, NJ 08854 USA. 08854 USA.

Printed in USA

JANUARY/FEBRUARY 2006

From the Editor wishing the best for everyone in the new year! John Enderle

wish to extend my best wishes to all for a Happy New Year! This year is shaping up to be an exciting year with great issues like this one on communication theory, coding theory, and molecular biology, which Guest Editor Elebeoba May put together. Following this issue, we will have issues on fMRI, clinical neuroengineering, direct humanitarian relief, cognitive technologies, and atrial fibrillation. Most of you know that Dr. Swamy Laxminarayan, a dear friend, passed away on 29 September 2005. An obituary highlighting his contributions was written by Ken Foster and Luis Kun and was published in the November 2005 issue of IEEE Engineering in Medicine and Biology Magazine.

I

Swamy has had huge impacts in just about all phases of the IEEE Engineering in Medicine and Biology Society (EMBS), from conferences to membership to publications. For this magazine, he was a guest editor on two bioterrorism issues, the first earning a well-deserved national award. We have just learned that Swamy was elected an IEEE Fellow for the class of 2006. Even after all these months, I still feel a sense of profound loss when I think of him. I will miss meeting him at EMBS conferences, catching up with each other’s lives, and enjoying his wit and wisdom. In the September 2005 “From the Editor,” I addressed student membership in professional societies and the

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE Editor-in-Chief John Enderle University of Connecticut Editorial Board Hojjat Adeli The Ohio State University Howard I. Bassen Food and Drug Administration Krzysztof J. Cios Univ. of Colorado at Denver and Health Sciences Center Pouran Faghri University of Connecticut Limin Luo Southeast University, Nanjing Jasjit Suri Biomedical Technologies Inc. Eugene Veklerov Lawrence Berkeley Laboratory Associate Editors A Look At Jean-Louis Coatrieux University of Rennes, France Book Reviews Paul King Vanderbilt University

Cellular & Tissue Engineering Maria Papadaki P&G Italian Research Center, Italy Clinical Engineering Stephen L. Grimes GENTECH COMAR Ruth Douglas Miller Temple University Emerging Technologies Dorin Panescu St. Jude Medical Faces and Places Andrew Szeto San Diego State University Genomics Harold (Skip) Garner University of Texas Southwestern Medical Ctr. Government Affairs Luis Kun National Defense University Industry Affairs Semahat Demir National Science Foundation Issues in Ethics John Fielder Villanova University

International News John Webster University of Wisconsin, Madison Patents Maurice M. Klee Fairfield, CT Point of View Gail Baura CardioDynamics San Diego, CA Regulatory Issues Robert Munzner DoctorDevice.com Grace Bartoo Instrumentation for Science and Medicine Retrospectroscope L.A. Geddes Purdue University Senior Design Jay Goldberg Marquette University Society News Barbara Oakley Oakland University Student Activities Lisa Lazareck Oxford University, U.K.

writing of recommendation letters; that is, I wrote that I will no longer write recommendation letters for students who are not student members of a professional biomedical engineering (BME) society. I knew that this philosophy would provoke a reaction from students in the BME program at the University of Connecticut (Uconn); I just wasn’t sure what the reaction would be. The outcome has been a very pleasant surprise, and serendipity has also played a role. Last spring, three students visited me about the lack of activities (beyond seminars) for the BME EMBS student club. We agreed to meet in the fall to see what could be done. Lyndon Charles, Ashley

IEEE PERIODICALS MAGAZINES DEPARTMENT

Managing Editor Desirée de Myer Art Director Janet Dudar Asst. Art Director Gail A. Schnitzer Business Development Manager Susan Schneiderman +1 732 562 3946 [email protected] Fax: +1 732 981 1855 Senior Advertising Production Coordinator Cathline Tanis Production Director Robert Smrek Editorial Director Dawn Melley Staff Director, Publishing Operations Fran Zappulla

Editorial Correspondence: Address to John D. Enderle, Program Director for Biomedical Engineering, University of Connecticut, Room 223 B, 260 Glenbrook Road, U-2157, Storrs, CT 06269-2157 USA. Voice: +1 860 486 5521. Fax: +1 860 486 2500. E-mail: [email protected]. Indexed in: Current Contents (Clinical Practice), Engineering Index (Bioengineering Abstracts), Inspec, Excerpta Medica, Index Medicus, MEDLINE, and listed in Citation Index. All materials in this publication represent the views of the authors only and not those of the EMBS or IEEE.

4

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

Reeners, and Chris Ambler did meet with me in the early fall with plans to make the EMBS student club more vibrant. At the first BME student meeting, four individuals were elected as officers of the club with six others in support roles. These students started meeting almost every Tuesday to get the process going. The first goal: form an EMBS student branch chapter with the first step of encouraging students to become members of EMBS (and other BME societies as well). We now have over 35 EMBS student members

at UConn, and more are signing up. The petition for the establishment of an EMBS student branch chapter has been completed and sent to the IEEE. The four EMBS student club seminars held in the fall semester have averaged about 75 students per seminar. Their next project is to do a BME career fair for early spring 2006 and also to hold some fun outings. Serendipity has played a huge role in this success story. There still will be some senior BME students joining EMBS as student members based on

the need for a recommendation letter. The best surprise is that most of the students signing up so far have been motivated by something different than the recommendation letter: a chance to learn about BME and to have some fun; really, this is what life-long learning should be about. For all the academics in EMBS, I would encourage you to try this at your university, and I wish you the luck that I have had at UConn. Until the next time, John Enderle

Letters to the Editor readers’ own recommendations ice editorial [see “From the Editor” November/December 2005]. Just a brief comment: Life-long learning for engineers in industry generally does not consist of attending conferences, participating in chapter activities, or attending more classes. I did not even find much difference in the quality of engineers between IEEE Members and nonmembers. We learn by very different means: application notes, design reviews, data sheets, and Web publications such as EE Design or Circuit Cellar, but, most of all, through lots of practice.

N

It would be sad if the IEEE would someday begin to endorse formal and mandated continuous education requirements like there are for nurses and other professions. It would just create another bureaucratic hurdle, and in the United States, we are facing enough of those as it is. Engineers worth their salt know best what they need for education after their degrees. They don't need to be told. Regards, Joerg Schulze-Clewing Cameron Park, California

I will mention your editorial [November/ December 2005] in my lecture to the biomechanical and biomedical students at the University of Iowa next week. The idea of professional (or student) membership is noteworthy. Keep up the good work. Fraternally, D.J. Anderson, P.E. Anderson Engineers, L.L.C. Minneapolis, Minnesota P.S. ABET stands for Accreditation Board for Engineering and Technology, not American Board for Engineering Technology.

Erratum

In the January/February 2003 (vol. 22, no. 1) issue of IEEE Engineering in Medicine and Biology Magazine, a reference was miscited in the article “Cell Membrane Electropermeabilization with Arbitrary Pulse Waveforms,” by T. Kotnik and D. Miklavcic (IEEE Transactions on Biomedical Engineering vol. 47, 2000). In [2], the page numbers are incorrect. The correct pagination is 1074–1081, not 1047–1081.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

5

A Look at ... shape and function from motion in medical imaging: part 2

n the first part of this article [1], some basic and generic issues concerning motion were reviewed. This article emphasized, among other critical points, the object descriptions (point, feature, region, boundary), view-based and model-based techniques, cooperation of distinct algorithms, and the fusion of sensors and information sources. Key questions about these motion estimation methods were also raised, regarding their merits, the impact of assumptions, the tradeoff between robustness and accuracy, and the requirements for assessment. This second article focuses on medical imaging aspects. It can be thought, at an early stage, that medical imaging can be considered simply by transferring and adapting this know-how to get the structural and functional patterns we are seeking. This natural trend, while it yields new clues and sometimes solutions, must be reconsidered with the maturity of the fields, namely, medical imaging on one hand and motion analysis on the other. The recent advances observed in magnetic resonance imaging (MRI), multislice computed tomography (MSCT), ultrasound (US), and optical imaging open new roads for improved diagnosis based on shape, tissue, and motion as well as advances in therapy from imageguided interventions and drug delivery tracking. This article is focused on macroimaging. A classification is proposed that departs from the classical views by identifying motion from imaging and vice versa and involves correction of motion and evoked motion. A future article will address new, challenging topics in nano- and microimaging, where motion should be a central component.

I

6

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Jean Louis Coatrieux

Featuring Medical Imaging for Motion

Revisiting the Generic Motion Estimation Methods

Much has been said about medical imaging, and our intent is not to reexamine the whole literature but to focus on the motion concerns. Several aspects that introduce significant differences in camera vision deserve to be underlined. The complexity of the imaging process is a major point. This is exemplified for US and MRI, as we will see throughout this article. The second challenge is represented by the shapes, structures, and functions; we must deal with highly complex tissular organization, mixtures of different components, a large variety of diseases (and subsequently of their appearances), small structures and patterns, and fast physiological mechanisms. The organs are highly deformable and subject to internal and surrounding constraints with strong global interactions. Most often, a major role is played by contrast agents which, while enhancing some properties, may bring additional variations and another time dimension. All these points require us to model the entire system involved, the sensing device, the objects under examination, and the physicalphysiological interactions (refer to [2]). It is my feeling that considering the data acquisition process as a black box and simply processing the resulting images cannot lead to a clear understanding of the features that can be tracked and extracted. I advocate that motion estimation and analysis must not be limited to considerations like spatial and temporal resolutions; the image variations under motion, due to tissue properties, deformations, and noise components, should be at the center of the modeling process. Then, tissue and shape characterization, structure to function, will have a sound basis when addressed through motion estimation and tracking.

From the above, it is clear that the validity of assumptions made in motion estimation can be at least questioned. Two basic tools, block matching and optical flow techniques, have been widely applied in medical imaging. A blockmatching approach assumes that the motion is similar for all pixels within a compact block of pixels, with the motion being derived as displacements of these blocks and estimated by maximizing or minimizing some cost function. Optical flow methods are based on the analysis of spatial and temporal gradients and rely on the assumption that the intensity of a particular point in a moving pattern does not change with time. In both cases, these assumptions are, in general, violated. In addition to the deformations, out-of-the-plane problems [features appearing and disappearing when two-dimensional (2-D) data are processed], inversion of movements, poor signal-to-noise ratio, and insufficient time sampling may lead to large interframe variations in displacements and intensities. The motion smoothness assumption is not verified as well. Boundary- (or edge-) and region-based methods (using either snakes or geodesic contours) have appeared as an alternative. They assume that contours are strong enough to guarantee their convergence or that regions have stable properties over time. Both are difficult to find in medical imaging; most often, the contrasts along edges are low, regions are not homogeneous, and their features are highly varying under motion. Despite these drawbacks, however, the performances of all these methods have been greatly improved by coupling block matching with optical flow and employing boundary and region tracking with deformable models, while relying on a sound choice of features JANUARY/FEBRUARY 2006

and an intimate knowledge of the “object-image process.” A good example of the latter can be found in US imaging with speckle tracking, resulting from interference of different back-scattering waves. Motion from Imaging

Most of the attempts at motion analysis in medical imaging rely on the application of generic methods. Classical approaches combining several techniques have been intensively exploited with a major target—the heart. In fact, a quantitative analysis of the heart wall deformations is considered as a major signature of myocardial ischemia and infarction. However, if these attempts bring some insights, they have been too often reduced to left ventricular (LV) analysis. They do not provide midwall motion information of the myocardium, which is critical in discriminating abnormal transmural and nontransmural patterns. MRI, echocardiographic, and (more recently) MSCT have been the major source of data for heart motion analysis. Tagged MRI takes advantage of the physics of imaging: it points out how the acquisition device, by physical tissue marking, may be of major importance in facilitating the motion tracking, rather than only focusing on motion estimation methods. The same comments apply to echocardiographic explorations, where the diffuse scatterer and pseudoscatterer effects must be taken into account as well as the out-ofplane problems. The image analysis methods, when applied in a blind way, lead to the same recurrent issues. What are the best tracking features to work with? Which reliability measure should be used to improve the estimation? How can it be incorporated into estimation schemes? It must be emphasized again that the object properties and the imaging system characteristics should be fully part of the investigation. For instance, the relative strain variation (see also the last section) over a cardiac cycle can go up to 30%, and it can be very difficult to measure. In addition to the magnitude of motion, the influence of inertial IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

forces should be examined. The study of regional contraction is still based on endocardial and epicardial wall motion excursion when the challenge is to get a quantitative assessment of the contractile state of the myocardium, free of noncontractile motion (translational and rotational movements of the heart). Therefore, it is a combination of motion, deformation, perfusion, and electrical and mechanical properties which must be the major objective.

tions into the vessels. The asynchronism of repetition time with the heart cycle or pulsatile flow, the presence of intravascular stents, and the movement of the patient can bring additional artifacts that make a quantitative assessment of vascular diseases more difficult. Therefore, over- or underestimation of a stenosis severity or aneurysm extent is a major concern, and even advanced segmentation methods cannot provide any correction to this fundamental problem.

Motion-Induced Image

In the previous case, the very standard vision of image analysis methods was used: the image sequence is available and image processing techniques for motion estimation can be applied. Here, the views are totally different. The “objects” of interest are made visible due to some internal motion. In other words, the image sequence results from some intrinsic motion, e.g., motioninduced image. This is the case of vascular imaging with MR angiography (MRA) which, rather than a single approach, offers a family of different techniques. If the gold standard for many vascular diseases remains X-ray angiography, MRA is considered as a relevant noninvasive alternative. MRA does not lead to motion as such but to indirect structural features, the vessel lumen. It appears again that the physics of the image, when nicely adapted, is a major component before extracting the information through image analysis methods. Unfortunately, blood motion or flow in vessels is far from simple. From the “object” viewpoint, stagnant flow (behaving like stationary tissue), laminar flow (consistent velocities across a vessel), vortex flow (fast flow in the vessel center, flow spirals near the walls in the presence of stenosis), or turbulent flow (fluctuation of different velocities) can be present. When looking at the physical device, many issues remain open in spin echo and gradient echo sequences, including “time-of-flight” effects, phase shifts with dephasing problems, magnitude variations in the MR signals, and inhomogeneous contrast-enhanced distribu-

Imaging with Motion Correction/Compensation

This is another intensively explored research area. Image artifacts resulting from motion, if not corrected or compensated, may lead to a major degradation (blurring) of image quality and, as a consequence, to poor clinical diagnostic performances. Two situations can be roughly considered: the uncontrolled movements and the intrinsic physiological movements. In the first case, we have to deal with the patient movements, especially when long-lasting acquisitions are performed. In the second case, respiration and heart cycles have to be taken into account. The organ is being moved when imaged and cannot be considered as stationary; therefore, corrections or compensations must be included during the reconstruction process. Imaging modalities like MRI, MSCT, and Rotational X (RotX), as well as positron emission tomography are concerned. These motion artifacts have different effects and different magnitudes, but understanding their key features is essential to yield appropriate solutions in a particular imaging method. The major issues are related to respiration and heart handling and their induced effect on other organs. In some cases (short-time period for relatively fast data acquisitions), breath holding can be enough to avoid artifacts. This can be at the expense of an increase in acquisition time if some data are discarded in order to keep only space-time coherent ones. Most often, however, gating is required. We must then face a multiple gating problem. JANUARY/FEBRUARY 2006

7

A Look at ... (continued)

Unfortunately, when looking more precisely to respiratory and cardiac patterns, a full reproducibility of cyclic patterns—the stationarity assumption—is far from being fulfilled. In addition, another time dimension is introduced by the contrast agents that may be used (flow and diffusion effects, for instance). In summary, multiple asynchronous motions for the objects, cumulating to the sensor motion, must be addressed. All these issues make the reconstruction of images challenging, in particular when the movements of organs are fast compared to the acquisition rate and the displacement velocity of the sensing device. They are even more challenging when the objects of interest are small with respect to the spatial resolution. Adaptive corrections and compensations aimed at dynamically tracking or repositioning the projections or slices and, conversely, the object, in such a way that a constant geometric relation is obtained between sensor and object positions despite their motions should remain an active research area in the near future. Reconstruction from Motion

The ill-posed problem I want to highlight here is very close to the basic situations encountered in computer vision in general. It is mainly inspired by Xray angiography where two projections (e.g., views) or a sequence of projections are available and the question is how to reconstruct the three-dimensional (3-D) object from these projections. Let us underline, however, that they are transmission images, far from camera images. Many attempts have been reported that rely on the extraction of some relevant features in the images (like centerlines and borders of vessels), epipolar geometry techniques for their matching between views, and additional constraints such as the ballistics of the imaging device and prior models of the vascular tree. The first situation—a static object (including the contrast agent) and moving source-detector—is perhaps the most comfortable. The motion will be related only to the sen8

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

sor, and the difficulty may come from the characteristics of the object (very small vessels, low contrasts, complexity of the vascular tree). The second case— a moving (deformable) object and static imaging system (like in coronarography)—is also classical. In both situations, the motion (strongly constrained in the first situation or totally unknown in the second situation) can play an important role. Its estimation in 2-D can be backprojected in 3-D in such a way that the tracking and reconstruction can be efficiently merged (hence, the “reconstruction from motion” paradigm). The third situation combines the two cases: a moving, deformable object observed with a moving sensor, like in Rot-X. Several facts may be considered to alleviate the difficulty of the reconstruction problem: the number of views is significantly increased, the imaging device ballistic can be roughly estimated and this could facilitate the disambiguation of motions, and electrocardiogram (ECG) gating can be performed in a short time lapse (avoiding the respiration movement factor). Nevertheless, the reconstruction of distal segments of arteries can only be achieved with a submillimeter precision which, knowing that beat-to-beat variations in cardiac positions may be up to 1 mm, remains very demanding. This is why the motion component is an essential element to incorporate into the reconstruction scheme. This is true also in MSCT. Evoked Motion

Evoked motion refers to motion that is artificially generated, and the best example of such a situation is elastography. This technique corresponds to the measure of periodic or quasi-static motion induced through the application of mechanical stimulus at the body/organ surface, and it enables the quantification of strain from an applied stress. Of course, intrinsic deformation of the tissues themselves, such as in the heart and vessels, can also be observed. The assumptions behind elastography are at least threefold: 1) differences exist in mechanical properties of

tissue components, 2) signal/image information is available to discriminate these differences, and 3) it yields new clues for diagnosis or therapy. It has been already shown that elastography may have a wide spectrum of applications, including changes in vessel wall elasticity (plaque heterogeneity and relation to rupture and thrombosis, risk of aneurismal tissue rupture), prostate screening for tumor detection to complement the palpation procedure or to display the region of destroyed tissue (heat-ablated, for instance), muscle fiber tension, and soft-tissue modeling for virtual reality surgical simulation. Many modalities are competing in the field of strain imaging and, in particular, in the areas of MR, US, and optical coherence tomography (OCT). Despite the recent improvements (depicting tissue structures within an organ) brought by these techniques, much more is expected. Only small strains can be estimated when we need to have a measure over a large interval of variations, and the applied compression must be controlled. The approximation made for tissue elasticity estimation based on gradient of the displacement should be analyzed more precisely; it can be easily corrupted by patient movement or undesired transducer motion. True 3-D anisotropy and incompressibility must be accessed and, as already emphasized, the interactions with the sensor signals must be understood (signal decorrelation with relative motions of the tissue scatterers and shear strain in US). The robustness and accuracy of the estimation methods (speckle tracking, for instance) must also be assessed. Conclusion

The nonstandard classification proposed in this article has attempted to show how specific features of medical imaging may affect motion extraction and tracking. If the main issues found in computer vision are also of concern in medical imaging, the importance of replacing the “black box” vision by an (continued on page 21) JANUARY/FEBRUARY 2006

The Journal with a Passion For Life

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Assistive technology is changing the world. Be part of its evolution.

IEEE Pub ID 018-163

■ Every issue of this groundbreaking journal profiles the future

Annual Rate: US$290

of physical and engineering science in biology and medicine.

IEEE Member Rate: US$35

From acoustic dynamics and nerve stimulation to electromyog-

RESNA and IFESS Member Rate: US$70

raphy, neuromuscular signal analysis and human performance

Email: [email protected]

measurement, you’ll read about the theory and practical clinical applications of the science that changes lives.

I N T E R N A T I O N A L

E L E C T R I C A L

S O C I E T Y

Technically Sponsored by: • Rehabilitation Engineering And Assistive Technology Society of North America • International Functional Electrical Stimulation Society

Join IEEE: www.ieee.org Submit papers: www.embs.org

Subscribe today! F U N C T I O N A L

ISSN: 1534-4320

S T I M U L A T I O N

Society News students serving Shanghai’s poor Barbara Oakley

reetings! By the time you read this, I will be turning the reins of the position of vice president, Member and Student Activities, to Prof. Jorge Monzon. Jorge has such a distinguished career that whatever I say about him, it would only be a drop in the bucket compared to the entirety of his distinguished career. Jorge has been an IEEE Engineering in Medicine and Biology Society (EMBS) Member for 24 years; he is currently a full professor at the Northeastern National University (UNNE) in Argentina. Among his most recent achievements, he was the General Conference Chair for the 25th EMBS Annual Conference (Cancun, Mexico, 2003). You will definitely be in good hands with Jorge!

G

The team (which included one international student from Delft University, The Netherlands) and local people.

Meanwhile, I am very pleased to have the opportunity to discuss some of the outstanding work being done by the Shanghai University IEEE EMB Students Society, a student club of Shanghai University. It was formed with the help of Club Advisor Prof. Yan Zhuangzhi and consultant Nalin Wanigasekara and is helmed by Jiang Jiehui, student chair. Shanghai’s 10 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

EMBS club considers helpful social activities to be seminal to the bioengineering club experience; as such, they have devised an exemplary program that might provide ideas for other student clubs and chapters.

The team goes to the villages.

The club’s activities were inspired by the viewpoint expressed by C.K. Prahalad and Allen Hammond in their paper “Serving the World’s Poor Profitably” (Harvard Business Review, September 2002). The title of the club’s project is BoP Shanghai (BoP stands for Bottom Of the economic Pyramid). The goal of the project is to improve the living conditions of the masses in rural Shanghai, especially in the areas of medical treatment, healthcare, and public sanitation. Towards that end, the club put together a BoP team composed of seven students from Shanghai University. First, the team decided to personally investigate rural places near Shanghai to see what the towns and villages actually look like and to determine what the most important problems were. After studying the information and statistics of rural Shanghai, Jinshan, Fenxian, Nanhui District, and Chongming County were chosen for further investigation; these are all locations that are relatively backward in economic and social development.

During the trip, the BoP team visited village households and interacted with doctors and villagers to gain an insight into the existing medical and healthcare system. They also delivered questionnaires to doctors and locals to ask them specifics about their problems. Some photos related to these visits are shown in this column. The club worked at these foundational activities for six months. In the months to come, they will focus on product design to help improve the lives of local people. Please feel free to visit http://202.121.192.183/EMBSS. By the time you read this, there will be more information available on the progress of this remarkable initiative! Ethical Issues Conference

In other news, the Third International Conference on Ethical Issues in Biomedical Engineering was held at the Crowne Plaza Hotel in Rochester, New York, from 4–6 June 2005. Following a welcome by conference chair, Dr. Subrata Saha, the participants were also formally welcomed by Dr. Alastair Cormack, dean of the School of Engineering, Alfred University, and by Dr. Charles Phelps, the provost of the University of Rochester. Welcoming notes from Senator Charles Schumer of New York and John R. Ryan, acting chancellor of the State University of New York and

The team visits a house.

JANUARY/FEBRUARY 2006

vice admiral, U.S. Navy (retired), were also distributed to the registrants. In his letter, Senator Schumer said Bringing together biomedical engineers, philosophers, research scientists, students, clinicians, and representatives from the biomedical engineering industries and federal agencies is essential when discussing the ethical issues that surround the development of new implants, devices, and treatments that will improve the quality of life of many. In his welcoming note, Chancellor Ryan may have expressed the feelings of many at this conference through his statement that as society awakens to the great power of biomedical science,

those of us who are exploring, discovering, and creating this new knowledge must exercise extraordinary care to ensure that we have anchored our work in equally as powerful and thoughtful ethical foundations. At this lively two-day conference, biomedical engineers, clinicians, research scientists, biomedical company representatives, attorneys, patient advocates, and graduate and undergraduate students from biomedical engineering programs discussed a large number of diverse topics. The authors came from many different states as well as several foreign countries. Forty formal papers were presented in seven sessions covering a wide range of topics: 1) “Advances in Biomedical Technology: Ethical Concerns”; 2) “Ethical Issues in Biomedical

The club’s students talked with local people to know their living conditions.

The team organized a questionnaire to help gain an understanding of problems related to people with low incomes (BoP).

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Engineering”; 3) “Ethics Education in Biomedical Engineering”; 4) “Biomedical Research: Ethical Issues”; 5) “Animal Experimentation: Ethical Issues”; 6) “Medical Practice Ethical Concerns”; and 7) “Nanotechnology and Ethics.” In addition, there were two panel discussions, one on “Regulation and Marketing of Medical Devices: Ethical Challenges” and the other on “Ethics Education in Bioengineering.” The keynote speaker for the conference was Dr. David Williams, professor of tissue engineering and the director of the U.K. Centre for Tissue Engineering, University of Liverpool. The title of his talk was “Global Dimensions to the Ethical Aspects of Regenerative Medicine.” He discussed the multiple ethical considerations, including the philosophical and spiritual facets of using cells and tissues derived from other humans and animals to treat individuals, the implications of using donated cells without donor consent, some of the logistical aspects of mass production of tissues and organs, problems of intellectual property rights, and problems of health economics associated with high cost treatments. The conference opened with another invited talk, “Who’s Responsible Here? Ethics and Duties in Human Subject Research,” by Dr. Gary Chadwick from the University of Rochester and chair of their institutional review committee. In his talk, Chadwick discussed the role of the investigator when conducting human subjects research and ethical principles described in the “Belmont Report,” i.e., respect for persons, beneficence, and justice that should serve as the basis for defining the roles and duties of the investigators when planning, designing, conducting, and reporting research.

JANUARY/FEBRUARY 2006

11

Student’s Corner Shanghai, China: a city of growth and friendship Lisa Lazareck

hopsticks, sunshine, and academia—a perfect combination for the successful 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) held in Shanghai, China, during the first week of September 2005. I have enlisted some of my new friends from across the globe to share their conference experiences.

C

Damien Coyle, Ph.D. Student, Faculty of Engineering, University of Ulster, Northern Ireland, U.K.

I am a Ph.D. student at the Intelligent System Engineering Laboratory (ISEL). My research interests include nonlinear signal processing, biosignal analysis, image and vision systems, chaos theory, information theory, and neural and adaptive systems. When I returned from San Francisco, California (EMBC 2004), I thought that I would never again experience such an academically and socially rewarding event. I was absolutely wrong! EMBC 2005 in Shanghai was just as gratifying, if not more so, and I have benefited a great deal from the technical and cultural knowledge gained at the conference. I have many exceptional memories of my time in China. The EMBC incorporates a diverse range of themes related to biomedical engineering. This year, a preconference symposium titled “Frontiers of Neural Engineering” was held in Beijing. Distinguished speakers delivered lectures in three thematic areas: neural micro-/nanotechnology, brain computer interface (BCI), and neural prosthesis. My main area of research is BCI tech12 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

nology, and the symposium provided the ultimate opportunity to network with some of the most distinguished researchers in the field. Other relevant themes present at the Shanghai conference included statistical/time-frequency analysis of nonstationary signals as well as nonlinear methods. EMBC 2005 enhanced my knowledge of state-ofthe-art neural engineering and biomedical engineering in general. I presented my own research paper with the objective of receiving feedback from attendees. The many interesting discussions I engaged in have already helped me to develop my research further and improve my research profile (http://isel. infm.ulst.ac.uk/coyle.html). Rola Gu Yuhong, Undergraduate Student, Zhejiang University; Research Assistant, Joint Center for Biomedical Engineering, Chinese University of Hong Kong (JCBME), China

Did we meet at the IEEE EMBC 2005? If you saw a tall girl in a yellow volunteer T-shirt and with a cool interphone, well, that was me! As an undergraduate student, I feel that participating in EMBC 2005—a first-class academic conference—has helped me gain a better understanding of the different areas of biomedical engineering (BME) and set clearer personal research goals. My current research interests include wearable medical devices for real-time monitoring, biosignal processing, and cardiovascular system modeling. My interest in BME stems from my experience as a research assistant at JCBME (signal processing) and as a competitor (national second prize) at the Olympic Biology Competition.

What made my experience at EMBC 2005 so special was the great honor of being an official conference volunteer within the core volunteer group. I was able to experience the conference preparation firsthand from start to finish, and I encountered the many difficulties related to running an international event for 2,000 attendees. (I specifically worked on the student reception and the lunch discount for attendees.) Overall, I truly believe that we succeeded in our efforts, and even showed the charm of China to the world! Due to my volunteer position, I was unable to attend many of the PowerPoint sessions. However, at the “Lunch with Leaders” events and the student reception, I was able to speak with leading professors in my field of study. These professionals helped me learn about new research trends at several universities and gave me ideas about solving my own research problems. Also, speaking with other senior students was helpful; many had overcome obstacles that I am currently facing. Finally, I would like to express my deep appreciation to the EMBC 2005 chair, Prof. Y.T. Zhang, and the volunteer group for their support and teamwork. I look forward to the success of EMBC 2006. Pawel Herman, Ph.D. Student, School of Computing and Intelligent Systems, University of Ulster, Northern Ireland, U.K.

I am a third-year Ph.D. student at the University of Ulster. Broadly speaking, my research interest is in the application of intelligent computational systems to a variety of dynamic brain processes reflected in the electroencephalogram JANUARY/FEBRUARY 2006

(EEG). Two main application areas are noninvasive BCI and the seizure prediction problem. This was my first IEEE EMBS conference. I will remember it as an exciting and, above all, valuable experience. The poster session I engaged in gave me a good opportunity to interact with academic and professional delegates with similar as well as diverse research interests. In addition, the “Lunch with Leaders” was another occasion for a casual and inspirational conversation. Naturally, participation in the student activities brings about many fond memories. I very much appreciated these experiences that helped boost scientific motivation and involvement in the Society’s activities. The specificity and uniqueness of the conference venue was definitely a significant and vibrant asset to this outstanding event. Therefore, I would also like to express how grateful I am for the organizers’ effort to give us a touch of local culture and traditions. As it was my second visit to Asia, I must admit that my fascination with and interest in this part of the globe have grown stronger. All in all, the conference surpassed my highest expectations. It was an excellent scientific, social, and cultural experience. I hope to take part in the next one. See you there! Jiang Jiehui, Master’s Student, Department of Biomedical Engineering, Shanghai University, China

Best greetings from Shanghai! I’m Jiang Jiehui, chair of the Shanghai University EMBS Student Club and master’s student in the Department of Biomedical

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Engineering, Shanghai University. I was fortunate enough to be a local volunteer at EMBC 2005. The conference was so exciting. I will never forget the experience! As a volunteer, I had a lot of work to do in the daytime, such as preparing PowerPoint sessions and the student reception. However, I did have the opportunity to join many student events, such as at the “Lunch with Leaders,” where I spoke with famous professors and researchers about telemedicine and medical imaging and their application within China. For example, I am currently studying about ways to improve healthcare in rural communities through IT technology. As an engineering student, I would like to incorporate advanced technology into the everyday lives of the Chinese people. Overall, EMBC was a wonderful experience! I will especially remember the many new friends I met from all over the world. We talked with each other about everything; if you’d like to keep in touch, feel free to contact me: [email protected]. Also, I would appreciate any ideas you have about my research. Andrew Niemiec, Master’s Student, Monash University, Clayton Campus, Australia

I’ve recently completed research for my master’s degree in biomedical engineering with a focus on EEG signal processing and, in particular, the search for EEG abnormalities in subjects prone to depression. I’ve taken an intermission in my studies to work in the biomedical industry while contemplating the age-old decision of whether to go for a Ph.D.

My Shanghai adventure started early Tuesday morning. I was the only student from my research group attending, and I had never presented at a conference. I’d also never been to Asia, so I was not sure what to expect from the place, the people, and the actual conference. I’m glad to say they were all fantastic. By sheer chance, on the plane I sat next to a student from Sydney University who was also going to the conference. We had numerous escapades around Shanghai, including taking part in a 6 a.m. Tai Chi session at the People’s Square, seeing the amazing night cityscape along the Huangpu River, surviving the chaotic traffic, and honing our haggling skills at the infamous Xiang Yang Market. I met plenty of great people from around the world at the student reception, and I still keep in contact with many of them. The conference itself was extremely useful. I constantly moved from room to room to attend relevant presentations, and being around so many other biomedical engineers gave me many useful ideas, both for my research and industrial projects. Furthermore, the “Lunch with Leaders” event generated in-depth conversations and valuable future contacts. This was my first EMBS conference, but it certainly won’t be my last. Missed the Shanghai conference? The 28th Annual IEEE EMBS Conference will be held in New York, New York. See the conference Web site for details: http://embc2006.njit.edu. Finally, please note that your new student representative would love to hear from you—submit your questions, comments, or contributions to this column! Please see the student Web site for the new student representative contact details: http:// ewh.ieee.org/soc/embs/student/.

JANUARY/FEBRUARY 2006

13

Book Reviews

Paul H. King

Engineering for Patient Safety: Issues in Minimally Invasive Procedures

Edited by Jenny Dankelman, Cornelius Grimbergen, and Henk Stassen, Lawrence Erlbaum Associates, 2005. ISBN: 0-8058-4905-X, xx + 300 pages, US$79.95. This text is the outgrowth of a program on minimally invasive surgery and interventional techniques, funded at Delft University by the board of the university. The text is a product of the collaboration of several clinicians, engineering faculty, and graduate students whose interests and studies coincided with the work reported here. The text is also the first of a patient safety series to be published by the Erlbaum Press. The series editor is Marilyn Susan Bogner. The series is off to a great start! The text consists of 12 chapters, each of which will be briefly described. Chapter 1, “Introduction to Minimally Invasive Surgery,” covers a brief history of minimally invasive surgery and contrasts the benefits and disadvantages of this technique with conventional surgery. An introduction to clinically driven research sets the stage for later analysis of various studies of techniques and devices. Chapter 2, “Evaluation of the Surgical Process,” discusses various methods and introduces the reader to different metrics used in quality analysis (postoperative measures, time constraints, timeaction analysis, flowcharting, and efficiency). This is a very good chapter to use for design or instrumentation class examples. Chapter 3, “Observation and Manipulation in Laparoscopic Surgery,” discusses the many problems that are apparent to the surgeon using these techniques (as opposed to standard surgical techniques). A few of the prototype devices developed at Delft to solve 14 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

some of the indicated problems analyzed are discussed here (a pattern that occurs in may of the following chapters). Chapter 4, “Eye-Hand Coordination in Laparoscopy,” covers several methods used to make the surgical procedure more intuitive. The DaVinci surgical system is used as a very good example of a reasonable but expensive solution. “Force Transmission of Laparoscopic Graspers,” chapter 5, discusses the need to provide force feedback to the surgeon. One prototype to provide a useful feedback signal is discussed and evaluated. Chapter 6, “Safe Manipulation of Bowel Tissue,” discusses the design and testing of various grippers to be used for safe retraction of bowel tissues. Chapter 7 discusses “Design and Evaluation of Endoscope Positioners” and discusses measurements in the clinical environment. Chapter 8 discusses “Design of Arthroscopic Instruments,” reviewing the need for instruments that can flex to reach areas not easily reached with current instruments. Chapter 9, “Virtual Colonoscopy,” reviews current techniques using barium (with X rays) or optical methods and contrasts the results with those that can be obtained with three-dimensional computed tomographic (fly through) imaging of the bowel. Missing is a discussion of the newer techniques using a pill camera that can be swallowed. Chapter 10 discusses “Steering of Catheters,” making the point that using X rays to locate the catheter tip during a procedure results in unnecessary radiation doses to both patient and provider. The potential use of magnetic tracking of the catheter tip is discussed. Chapter 11, “Sensors for Catheters and Guidewires,” is a good general discussion of the types of sensors available and several of the design constraints that must be satisfied.

Chapter 12, “The Future of Minimally Invasive Surgery and Training,” briefly recaps the design problems listed in earlier chapters and some of the research that needs to be done to solve them. It also initiates a discussion of the need for better training facilities. This text is very well written and superbly referenced. Therefore, it is a good text for anyone facing problems related to the development of equipment for minimally invasive surgery. It has some excellent sections that can be used for example discussions in a design or instrumentation class. It is an excellent reference text for investigators in this area. —Paul H. King Vanderbilt University Introduction to Biomedical Engineering (2nd Edition)

John Enderle, Susan Blanchard, and Joseph Bronzino, Elsevier Academic Press, 2005. ISBN: 0-12-238662-0, 1,140 pages, US$90.00. Biomedical engineering involves applying the concepts, knowledge, and approaches of virtually all engineering disciplines (e.g., electrical, mechanical, and chemical engineering) to solve specific healthcare-related problems; the opportunities for interaction between engineers and healthcare professionals are many and varied. The medically relevant design problems can range from very complex large-scale constructs, such as the design and implementation of automated clinical laboratories, multiphasic screening facilities, and hospital information systems, to the creation of relatively small and simple devices, such as recording electrodes and transducers that may be used to monitor the activity of specific physiological processes in either a research or clinical setting. They encompass the many JANUARY/FEBRUARY 2006

Book Reviews (continued)

complexities of remote monitoring and telemetry and include the requirements of emergency vehicles, operating rooms, and intensive care units. The world of biomedical engineering includes biomechanics, prosthetic devices and artificial organs, medical imaging, biomaterials, biotechnology, tissue engineering, neural engineering, biomedical instrumentation, bionano-technology, physiological modeling, rehabilitation engineering, medical and bioinformatics, clinical engineering, biosensors, and medical and biological analysis. The purpose of the second edition of this volume in the Academic Press Series in Biomedical Engineering remains the same as the first edition: to serve as an introduction to an overview of the field of biomedical engineering. The textbook provides a historical perspective of the major developments in specific biomedical domains as well as the fundamental principles underlying biomedical engineering design, analysis, and modeling procedures in those domains. The editors and authors provide the most thorough review of concepts from biomaterials and tissue engineering to bioinstrumentation and medical imaging. The material in this textbook is presented in the following 17 chapters: “Biomedical Engineering: A Historical Perspective”; “Moral and Ethical Issues”; “Anatomy and Physiology”; “Biomechanics”; “Rehabilitation Engineering and Assistive Technology”; “Biomaterials”; “Tissue Engineering”; “Bioinstrumentation”; “Biomedical Sensors”; “Biosignal Processing”; “Bioelectric Phenomena”; “Physiological Modeling”; “Genomics and Bioinformatics”; “Computational Cell Biology and Complexity”; “Radiation Imaging”; “Medical Imaging”; and “Biomedical Optics and Lasers.” —Armando Perraioli Italy Usability and Internationalization of Information Technology

Edited by Nuray Akin, Lawrence Erlbaum Associates, 2004. ISBN: 0805844791, 366 pages, US$40.00. 16 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Our society is becoming increasingly reliant on information technology (IT) because of the simple truth that it has introduced freedom, worldwide connectedness, and loss of remoteness. From finding a subway map of Prague for vacation, a romantic restaurant in Paris, or a nightclub in Las Vegas to locating the best buys for books, movie tickets, mortgages, or cowboy boots, many of us find that the use of IT is a supremely valuable enhancement to the quality of our lives. Ensuring that IT is effectively utilized across culturo-continental boundaries is the prime focus of Akin’s Usability and Internationalization of Information Technology. Developed in the spirit of Human Factors and Ergonomics (series editor: Gavriel Salvendy), this volume is part of a larger series dedicated to topics on interacting with our “information society.” Our initial reaction was how such an incredibly broad topic could be dutifully covered in a medium-sized paperback (366 pages cover to cover). The book’s three sections comprise 11 chapters authored by foremost practitioners from the United States, Europe, and Asia. The focus ranges across guidelines and practices for internationalization and localization, cultural consideration and guidelines in the design of IT, usability evaluation methodology and cost-benefit analysis for cross-cultural design and concludes with a few case studies. While the series and text editors do not claim to have included all the answers, they, in collaboration with the contributing authors, have done an impressively thorough job in either addressing the most pressing internationalization questions or offering resources where the unanswered questions may be further researched. Indeed, one of the greatest strengths of this book is the breadth of what is covered, and by selecting some of the leaders in the field as authors, the editors have provided the necessary authority to the writing, even if styles change from chapter to chapter.

Certainly readable, this volume may best serve as an invaluable reference tool to any who wish to gracefully convey topics of interest beyond the borders of their home countries and cultures. As the cover image (an inverted globe) suggests, the challenge of internationalized commerce and the ever-increasing diversity of the workplace is turning cultural biases and misconceptions on their ear. It is imperative for aspiring and established enterprises in need of an international portal to take heed of the numerous communication issues addressed in this volume. Of course, there are some inconsistencies and some omissions. Why, for example, is the important issue of health information transmission in our modern biomedical era never mentioned? International health IT is significant for tracking public health threats/problems, enhancing the quality of medical care, and even reducing a substantial amount of our healthcare expenditures. In addition to the aforementioned major problem, the text is rife with typeface, spelling, and grammatical bloopers that are difficult to ignore. Additionally, we have a gripe about the quality of the figures. We increasingly depend on visual images in order to learn; yet the book is packed with rather dull black-andwhite illustrations and some relataively substandard hand drawings. While certainly not the final stop for all aspects of information transfer, this quick and easy romp in the field of human factors and social interaction will guide the reader through varied topics such as interface design and cultural issues, financial and legalistic concerns, and justification and validation. As an overall package, this volume is an opportune rendition to the engineering and IT community by the contributors. The most important question, however, is whether we would buy this book. The answer is yes! —Diana Anderson and Guruprasad Madhavan State University of New York, Binghamton JANUARY/FEBRUARY 2006

BME Company Profiles a microsystem design house Semahat S. Demir

n this column I am featuring European Technology for Business Ltd. (ETB) by presenting an electronic interview I had recently with Dr. Diana Hodgins, managing director.

I

S.S. Demir: Would you please introduce ETB to us and also the engineering and biosciences that your company is involved in? Please give us a short history of the company and also whom your company employs and what kind of engineers. D. Hodgins: ETB is a privately owned system design house based in the United Kingdom and established in 1997. We specialize in microsystems for health, physical motion sen- Diana Hodgins, sors, and piezo-elec- ETB’s managing tric-based sensors director. and actuators. The company started out with mechanical design expertise, from applied research to product design. This was sufficient for the first few years when we were developing our sensors, the solid state gyro and the three-axis accelerometer. As the company started to explore medical applications for microsystems in 2002, we realized that we needed to extend our skill set to include electronic design. This enabled us to develop complete medical systems, integrating sensors and actuators, and offer this expertise to other companies. ETB built up its reputation as a leading design company in microsystems, and customers were asking for complete system solutions. This meant the products designed by ETB needed to be manufactured, assembled, and tested. So, in 2004, ETB extended the team to include production and test IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

engineers. Finally, in 2005, ETB introduced software and systems expertise, and the team now comprises mechanical, electrical, production test, and system engineers. The decision was taken early on in ETB’s growth that only highly skilled and motivated individuals would be employed. The company adopts an extremely flat management structure; therefore, all personnel must be able to work as part of the team. This applies equally to projects where ETB is one of the partners; all our personnel must be able to integrate into the overall project team. ETB is not a listed company and has not gone for market capitalization. S.S. Demir: Where are your company’s efforts concentrated in bioengineering and biosciences? D. Hodgins: ETB has been involved in a number of successfully completed medical related projects. We are currently running one major European Commission FP6 (6th Framework Programme) project, Healthy Aims, and we are a partner in another U.K. collaborative project, the BioMed HTC. In both of these projects, ETB offers our system design skills and our knowledge of manufacture and testing of the final systems. In addition, ETB often acts as the coordinator of these multidisciplined projects. In addition to these complex medical projects, ETB has run a few projects that focus on the development of their gyro and accelerometer for health and other applications. Completed projects include one U.K. and one EC project to develop a dropped foot implant. These two projects have resulted in the world’s first commercially available device. ETB has acted as the design company, working with research groups in Holland and

Ireland, clinical teams in Holland and the U.K., and the end-user manufacturer, an SME (small- to medium-sized enterprise) in the United Kingdom. A U.K. project to develop a range of innovative products for people suffering from intractable urinary incontinence. This three-year project involved academic groups, a clinical team, and a number of SMEs. ETB was the project coordinator and also developed with the end-user manufacturer and clinical partners an “automatic valve.” A number of new products and training aids were successfully launched on the market following this project. In an EC project, ETB was to develop a planar piezo-electric based three-axis accelerometer for a range of applications including human body motion. This resulted in our device, which is ideally suited to a range of medical applications. There are also three U.K. DTI-funded projects to develop a novel solid-state piezo-electric gyro. This is ETB’s own product which has been designed for a range of applications including human body motion. First prototype devices are just becoming available. The current projects are: ➤ Healthy Aims. This is a €26 million EC project funded under the FP6 IST Microsystems program. It has 26 partners from nine EC countries, and the goals are to develop a range of implantable devices and diagnostic products for medical applications. The products being developed in the Healthy Aims project include ➤ cochlear implant ➤ retina implant ➤ functional electrical stimulation for the upper limb ➤ glaucoma sensor ➤ intracranial pressure sensor ➤ sphincter sensor JANUARY/FEBRUARY 2006

19

BME Company Profiles (continued)

➤ inertial measuring unit for human

body motion ➤ implantable battery (rechargeable, ten-year life) ➤ communication system from implants and diagnostic tool from in or on the body to up to three miles away. In addition, a number of underlying technologies are being developed, which include ➤ biomaterials ➤ microelectrodes ➤ microaassembly and micropackaging methods. After 21 months into the four-year project, a number of demonstrators has already resulted. The project includes clinical trials that will be undertaken by the clinical partners once ethics approvals have been obtained. The other medical project that ETB is involved in is the BioMed HTC. This is a U.K. team that is focused on encouraging innovation in intractable urinary incontinence. This builds upon the earlier work in the NuTap project and includes clinical experts together with end-user manufacturers and ETB as a system design house.

involved in the medical sector. It was formed in 2002 by me and now is recognized as one of the best networks for bringing research groups from a range of disciplines in contact with medical device manufacturers and clinical teams. The Healthy Aims project was one successful example of a project team formed from the network. S.S. Demir: What are your products? D. Hodgins: ETB is now focusing on their three-axis accelerometer unit and their gyro. These will be used for standalone applications and are also being designed into our own wireless sensor units for monitoring human body motion. The specific human motion applications that ETB is currently considering address conditions that affect the elderly and infirm. In addition to the sensor systems that are under development, ETB is also pushing their core sensor technology, the gyro and accelerometer. Three-Axis Accelerometer

S.S. Demir: Would you please comment on your company’s partnerships, collaborations, and alliances? D. Hodgins: ETB has a whole range of partners from their work in the medical sector, acting as a leading microsystems design house. For example, we have 24 direct partners from the Healthy Aims team and 14 from the BioMed HTC. Additionally, ETB works with other companies from the projects mentioned. These partners include Vodafone, Finetech Medical, C-MAC, Mediplus, Zarlink, Cranfield University, North Bristol NHS, Salford, and Salisbury Hospital. In addition to these specific partnerships ETB also runs two networks and is a director on a third. The NEXUS Medical Devices User Supplier Club is a European Unionbased network for organizations 20 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Fig. 1. Accelerometer unit.

The target markets for this device are where all three axes of acceleration must be measured simultaneously. The design developed by ETB is a Silicon die, with three separate proof masses for measuring x, y, and z accelerations, with thin film piezo-electric material used to measure the accelerations. The markets ETB is looking into include vibration, tracking, and defense-guidance applications where precision is required.

The unique selling features of the three-axis accelerometer are ➤ small size and weight compared to precision sensors (11 × 5 × 1 mm for the die alone). ➤ low-power requirement and will run from a 3- or 5-V battery source (typically 250 µW). ➤ low cost compared to precision sensors. ➤ low cross axis sensitivity, due to the fact that all three axes of acceleration are measured using a device fabricated from a single silicon wafer. ➤ low noise and, hence, high sensitivity comparable to precision instrumentation (0.2 mg at 50 Hz bandwidth). ➤ high bandwidth if required, up to 2 kHz ➤ can run remotely and, hence, can work in hazardous environments. Solid-State Gyro

The markets include hazardous environments, measurement, control, and guidance systems, including transport, human motion, down hole drilling, space, and defense applications. The design is particularly suitable for those applications where a small number of gyros to suit a particular specification are required. ETB owns the intellectual property (IP) and is able to produce a design to suit specific applications. For example, the rate of rotation to be measured, the g-force that it must survive, and the temperature range it will be exposed to can all be taken into account when producing a specific design. The manufacturing methods are such that volumes from ten to ten million per annum can be produced.

Fig. 2. Gyro unit.

JANUARY/FEBRUARY 2006

The time from concept to working prototypes is also short, typically six months, due to the design and manufacturing techniques adopted. This cannot easily be achieved with current silicon designs. The device is fabricated from piezo-electric material. The unique selling features of the ETB solid-state gyro are ➤ simple construction, hence, easy to manufacture ➤ able to be produced in low or high numbers ➤ can be designed for specific rate and scale factor ➤ can be designed for different environmental conditions e.g., high temperature, high g-force applications ➤ total package size comparable to all

➤ ➤ ➤ ➤

microfabricated designs (typically Ø15 mm, 3 mm long, excluding electronics) high sensitivity due to low noise high bandwidth insensitive to g-forces insensitive to vibration up to 100 kHz

S.S. Demir: Can you give us an overview of the new areas that your company will expand into in the near future? D. Hodgins: ETB is committed to developing new wireless sensor systems for human body motion applications. These will be targeted towards medical applications and health and well-being applications such as fitness and activity. These are emerging mar-

kets where products can now only be realized with the development of microsystem technology combined with wireless communications, including mobile networks. S.S. Demir: Lots of the readers will be interested in hearing about the career and/or job opportunities at your company. Would you please summarize the career and/or job opportunities? D. Hodgins: ETB is currently seeking excellent system and design engineers. In addition, ETB is looking for a strong marketing person who can help the company sell its systems into emerging markets, focusing on human body motion applications. Our Web site is http://www.etb.co.uk.

A Look at ... (continued from page 8) in-depth knowledge of object properties, the physics of the sensing device, and the interactions between them, has been emphasized. It has been pointed out that the standard assumptions in generic motion estimation methods are not verified due to the limitations in data acquisition, the complexity of tissues and organs, and the multiple fac-

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

tors that modify the appearances of the objects of interest. Most of the classical paradigms should benefit from prior modeling to attain a better understanding of physiological motion and to derive innovative ways for expanding its place in the diagnostic process. The third part in this series of articles will show that beyond medical imaging, the

same issues in biological imaging must be addressed. References [1] J.L. Coatrieux, “Shape and function from motion in medical imaging: Part 1,” IEEE Eng. Med. Biol. Mag., vol. 24, no. 3, pp. 29–31, 2005. [2] J.L. Coatrieux “Integrative science: Biosignal processing and modeling,”IEEE Eng. Med. Biol. Mag., vol. 23, no. 3, pp. 9–12, 2004.

JANUARY/FEBRUARY 2006

21

Around the World Vo Van Toi

the first international conference on the development of BME in Vietnam

ietnam is an economically and socially dynamic country, and interest in biomedical engineering (BME) activities has grown considerably in this nation in recent years. The traditions, environment, and conditions within the country offer unique research opportunities, and the Vietnamese government and university policy makers are well aware of the importance of this field. To address urgent educational and research needs in biomedical engineering within this country, a domestic and international exchange of experiences, support, and collaborations among researchers and educators in this field is necessary. In January 2004, under the sponsorship of the U.S. National Science Foundation (NSF), a delegation consisting of biomedical engineering professors from different universities in the United States visited several universities and research institutions in Vietnam to assess the development of the field of biomedical engineering (IEEE Engineering in Medicine and Biology Magazine, vol. 23, no. 2, March/April 2004, page 17; IEEE Engineering, Medicine and Biology Magazine, vol. 24, no. 3, May/June 2005, pages 7–11). The delegation enthusiastically recommended that the United States and inter-

V

Prof. Vo Van Toi of the Biomedical Engineering Department of Tufts University, organizer and general chair of the conference.

22 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

national scientific communities actively develop collaborations with Vietnam. It also developed a five-year plan for the U.S./Vietnam collaboration (for more information, see the Web site at http://www.cs.tufts.edu/~vanvo/VN/ DevelopmentBMEVN.htm). This conference was one of the first steps in the framework of this recommendation. The First International Conference on the Development of Biomedical Engineering in Vietnam was organized at the Ho Chi Minh University of Technology (HCMUT) from 27–29 July 2005 and was sponsored by the NSF, Vietnam Education Foundation, Vietnam National University at Ho Chi Minh City, and the National Center of Laser Technology. The organization committee included: ➤ General Chair: Prof. Vo Van Toi, BME Department at Tufts University, Medford, Massachusetts ➤ Cochair: Prof. Nguyen Thanh Son, vice president of HCMUT ➤ General Secretary: Prof. Huynh Quang Linh, chair of the Biomedical Engineering Department of HCMUT, general secretary of the conference. The theme of the conference was the development of research, education, and entrepreneurship in biomedical engineering. The goal of this conference was fourfold: ➤ to give Vietnamese educators, students, researchers, administrators, and policy makers the opportunity to learn about the related works and models in the United States and elsewhere and to present their accomplishments ➤ to give international scholars the opportunity to learn about biomedical engineering developments in Vietnam for future collaborations ➤ to develop the relationship between international and local industry

➤ to motivate further the development

of biomedical engineering in Vietnam. The conference attracted more than 200 participants. Participants from the United States included Tom DeMarse (professor, University of Florida), Semahat Demir (program director, NSF, and professor, University of Memphis), An Dinh, (graduate student, Tufts University), William Ditto (chair of BME, University of Florida), Tim Duong (professor, Emory University), Sergio Fantini (interim associate dean of College of Engineering, Biomedical Engineering Department, Tufts University), Don Giddens (dean, College of Engineering, Georgia Institute of Technology; Lawrence L. Gellerstedt, Jr. Chair in Bioengineering; Georgia Research Alliance Eminent Scholar), David Kaplan (chair of BME, Tufts University), Mailen Kootsey (professor, Loma Linda University), Matthew Loeb (director, the IEEE), Alan Macy (BIOPAC Systems, Inc.), Renata Mattson (TOI, Inc.), Trang Nguyen (graduate student, Tufts University), Anh Nguyen (graduate student, Tufts University), Thang Nguyen (graduate student, Stanford University), Cecil Perrault (graduate student, University of Florida), Sheryl Prucka (Prucka Engineering, Inc.), Matthew Prucka (Prucka Engineering, Inc.), Jianguo Sun (Vitalog Inc.), Roger Tran Son Tay (professor, University of Florida), Van Toi Vo (professor, Tufts University), Bruce Wheeler (interim chair of BME, University of Illinois, Urbana Champaign), Leslie Winston, and Arthur Winston (past president of the IEEE, Tufts University) On the first day of the conference, the keynote speaker, Prof. Don Giddens (dean of the College of Engineering, Georgia Institute of Technology) retraced the development of biomedical JANUARY/FEBRUARY 2006

engineering in the United States, emphasized current and future opportunities, and suggested steps toward developing biomedical engineering in Vietnam. Other speakers elaborated on different educational, research, and entrepreneurial models practiced in the United States, including the conditions of their establishment, curricula, infrastructure, difficulties encountered, advantages, and disadvantages. Additional topics included the relationship between universities, hospitals, and industry; technology transfer mechanisms, including small business innovative research (SBIR) and small business technology transfer (STTR); governmental and private research funding institutions; venture capitalism; and professional societies such as the IEEE Engineering in Medicine and Biology Society (EMBS) and AIMBE. Over the course of the second day, speakers presented the progress and scientific achievements in different fields of biomedical engineering, including medical imaging, regenerative medicine, medical instrumentations, biomechanics, and complementary and alternative medicine. Overall, there were more than 60 oral and poster presentations by authors in both academia and the private sector from Vietnam, the United States, France, the United Kingdom, Switzerland, Belgium, Ukraine, Bulgaria, Australia, China, and Thailand. The third day brought a roundtable discussion focused on the effective ways to enhance the activities of the biomedical engineering field in Vietnam. It gave the nation of Vietnam the input needed to establish its own roadmap detailing the fields that must be developed, the steps that must be taken, how this must be done, what outcomes can be expected, and how effects should be measured. From the discussion, the Consortium of VietnamInternational Universities was created. It will function as an advisory board to the Vietnamese universities and a magnet to attract Vietnamese from overseas. The consortium will assist the Vietnamese in the development of bioIEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Representatives of Vietnam and international delegations during the conference.

medical engineering. The Vietnamese Association of Biomedical Engineering was also created. It consists of Vietnamese researchers, educators, individuals in the private sector, and students who are interested in biomedical engineering activities. This newly

created association will organize the Second International Conference on the Development of Biomedical Engineering in Vietnam in Hanoi in August 2006. For more information, please visit the postconference Web site at http://www. cs.tufts.edu/~vanvo/VN/BME.

JANUARY/FEBRUARY 2006

23

Senior Design national student design competitions for biomedical engineers Jay R. Goldberg

ational design competitions for biomedical engineering students are beneficial to students, faculty, academic institutions, and the medical device industry. They allow students and faculty to showcase the results of senior design projects, academic institutions to publicize their senior design programs, and medical device companies to become familiar with the quality and content of senior design projects. The goals of these competitions may include: ➤ providing students with incentives and opportunities to learn about engineering design and teamwork ➤ encouraging the creation of design innovations and intellectual property of interest to the medical device industry and investment community that could be the impetus for the formation of new business ventures and start-up companies ➤ providing a way of bringing the academic and industrial biomedical engineering communities together, enabling the sharing of best design education practices and the dissemination of effective learning tools. There are several national design competitions of interest to the biomedical engineering community. Some emphasize entrepreneurship and the potential for commercialization and provide awards in the form of cash to be used for further development of the design concept. Others focus more on technical aspects and how well the design meets the needs of a specific patient population. Submissions to these competitions are not limited to senior design projects. However, many senior design projects contain the components required by these competitions and are of sufficient quality to be highly competitive with other entries. Teams are not required to

N

24 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

consist of only biomedical engineering students. Entries from multidisciplinary teams are strongly encouraged. National Student Design Competition

Sponsor: Rehabilitation Engineering Research Center on Accessible Medical Instrumentation (RERC on AMI) This competition provides funds of up to US$2,000 per team for prototype development costs, awards cash prizes of up to US$1,000, and will pay for travel to present an accepted paper at a major conference. Three target design areas, each having to do with accessible medical instrumentation, are defined at the beginning of each academic year. Hypothetical clients with various disabilities are described, and project teams are required to develop designs in the target design areas that will be easily used by specific clients. The design areas for the 2005–2006 competition are ➤ Accessible Blood Glucose Monitor Interface ➤ Accessible Medication Dispensing Device ➤ Patient Positioning Aid. Teams are required to create a Web site to be used to evaluate their projects. The Web sites include a final report, detailed photos and video of the prototype in use to demonstrate function, engineering analyses, an accounting of all project expenses (not to exceed US$2,000), estimated production costs, and a discussion of how the design addresses the needs of the specific clients. More information is available at http://www.rerc-ami.org. BME-IDEA (Biomedical Engineering Innovation, Design, and Entrepreneurship Award)

Sponsors: National Collegiate Inventors and Innovators Alliance

(NCIIA), National Science Foundation, Canon Communications, LLC, Industrial Design Society of America (IDSA), Biomedical Engineering Society (BMES), and the Council of Chairs of Bioengineering and Biomedical Engineering Programs This competition seeks student design projects that focus on healthrelated technology and meet a relevant clinical need. Teams must submit a project description including documentation of the final design, proof of functionality, assessment of patentability (patent search and search for prior art), anticipated regulatory pathway, estimated production costs, and a business plan with a market analysis and details regarding strategy for commercialization. Judging criteria include technical, economic, and regulatory feasibility, contribution to healthcare and quality of life, technical innovation, and potential for commercialization. Awards are presented at the Medical Design Excellence Awards ceremony during the Medical Design and Manufacturing East Convention in the presence of many of the top medical device designers and manufacturers. This competition provides students with the opportunity to win substantial product development funding, gain exposure to the medical device industry, and develop industry contacts. More information is available at http://www.nciia.org. EMBS Undergraduate Student Design Competition

Sponsor: IEEE Engineering in Medicine and Biology Society Entry into this competition requires students to design and construct a device that solves a problem in medicine or biology. The device must not be commercially available. However, significant design modifications to existing products are

0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

acceptable. Product designs may consist of hardware, software, or a combination. Submissions include a project summary, background of the problem to be solved, purpose and scope of the project, a technical description of the design, supporting technical analyses, conclusions, and design specifications. Verification of project success must be supported with videotape and/or photographic evidence. Awards of up to US$300 and a plaque are presented at the awards ceremony during the IEEE Engineering in Medicine and Biology Society (EMBS) Annual Conference. Winning teams are provided with travel stipends and conference registration. Winning entries are published in IEEE Engineering in Medicine and Biology Magazine (winners for 2004 are featured in this issue). More information is available at http://www.ieee.org. The Collegiate Inventors Competition

Sponsors: National Inventors Hall of Fame, United States Patent and Trademark Office The goal of this international competition is to create excitement and interest in technology and economic leadership. It encourages college students to combine knowledge of science, engineering, and mathematics with technical skills to develop creative inventions. The competition recognizes the potential for the student/mentor relationship to produce patentable inventions. Reduced-to-practice ideas or workable models developed by student teams may be entered in this competition. The invention must be original and reproducible. Entries must include a summary of current literature, results of a patent search, test data and analyses to prove functional-

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

ity, and a discussion of the environmental, societal, and economic benefits of the invention. Judging is based on originality, inventiveness, and workability of the invention, quality of supporting documentation, and the invention’s potential value to society. The award for the best undergraduate student or student team is US$15,000 for the team and US$5,000 for the advisor. More information is available at http://www. invent.org/collegiate. Some Winning Ideas

The BME-IDEA competition recently completed its first year. The first place winner (US$10,000) was “A Novel Treatment for Cerebral Aneurysms,” submitted by a team from Stanford University. The second place winner (US$2,500) was “Bioimpedance Probe to Detect Preterm Labor,” submitted by a team from Johns Hopkins University. The third place winner (US$1,000) was “The Halo-Pack: A Low Profile Cervical Spine Orthosis,” submitted by a team from Washington University. This year, the RERC on Accessible Medical Instrumentation (AMI) competition completed its second year. Teams submitted designs for accessible ergometers, accessible syringe dosing devices, and accessible weight scales. The first place winning team was from the University of Rochester (Accessible Ergometer). Second place winners (each the top in their design areas) were teams from Marquette University (Accessible Syringe Dosing) and Texas A&M (Accessible Weight Scale). Third place winners (second in their design areas) were teams from University of Wisconsin (Accessible Ergometer), Catholic University (Accessible Weight Scale), and University of Connecticut (Accessible Syringe Dosing). In summary, there are several design competitions that welcome entries from biomedical engineering students and

multidisciplinary project teams. Several have been established to encourage students to use their knowledge and skills to solve medical or other related problems. Some encourage design projects in specific areas of engineering, while others focus more on entrepreneurship, innovation, and commercialization. As engineering educators, we should encourage our senior design students to enter these competitions. Perhaps the next major innovation in medical technology will come from a project that was a winner of one of these competitions. National Scholar Award for Workplace Innovation and Design

Sponsor: NISH The purpose of this competition is to encourage students to design creative technical solutions that overcome barriers that prevent people with disabilities from entering or advancing in the workplace. Individual students or student teams are encouraged to submit workplace technology designs related to technology for special populations, computer access and use, environmental accommodations, functional control and assistance, transportation/mobility, service delivery, and augmentative and alternative communication. Awards of up to US$10,000 are provided to the best designs that will help create employment opportunities for people with severe disabilities. A matching gift is given to the sponsoring department. Winners are expected to attend the NISH National Training & Achievement Conference and to present a poster exhibit and an oral platform presentation during a conference session. Judging is based on relevance of the design to NISH goals and the quality of the abstract, background discussion, problem statement, appropriate design and evaluation methods, and results and discussion. More information is available at http://www.nish.org.

JANUARY/FEBRUARY 2006

25

Point of View a proposal for ABET criterion 9 Gail D. Baura

s one of the Accreditation Board for Engineering Technology (ABET) evaluators for biomedical engineering, I recently returned from my annual visit to an assigned program. Typically, all the engineering programs within an engineering school are reevaluated together every six years. Each program is assessed by one evaluator. Our evaluations are based on several general criteria and on an eighth criterion specific to the type of engineering. For example, per Criterion 2, each program chooses its own program educational objectives, which are based on the mission of the institution. These objectives are then periodically assessed. The subsections of Criterion 2 are listed in Figure 1 [1]. Per Criterion 8, each biomedical engineering program is required to teach differential equations, statistics, and the capability of solving problems at the interface of engineering and biology. Lately, I’ve been wondering if we need to add a new criterion. With evolution on the defense in public school system science classes in both Pennsylvania and Kansas, ABET Criterion 2 and Criterion 8 seem to be on track for a head-on collision. Some engineering programs exist within universities possessing mission statements that specifically mention the Bible or the Scripture [2], [3]. Are programs associated with such mission statements capable of independent thought? Figure 2 shows my proposal for a new ABET Criterion 9.

A

References [1] 2005-06 Program Evaluation Worksheet, ABET, Baltimore, MD, 2005. [2] Oral Roberts University Catalog 2005-6, vol. 24,

26 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

2. Program Educational Objectives Published and consistent with mission and these criteria Process in place to determine and periodically evaluate Based on needs of constituencies Curriculum and processes to ensure achievement Results to determine achievement Results used to improve effectiveness of the program Fig. 1. ABET Criterion 2.

9. Scientific Method Assessment Assessment demonstrates that graduates dispute the notions that: (a) The earth was created in 6 days. (b) The earth is less than 10,000 years old [4]. (c) Intelligent Design is intelligent. (d) Intelligent Design is design (see ABET Criterion 4). (e) Intelligent design is science and counts towards the required one year of mathematics and science credits in ABET curriculum analysis. (f) Noah’s flood is explained by tectonics [4]. (g) Evolution, since it is only a theory, needs more proof. (h) There is no evidence of evolution in our daily lives. (And what does bird flu have to do with anything?) (i) Man and monkeys cannot be related (j) The primordial soup is more likely to be the soup of the day than the source of life on this planet. Fig. 2. Proposed Criterion 9.

Tulsa, OK, Oral Roberts University, 2005, p. 14. [3] R. Horton, “BJU Statement of Christian Education,” Bob Jones University Web site, Oct. 2005 [Online]. Available: http://www.bju.edu/academics/ edpurpose.html [4] J.F. Ashton, Ed., In Six Days: Why Fifty Scientists Choose To Believe In Creation. Green Forest, AR: Master Books, 2003.

0739-5175/06/$20.00©2006IEEE

Gail Baura is vice president of research at CardioDynamics in San Diego, California. She is a graduate of St. Anthony Elementary School, St. Anthony High School, and Loyola Marymount University. Gail can be contacted at [email protected]. JANUARY/FEBRUARY 2006

Healthcare Information Technology at the

Speed of Life

Telemedicine. Bioinformatics. Interoperability. Biocomplexity.

IEEE Transactions on Information Technology in Biomedicine

Information technology in biomedical engineering: a technology of trust.

IEEE Pub ID 500-171

■ Lives depend on groundbreaking developments in e-health and

Annual rate: US$225

home monitoring, intelligent systems and collaborative technologies,

IEEE Member Rate: US$35

medical informatics and education, virtual reality, real-time remote

ACCE Member Rate: US$70

treatment, and distributed computing in biomedicine.

Email: [email protected]

■ IEEE Transactions on Information Technology in Biomedicine brings

ISSN: 1089-7771

Join IEEE: www.ieee.org

together experts in computer science and biomedical engineering. Submit papers: www.embs.org Read the latest innovations driving healthcare information technology in the journal that defines the field.

Subscribe today!

Published by:

Co-sponsored by:

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

Communication Theory and Molecular Biology at the Crossroads The Guest Editor Explains the State of Research © EYEWIRE

BY ELEBEOBA E. MAY

n 1948, Claude E. Shannon’s idea that images, text, and various types of data can be transmitted using a series of binary digits transformed the communication industry and society as a whole. Several years later in 1953, James Watson and Francis Crick announced the discovery of the DNA double helix. (In addition to Watson and Crick’s work, Rosalind Franklin’s research contributed significantly to the discovery of the structure of DNA.) Their discovery led to the eventual realization that proteins and regulatory signals can be represented using a series of quaternary symbols, A, T, G, and C, corresponding to the nucleic acid bases adenine, thymine, guanine, and cytosine. Whereas the quantification of Shannon’s treatise on information theory led to the birth of coding theory and has promoted advances in digital communication, satellite communication, storage technology, and biomedical imaging, the parallels between and potential impact of the intersection of Shannon’s 1948 ideas with Watson and Crick’s 1953 discovery is still being realized. As evidenced by the eight articles in this issue, researchers are increasingly curious about the communication protocols of molecular systems. In this special issue we endeavor to explore ideas at the crossroads of communication theory and molecular biology from various disciplinary backgrounds and vantage points, providing an overview of the state of research and making compelling observations regarding the nature of biological information transmission in light of the principles of digital communication.

I

Shannon and the Double Helix

We commence with an intriguing article, “Claude Shannon: Biologist,” in which Schneider describes how insights gained from the application of information theory to molecular biology suggest that Shannon’s channel capacity theorem only applies to living organisms and products of living organisms. He goes on to argue that information theory is actually a theory about biology, which makes Shannon—the father of information theory—a biologist. In the article “Should Genetics Get an InformationTheoretic Education?,” Battail describes how information theory can provide a theoretical framework for understanding the role of information in living systems. This compelling discourse shows how the hypothesis that faithful communication 28 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

of genetic information over geological time depends on errorcorrecting codes can be used to explain the evolutionary emergence of discrete species and taxonomical hierarchy as well as evolution’s trend towards increased complexity. Gupta provides a comprehensive overview of research at the intersection of Shannon’s information theory and the Watson-Crick and Franklin double helix discovery. “The Quest for Error Correction in Biology” shows the breadth of information and coding theory motivated inquiries into biological phenomena. Gupta gives special focus to work that deals with the existence of error correction in biology. Coding Theoretic Properties of Nucleic Acids

The degree to which principles from communication theory can be used to understand, describe, or explain molecular biology is an ongoing debate. A central question is whether evidence of coding theoretic properties exists in biology and if such evidence supports information or coding theory view of genetics. The next three articles explore this question from varying perspectives. We begin with Mac Dónaill’s critical assessment of the emergence of the DNA alphabet in “Digital Parity and the Composition of the Nucleotide Alphabet.” Expressing nucleotide bases as four-digit binary numbers, this fascinating article examines nucleic acid replication from a coding theory perspective and shows how this framework explains the selection of A, C, G, and T as the optimal alphabet for encoding genetic information. Beginning with a review of the structure, signal content, and mutation mechanisms that affect DNA, Rosen investigates how DNA protects itself in “Examining Coding Structure and Redundancy in DNA.” In an effort to uncover potential coding properties in genomic sequences, Rosen develops a method for detecting linear dependencies and repetitive structures in DNA. Application of these methods to the analysis of coding theoretic properties of protein-coding and nonprotein-coding regions are discussed. Gonzalez, Giannerini, and Rosa take an interesting approach in investigating the existence of error control mechanisms in genetic processes. In “Detecting Structure in Parity Binary Sequences,” they encode the exons of a gene using a mathematical coding strategy that transforms the exons into binary parity strings. The encoded sequence is analyzed for 0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

Researchers are increasingly curious about the communication protocols of molecular systems.

dependency structures, which, if discovered, would help support the hypothesis of the existence of deterministic error control within genetic sequences. Coding Theory and Gene Expression

Successful development of biological information and coding theory can provide a theoretical basis for understanding, quantifying, and engineering error control in natural and synthesized biosystems. In the future, we can envision diseases, including various forms of cancer, AIDS, and geriatric maladies quantified in terms of failures in the genetic error-control system. Thus the intersection of communication theory and molecular biology could potentially yield a quantitative framework for engineering fault-tolerant genes, proteins, and genomes that approach an organism’s communication capacity. Although such ideas remain ahead of us, the final articles in this special issue illustrate immediate applications of coding theory and coding theoretic principles to genomics. In “Finding Large Domains of Similarly Expressed Genes,” Nicorici, Yli-Harja, and Astola present a minimum description length (MDL, useful for source encoding) method for finding large domains of similarly expressed genes. They discuss results of using their method to discover coexpressed genes in Drosophila and human genomes. May, Vouk, and Bitzer present an error-control coding model for translation initiation in “Classification of Escherichia coli K-12 Ribosome Binding Sites.” Modeling the messenger RNA as noisy encoded sequence and the ribosome as an error-control decoder, they construct a Bayesian classifier to distinguish between valid and invalid ribosome binding sites using an eleven-base classification window.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Acknowledgments

I would like to acknowledge the assistance of Mladen Vouk and Donald Bitzer who served as coeditors for this special issue and Robert Istepanian for assistance in the review process. I would also like to thank John Enderle, editor-in-chief of IEEE Engineering in Medicine and Biology Magazine, for giving us the opportunity to serve as guest editors for this special issue on communication theory, coding theory, and molecular biology, and I wish to acknowledge Raouf Naguib, Ron Summers, Robert Istepanian, and the 2003 IEEE Engineering in Medicine and Biology Conference Committee for allowing us to organize a special session on this topic, from which this issue results. I am indebted to all who participated in that workshop and to Sandia National Laboratories’ Computer Science Research Institute (CSRI) for financial support of the workshop. I sincerely thank all of the authors for their contributions and diligent efforts, which made this special issue possible. Your willingness to contribute to this work has hopefully shed greater light on the exciting and growing field of biological information and coding theory. We greatly appreciate all of the reviewers for their critical reading of the articles submitted. Finally the author would like to thank all involved for their tremendous patience and cooperation in completing this special issue. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

JANUARY/FEBRUARY 2006

29

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

Claude Shannon: Biologist

© EYEWIRE

The Founder of Information Theory Used Biology to Formulate the Channel Capacity

BY THOMAS D. SCHNEIDER

laude Shannon founded information theory in the 1940s. The theory has long been known to be closely related to thermodynamics and physics through the similarity of Shannon’s uncertainty measure to the entropy function. Recent work using information theory to understand molecular biology has unearthed a curious fact: Shannon’s channel capacity theorem only applies to living organisms and their products, such as communications channels and molecular machines that make choices from several possibilities. Information theory is therefore a theory about biology, and Shannon was a biologist. Shannon (30 April 1916–24 February 2001) is heralded for his major contributions to the fundamentals of computers and communications systems [1]–[4]. His Massachusetts Institute of Technology (MIT) master’s thesis is famous because in it he showed that digital circuits can be expressed by Boolean logic. Thus, one can transform a circuit diagram into an equation, rearrange the equation algebraically, and then draw a new circuit diagram that has the same function. By this means, one can, for example, reduce the number of transistors needed to accomplish a particular function. Shannon’s work at Bell Labs in the 1940s led to the publication of the famous paper “A Mathematical Theory of Communication” in 1948 [5] and to the lesser known but equally important “Communication in the Presence of Noise” in 1949 [6]. In these groundbreaking papers, Shannon established information theory. It applies not only to human and animal communications, but also to the states and patterns of molecules in biological systems [7]–[9]. At the time, Bell Labs was the research and development part of the American Telephone and Telegraph Company (AT&T), which was in the business of selling the ability to communicate information. How can information be defined precisely? Shannon, a mathematician, set down several criteria for a useful, rigorous definition of information, and then he showed that only one formula satisfied these criteria. The definition, which has withstood the test of more than 50 years, precisely answered the question What is AT&T selling? The answer was information transmission in bits per second. Of course, this immediately raised another question: How much information can we send over existing equipment, our phone lines? To answer this, Shannon developed a mathematical theory of the channel capacity.

C

30 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Before delving into how he arrived at this concept, which explains why Shannon was a biologist, it is necessary to understand the surprising (Shannon’s word) channel capacity theorem, and how it was developed. The channel capacity C in bits per second, depends on only three factors: the power P of the signal at the receiver, the noise N disturbing the signal at the receiver, and the bandwidth W, which is the span of frequencies used in the communication: C = Wlog2 (P/N + 1) bits per second.

(1)

Suppose one wishes to transmit some information at a rate R, also in bits per second (b/s). First, Shannon showed that when the rate exceeds the capacity (R > C), the communication will fail and at most C b/s will get through. A rough analogy is putting water through a pipe. There is an upper limit for how fast water can flow; at some point, the resistance in the pipe will prevent further increases or the pipe will burst. The surprise comes when the rate is less than or equal to the capacity (R ≤ C). Shannon discovered, and proved mathematically, that in this case one may transmit the information with as few errors as desired! Error is the number of wrong symbols received per second. The probability of errors can be made small but cannot be eliminated. Shannon pointed out that the way to reduce errors is to encode the messages at the transmitter to protect them against noise and then to decode them at the receiver to remove the noise. The clarity of modern telecommunications, CDs, MP3s, DVDs, wireless, cellular phones, etc., came about because engineers have learned how to make electrical circuits and computer programs that do this coding and decoding. Because they approach the Shannon limits, the recently developed Turbo codes promise to revolutionize communications again by providing more data transmission over the same channels [10], [11]. What made all this possible? It is a key idea buried in a beautiful geometrical derivation of the channel capacity in Shannon’s 1949 paper [6]. Suppose that you and I decide to set up a simple communications system (Figure 1). On my end, I have a 1-volt (V) battery and a switch. We run two wires over to you and install a volt meter on your end. When I close the switch, you see the meter jump from 0 to 1 V. If I 0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

The concept that the spheres must be separated is a biological criterion that does not apply to physical systems in general.

set the switch every second, you receive up to 1 b/s. But on (In the preceding, T is the message time and W is the bandcloser inspection, you notice that the meter doesn’t always width.) The transmitter picks the point and the receiver read exactly 1 V. Sometimes it reads 0.98, other times 1.05, receives a point located in a fuzzy sphere around the transand so on. The distribution of values is bell shaped mitted point. This would not be remarkable except for an (Gaussian), because the wire is hot (300 K). From a thermointeresting property of high-dimensional spheres. As the dynamic viewpoint, the heat is atomic motions, and they disdimension goes up, almost all the received points of the turb the signal, making it noisy. You can hear this as the static sphere condense onto the surface at radius r, as shown by hiss on a radio or see it as snow on a television screen. Shannon realized that the noise added to one digital pulse would genREPRESENTING A MESSAGE AS A HYPERSPHERE erally make the overall amplitude different from that of another, otherwise (a) A simple electrical communications system consists of a battery, a switch identical, pulse. Further, the noise amplitudes for two pulses are indepenand a volt meter connected by wires. dent. When two quantities are inde(b) The voltage of one pendent, one can represent this pulse sent down the transgeometrically by graphing them at 90◦ Switch mission line is disturbed, as to each other (orthogonal). Shannon Transmission in a drunken walk, by the recognized that for two pulses, the Battery Volt Meter Line individual Gaussians combined to motion of atoms in the hot make a little circular smudge on a twowire, so the voltage re(a) dimensional graph of the voltage of ceived will vary according the first pulse plotted against the voltto a Gaussian distribution. For a first voltage pulse, x , the probability of the voltage of the second pulse, as shown in 2 age variation is p(x) ∝ e −x . Figure 1. If three digital pulses are A second voltage pulse, sent, the possible combinations can be plotted as corners of a cube in three 2 y, has a distribution p(x)∝e−x dimensions. The receiver, however, −y 2 p(y) ∝ e . Since noise is does not see the pristine corners of the independent for the two cube. Instead, surrounding each corner pulses, the probabilities of x are fuzzy spheres that represent the the distributions are indepenprobabilities of how much the signal (b) can be distorted. dent and the overall proba2 2 2 2 2 With four pulses, the graph must be bility multiplies: p(x, y) = p(x) · p(y) ∝ e −x · e −y = e −(x +y ) = e −r . made in four-dimensional space, and (c) Plotting the voltage variation of x against the voltage variation of y, one the cube becomes a hypercube (tesserfinds that r is the hypotenuse of a triangle with x and y as the legs. To see the shape act), but the spheres are still there at of the distribution, set the each corner. probability p(x, y)to be cony Shannon realized that when one looks at many pulses—a message— stant. This fixes r as the radius they correspond to a single point in a of a circle. So the distribution r high dimensional space. is circularly symmetric. With

x

three pulses, p(z ) ∝ e −z and 2

Essentially, we have replaced a complex entity (say, a television signal) in a simple environment (the signal requires only a plane for its representation as f(t)) by a simple entity (a point) in a complex environment (2TW dimensional space) [6].

p(x, y, z ) ∝ e

−r 2

again, so the

distribution is a sphere in higher dimensions. (c)

Fig. 1. Representing a message as a hypersphere.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

31

Because a communications failure can have serious consequences for a living organism, Darwinian selection will prevent significant sphere overlap.

Brillouin and Callen [7], [12], [13]. At high dimension, the sphere density function becomes a sharply pointed distribution [7]. Shannon called these spheres “sharply defined billiard balls,” but I prefer “ping-pong balls” because they are hollow and have thin shells. The sharp definition of the sphere surface at high dimension has a dramatic consequence. Suppose that I want to send you two messages. I represent these as two points in a high-dimensional space. During transmission, the signal encounters thermal noise and is degraded in all possible ways so that you receive results somewhere in two spheres. If the spheres are far enough apart, you can easily determine the nearest sphere center because we agree beforehand where I will place my points. That is, you can decode the noisy signal and remove the noise! Of course, this only works if the spheres do not overlap. If the spheres overlap, then sometimes you cannot determine which message I sent.

Fig. 2. A gumball machine represents a communications system, as seen by a receiver. Each gumball represents the volume of coding space a single transmitted message (a point in the space) could be moved to after thermal noise has distorted the message during communication. The entire space accessible to the receiver, represented by the outer glass shell, is determined by the power received, the thermal noise, and the bandwidth. The number of gumballs determines the capacity of the machine and is estimated by dividing the volume enclosed by the outer glass shell by the volume of each gumball. A similar computation gives the channel capacity of a communications system [6]. The painting is by Wayne Thiebaud (b. 1920) Three Machines (1963), oil on canvas, Fine Arts Museums of San Francisco; copyright Wayne Thiebaud/licensed by VAGA, New York, N.Y., reproduced with permission. The image was obtained from http://www.artnet.com/magazine/news/newthismonth/walrobinson2-1-16.asp.

32 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

The total power of the received signal allows me (at the transmitter) to pick only a limited number of messages, and they all must be within some distance from the origin of the high-dimensional space. That is, there is a larger sphere around all the smaller thermal spheres that represent possible received messages. Shannon recognized this, and then he computed how many little message spheres could fit into the big sphere provided by the power and also the thermal noise, which extends the big sphere radius. By dividing the volume of the big sphere by the volume of a little one, he determined the maximum number of messages just as one can estimate the number of gumballs in a gumball machine (Figure 2). Taking the logarithm (base 2) gave the result in bits. This gave him the channel capacity formula (1), and, using the geometry of the situation, he proved the channel capacity theorem [6]. We can see now that this theorem relies on two important facts. First, by using long messages, one gets high dimensions and so the spheres have sharply defined surfaces. This allows for as few errors in communication as one desires. Second, if one packs the spheres together in a smart way, one can send more data, all the way up to the channel capacity. The spherepacking arrangement is called the coding, and for more than 50 years, mathematicians have been figuring out good ways to pack spheres in high dimensions. This results in the low error rates of modern communications systems. Even when they are far apart, the spheres always intersect by some amount because Gaussian distributions have infinite tails. That is why one can’t avoid error entirely. On the other hand, if the distance between two sphere centers is too small, then the two spheres intersect strongly. When random thermal noise places the received point into the intersection region, the two corresponding messages will be confused by the receiver. The consequences of this could be disastrous for the sender or the recipient, who could even die from a misunderstanding. Because a communications failure can have serious consequences for a living organism, Darwinian selection will prevent significant sphere overlap. It can also go to work to sharpen the spheres and to pack them together optimally. For example, a metallic key in a lock is a multidimensional device because the lock has many independent pins that allow a degree of security. When one duplicates the key, it is often reproduced incorrectly, and one will have to reject the bad one (select against it). If one’s home is broken into because the lock was picked, one might replace the lock with a better one that is harder to pick (has higher dimension). Indeed, key dimension has increased over time. The ancient Romans and the monks of the Middle Ages used to carry simple keys for wooden door locks with one or two pins, while the key to my lab seems to have about 12 dimensions. All communications systems have the property that they are important to living organisms. That is, too much sphere JANUARY/FEBRUARY 2006

overlap is detrimental. In contrast, although the continuously changing microstates of a physical system, such as a rock on the moon or a solar prominence, can be represented by one or more thermal noise spheres, these spheres may overlap, and there is no consequence because there is no reproduction and there are no future generations. A living organism with a nonfunctional communication system is unlikely to have progeny, so its genome may disappear. Shannon’s crucial concept was that the spheres must not intersect in a communications system, and from this he built the channel capacity formula and theorem. But, at its root, the concept that the spheres must be separated is a biological criterion that does not apply to physical systems in general. Although it is well known that Shannon’s uncertainty measure is similar to the entropy function, the channel capacity and its theorem are rarely, if ever, mentioned in thermodynamics or physics, perhaps because these aspects of information theory are about biology, so no direct application could be found in those fields. Since he used a property of biology to formulate his mathematics, I conclude that Claude Shannon was doing biology and was therefore, effectively, a biologist—although he was probably unaware of it. It is not surprising that Shannon’s mathematics can be fruitfully applied to understanding biological systems [7], [8], [14]. Models built with information theory methods can be used to characterize the patterns in DNA or RNA to which proteins and other molecules bind [15]–[19] and even can be used to predict if a change to the DNA will cause a genetic disease in humans [20], [21]. Further information about molecular information theory is available at the Web site http://www.ccrnp.ncifcrf.gov/~toms/. What are the implications of the idea that Shannon was doing biology? First, it means that communications systems and molecular biology are headed on a collision course. As electrical circuits approach molecular sizes, the results of molecular biologists can be used to guide designs [22], [23]. We might envision a day when communications and biology are treated as a single field. Second, codes discovered for communications potentially teach us new biology if we find the same codes in a biological system. Finally, the reverse is also to be anticipated: discoveries in molecular biology about systems that have been refined by evolution for billions of years should tell us how to build new and more efficient communications systems. Acknowledgments

I thank Denise Rubens, Herb Schneider, Doris Schneider, John Spouge, John Garavelli, Pete Rogan, Jim Ellis, Ilya Lyakhov, Michael Levashov, Zehua Chen, Danielle Needle, and Marirose Coulson for comments on the manuscript. This research was supported by the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute–Fredrick. Thomas D. Schneider is a research biologist at the National Cancer Institute in Frederick, Maryland. He graduated from the Massachusetts Institute of Technology in biology (1978) and received his Ph.D. from the University of Colorado in molecular biology (1986). His primary work is analyzing the binding sites of proteins on DNA and RNA in bits of information. Since beginning this IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

research, he thought that he was taking Shannon’s ideas “kicking and screaming” into molecular biology. But, after crawling out of many pitfalls, the connection between information theory and molecular biology became so clear and the results so plentiful that he dug deeper and eventually discovered that information theory was already about biology. Address for Correspondence: Thomas D. Schneider, National Cancer Institute, Center for Cancer Research Nanobiology Program, Molecular Information Theory Group, Frederick, Maryland 21702-1201 USA. E-mail: [email protected]. References [1] N.J.A. Sloane and A.D. Wyner, Claude Elwood Shannon: Collected Papers. Piscataway, NJ: IEEE Press, 1993. [2] J.R. Pierce, An Introduction to Information Theory: Symbols, Signals and Noise, 2nd ed. New York: Dover Publications, Inc., 1980. [3] R. Calderbank and N.J. Sloane, “Obituary: Claude Shannon (1916–2001),” Nature, vol. 410, p. 768, 2001. [4] S.W. Golomb, “Claude E. Shannon (1916–2001),” Sci., vol. 292, p. 455, 2001. [5] C.E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, pp. 379–423, 623–656, 1948 [Online]. Available: http://cm.belllabs.com/cm/ms/what/shannonday/paper.html [6] C.E. Shannon, “Communication in the presence of noise,” Proc. IRE, vol. 37, pp. 10–21, 1949. [7] T.D. Schneider, “Theory of molecular machines. I. Channel capacity of molecular machines,” J. Theor. Biol., vol. 148, pp. 83–123, 1991 [Online]. Available: http://www.ccrnp.ncifcrf.gov/~toms/paper/ccmm/ [8] T.D. Schneider, “Theory of molecular machines. II. Energy dissipation from molecular machines,” J. Theor. Biol., vol. 148, pp. 125–137, 1991 [Online]. Available: http://www.ccrnp.ncifcrf.gov/~toms/paper/edmm/ [9] T.D. Schneider, “Sequence logos, machine/channel capacity, Maxwell’s demon, and molecular computers: a review of the theory of molecular machines,” Nanotechnol., vol. 5, pp. 1–18, 1994 [Online]. Available: http://www.ccrnp. ncifcrf.gov/~toms/paper/nano2/ [10] C. Berrou, A. Glavieux, and P. Thitimajshima, “Near Shannon limit error-correcting coding and decoding: Turbo-codes,” Proc. IEEE, vol. 2, pp. 1064–1070, May 1993. [11] E. Guizzo, “Closing in on the perfect code,” IEEE Spectr., vol. 41, no. 3, pp. 36–42, Mar. 2004. [12] L. Brillouin, In Science and Information Theory. New York: Academic, 1962, p. 247. [13] H.B. Callen, In Thermodynamics and an Introduction to Thermostatistics. New York: Wiley, 1985, p. 347. [14] T.D. Schneider, G.D. Stormo, L. Gold, and A. Ehrenfeucht, “Information content of binding sites on nucleotide sequences,” J. Mol. Biol., vol. 188, pp. 415–431, 1986 [Online]. Available: http://www.ccrnp.ncifcrf.gov/ ~toms/paper/schneider1986/ [15] R.M. Stephens and T.D. Schneider, “Features of spliceosome evolution and function inferred from an analysis of the information at human splice sites,” J. Mol. Biol., vol. 228, pp. 1124–1136, 1992 [Online]. Available: http://www. ccrnp.ncifcrf.gov/~toms/paper/splice/ [16] P.N. Hengen, S.L. Bartram, L.E. Stewart, and T.D. Schneider, “Information analysis of Fis binding sites,” Nucleic Acids Res., vol. 25, no. 24, pp. 4994–5002, 1997 [Online]. Available: http://www.ccrnp.ncifcrf.gov/~toms/paper/fisinfo/ [17] R.K. Shultzaberger and T.D. Schneider, “Using sequence logos and information analysis of Lrp DNA binding sites to investigate discrepancies between natural selection and SELEX,” Nucleic Acids Res., vol. 27, no. 3, pp. 882–887, 1999 [Online]. Available: http://www.ccrnp.ncifcrf.gov/~toms/paper/lrp/ [18] R.K. Shultzaberger, R.E. Bucheimer, K.E. Rudd, and T.D. Schneider, “Anatomy of Escherichia coli ribosome binding sites,” J. Mol. Biol., vol. 313, pp. 215–228, 2001 [Online]. Available: http://www.ccrnp.ncifcrf.gov/~toms/paper/flexrbs/ [19] M. Zheng, B. Doan, T.D. Schneider, and G. Storz, “OxyR and SoxRS regulation of fur,” J. Bacteriol., vol. 181, pp. 4639–4643, 1999 [Online]. Available: http://www.ccrnp.ncifcrf.gov/~toms/paper/oxyrfur/ [20] P.K. Rogan and T.D. Schneider, “Using information content and base frequencies to distinguish mutations from genetic polymorphisms in splice junction recognition sites,” Human Mutation, vol. 6, pp. 74–76, 1995 [Online]. Available: http://www.ccrnp.ncifcrf.gov/~toms/paper/colonsplice/ [21] P.K. Rogan, B.M. Faux, and T.D. Schneider, “Information analysis of human splice site mutations,” Human Mutation, vol. 12, pp. 153–171, 1998 [Online]. Available: http://www.ccrnp.ncifcrf.gov/~toms/paper/rfs/ [22] P.N. Hengen, I.G. Lyakhov, L.E. Stewart, and T.D. Schneider, “Molecular flip-flops formed by overlapping Fis sites,” Nucleic Acids Res., vol. 31, no. 22, pp. 6663–6673, 2003. [23] T.D. Schneider and P.N. Hengen, “Molecular computing elements: Gates and flip-flops,” U.S. Patent 6 774 222, European Patent 1057118, 2004. U.S. Patent WO 99/42929, PCT/US99/03469 [Online]. Available: http://www.ccrnp.ncifcrf.gov/ ~toms/ patent/molecularcomputing/

JANUARY/FEBRUARY 2006

33

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

Should Genetics Get an Information-Theoretic Education? Genomes as Error-Correcting Codes © EYEWIRE

BY GÉRARD BATTAIL

he main contribution of engineering to biology and medicine has mainly been as yet ancillary, e.g., providing instrumentation in fields like imagery and assisted diagnosis, which enables investigating reality far beyond the range accessible to the senses alone, thus widely improving the vision and control that biologists, physicians, and surgeons can have on living things. We shall outline here another potential contribution of engineering that is completely different and has few precedents, that of providing a theoretical framework and conceptual tools to biologists. This article is intended to show that engineering concepts can help account for the prominent role of information in life phenomena. Living beings are open systems which exchange matter, energy, and information with the outer world, with each others, and in the process of their own operation. They receive information from the outer world, and information circulates inside themselves and between individuals at any scale, from the molecular level to that of ecosystems and beyond. Moreover, they are constructed and maintained using the genetic information they receive from their ancestors since the very beginning of life, some 3.5 billion years ago or maybe even earlier. In its historical development, biology slowly integrated the sciences of matter and energy, namely chemistry and physics, which enabled it to go far beyond the mere description of the living world, thereby acquiring a truly scientific status. Last but not least, the less obvious importance of information in the life phenomena has been recognized much later than that of matter and energy. Modern biology assigns an ever increasingly important role to information, but the science relevant to it, information theory, has not been integrated into biology yet as were chemistry and physics much earlier. It is indeed a much younger science since its birth can be dated to 1948 when Shannon’s papers were published [1]. Its strong connection with communication techniques, its mathematical formalism, and some conceptual difficulties made its impact on other sciences rather limited. For several decades, it looked as a rather abstract matter with little impact even on communication techniques for its lack of proper implementation means. However, with the tremendous development of the semiconductor technology (which also started in 1948!), the applications of information theory are by now countless. As a striking example, mobile telephony would simply not exist without

T

34 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

information theory and the coding techniques it generated. The many succcessful applications of information theory now provide strong experimental proof of its validity in the field of communications. Although it became the most powerful conceptual tool avaible in this field, it remains almost unknown outside the communication engineers’ community. It shaped much of the present way of living, but few people realize this even though everybody makes daily use of its engineering products. That information theory is largely unknown in the biologists’ community does not mean that the importance of information in the living world is overlooked. An increasing number of biological papers are devoted to many forms of information recording and transfer, and the word information has become ubiquitous in the biological literature. However, it is used most often with a loose meaning. Many biologists seem to ignore that the scientific concept of information generated a science having reached maturity. We believe that no real progress will result from the recognition of the prominent role of information in life phenomena unless information theory is integrated into biology as physics and chemistry had been. Among the many domains of biology where information plays a prominent role, we shall restrict ourselves to the communication of genetic information through the ages. Starting from the very fundamental question How is genetic information faithfully communicated?, we hypothesize that nature developed error-correcting codes since the origin of life. As powerful tools available to communication engineers, errorcorrecting codes are, paradoxically, reliable communication over unreliable channels. Some of the most important results of information theory are statements about these codes, especially concerning the attainable limits of their performance. It turns out that aside from answering the above question, our hypothesis also sheds light on the process of biological evolution and on the structure of the living world. We first formulated it and discussed its biological impact in [2]. Subsequent papers were devoted to refine this hypothesis and to better understand its consequences. We also tried to identify the error-correcting means involved and to understand how they are implemented [3]–[5]. Besides the interest of this topic of its own, we also think of it as exemplifying 0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

the mutual benefits that a collaboration between information theorists and biologists could provide. Genetics was at its beginnings a rather abstract science. Later, the discovery by Avery et al. [6] that DNA is the bearer of genetic information, and the subsequent discovery of its double-helix structure by Franklin, Watson, and Crick [7], [8], gave a chemical content to the concept of gene. In 1979, Chargaff wrote a paper entitled “How Genetics Got a Chemical Education” [9], where he complained that genetists were so reluctant to accept the consequences of the discovery that DNA was the actual bearer of genetic information (35 years earlier) that a more appropriate title of his paper could have been “How Genetics Refused to Get a Chemical Education.” On the interrogative mode, the title of this article is an allusion to Chargaff’s. Besides the chemical structure of DNA, we believe it is time to consider the abstract framework of information theory as appropriate to genetics. There is another reason why this title is relevant. It stresses the need for an education; that is, genetists should make an effort to assimilate a topic that is rather foreign to their traditional culture. A superficial knowledge of the results of information theory does not suffice. Only a deep enough understanding of the topic itself, including its paradoxes, can be fruitful since adaptation is required before information theory can be of any use in genetics (and, more generally, in biology). The problems of terminology then become of paramount importance, since the same words are often used with different meanings in genetics and in information theory. As an example, the “genetic code” is not truly a code in the informationtheoretic sense. Extreme attention must therefore be be paid to the property of vocabulary. The Faithful Communication of Genetic Information: A Crucial Question A Model of Genetic Communication

When we first attempted to study evolution at the light of information theory, we found that high-quality popularizing books dealing with genetics and biological evolution, especially those authored by Dawkins [10], [11], contain a very simple model of genetics and biological evolution: the genome is made of deoxiribonucleic acid (DNA), a long unidimensional polymer bearing nucleic bases (or nucleotides), which are small molecules of only four different types, denoted A (adenine), T (thymine), G (guanine), and C (cytosine). Each nucleic base acts as a symbol of the quaternary alphabet {A, T, G, C}, and the genetic message consists of a sequence of such molecules. The genome can be replicated. Each genome is housed inside a phenotype which shields it against outer perturbations which would destroy it if left unprotected. The development of the phenotype is controlled by the DNA message itself, which directs a succession of protein syntheses through the “genetic code” (we use quotes here and in the sequel as a reminder that this is a mapping rather than a code in the engineering meaning). This succession of protein syntheses results in the construction of a phenotype through an extremely complicated and still poorly understood process. The phenotypes are subject to natural selection, so the only remaining genomes host surviving phenotypes. We may thus think of the communication of genetic information through the ages as a recording and copying process. An initial written message has been copied several times, its IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

copies themselves have been copied, and this process has been repeated. It reminds how texts written in antiquity were made available to us thanks to generations of monks. In a sense, however, this metaphor is misleading. Ink strokes on parchment are macroscopic objects involving a huge number of molecules so they may be expected to strongly resist degradation. Contrasting with any man-made memory element, the bearer of the genetic message is a single DNA molecule. Belonging to the submicroscopic world, it may be thought of as highly vulnerable to degradation by mechanical, chemical, and radiative agents, and since it is relevant to quantum physics, it can be described only in terms of probabilities. Contrary to any expectation, the genetic message has however unmatched longevity since for instance the HOX genes which determine the organization plan of living beings are shared by, e.g., humans and flies, which diverged from a common ancestor hundreds of millions of years ago. Explaining this very paradoxical longevity, which is the cornerstone of molecular genetics, has been our major goal. DNA should be shielded by membranes, rather obviously, because a protection against mechanical constraints and chemical reactants is necessary. But radiations of cosmic or solar origin, or due to natural radioactivity, are pervasive threats against DNA integrity. At a still more fundamental level, the DNA molecule is a quantum object which cannot bear a precisely defined message unless it is protected against its own indeterminism. Protection against radiations and indeterminism cannot be provided by outer devices like a membrane but must be intrinsic to the genome itself. As a consequence, the idea that the phenotype is the sole target of natural selection is not tenable. The genome must itself be subject to it with regard to its capacity to resist errors (here we encounter a statement formulated by some biologists who introduced the concept of “genome phenotype,” a seeming oxymoron [12]). The problem of natural selection should be restated to include the hypothesized existence of an errorcorrecting system intrinsic to the genome. Reformulating the Model

When a phenotype is destroyed in the process of natural selection, the genome it hosts disappears. But a genome also ceases existing if an error transforms it into another one. Therefore, survival of any genome implies that its replication be as reliable as possible, aside from it hosting a well-fitted phenotype. Although they properly recognized the importance of maintaining the genome integrity in the evolutive success of a species, biologists did not realize how difficult it is to perform at the scale of geological times. Dawkins wrote about the needed accuracy of replication [10], pp. 16–17: We do not know how accurately the original replicator molecules made their copies. Their modern descendants, the DNA molecules, are astonishingly faithful compared with the most high-fidelity human copying process. He expresses his astonishment about such a high reliability but does not question the ways to obtain it nor the consequences which may result from their use. Communication engineers know that the answer lies in the use of error-correcting codes and that the price to pay for correction ability is redundancy, which should be high enough to make the transmission rate less than the channel capacity, a fundamental limit set by JANUARY/FEBRUARY 2006

35

The word information has become ubiquitous in the biological literature.

information theory. It is why we suggested that information theory could be relevant to biological evolution [2]. Resuming in [3] the argument of [10], p. 24, we considered the two antagonistic properties of fecundity and permanency as being both beneficial to the genome conservation. Fecundity refers to the rate at which the genome replicates itself. We named permanency the ability of a genome to remain strictly identical to itself whether it has been replicated or not, thus combining the two properties of longevity and copying-fidelity that Dawkins distinguished. The material for making copies of the genomes is available in limited quantity so only the most successful ones in maintaining and replicating themselves will survive. This is the most basic form of natural selection. The most abundant, hence eventually surviving, genomes will then be those which optimally combine permanency and fecundity. Clearly, the balance between these two properties can be very different from one species to another, and the selective success can mainly rely on a high fecundity (as for viruses and bacteria) or on a high permanency (as for very complex beings like animals and plants). For a given replication mechanism, the shorter the genome, the higher the fecundity. However, the genome size has a lower limit because it must specify the machinery and process for its own replication (we exclude here the smallest genomes, those of viruses, which are devoid of a replication machinery of their own and must use that of host cells to replicate themselves). The attainable survival ability, which results from fecundity alone, therefore, has an upper limit. Simultaneously starting the replication process at several places would probably not successfully increase fecundity for short genomes, because complicating the replication process would need a longer genome for specifying its machinery, with a presumably detrimental effect on the overall replication speed. Only already long genomes could benefit from such a strategy, insofar as the genome part devoted to specify the replication machinery is small with respect to the remainder. If error-correcting codes are used in the genome replication process, they are themselves products of evolution. Due to their key role in the genome conservation and the efficiency of the Darwinian process, we may safely assume that natural selection made them evolve to come very close to the limit of what is possible, so the error correction means that we hypothesized are endowed with the properties of theoretically optimal codes, hence exhibiting the seemingly paradoxical behavior of a decreasing error rate as the codeword length increases (see below). Then, increasing the length of a genome increases its permanency. Moreover, no upper limit is set to the genome length, so increasing it appears as a way for a genome to endlessly enhance its ability to survive natural selection. To summarize, permanency as just defined measures the ability of a genomic message to survive in its physical, chemi36 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

cal, and biological environment, including that of resisting radiations and its own indeterminism. Therefore, the survival of a genome does not only depend on the ability of the phenotype it hosts to exploit its physical and ecological environment, escaping predators and resisting pathogenic agents, but also, and more fundamentally, on the error-correcting means it developed. This just generalizes the concept of natural selection to encompass the most pervasive and ubiquitous threats to the genome integrity. At variance with the traditional view that the phenotype is the sole target of natural selection (as in Dawkins’s model recalled above), this point of view extends the concept of natural selection to the genome itself. Aging, Mutations, and Variability of Genome Conservation

A very strong argument for the need of genomic error-correcting means (perhaps the most convincing one) is the fact that mutations, i.e., errors in the genome replication due to chemical agents or radiations, are responsible for aging and certain diseases like cancers. Had the error rate in communicating genomic information noticeable effects at the scale of the lifetime of an individual, the accumulation of errors during periods million times longer would simply make genetic communication—hence life—impossible. Moreover, if we look at the litterature on chromosomes and cellular division and the literature on the performance of DNA replication, the former appears as describing messy, involved, and unreliable mechanisms; however, outstanding faithfulness of DNA replication is reported in the latter. This sharp contrast strongly suggests that mechanisms needed for correcting replication errors actually exist. Based on the duplication, in complementary form, of the sequence of nucleotides in the double-helix structure and the assumption that damages on one string can be corrected in terms of the other one, many “proofreading” mechanisms are known. However, they can at best ensure that the copy is faithful to the original. In other words, they can correct the errors which occur within the replication process but not those that may affect the original itself. Faithful copying thus does not adequately describe the function of replication. The needed property, which only errorcorrecting codes can provide, is resilience to casual errors. The error rate of DNA replication is reported to be of about 10(−9) per nucleic base and per replication for higher animals. It is greater 10(−3) per year and even more, which amounts to a rate per replication some hundred times larger than that of higher beings) for some genes of viruses and bacteria. This large difference between more or less complex living beings is itself difficult to understand without hypothesizing that more efficient error-correcting means exist in higher living beings than in bacteria and viruses. And this assumption itself is consistent with the difference of the corresponding genome sizes and the result JANUARY/FEBRUARY 2006

of information theory alluded to above that the longer the encoded message, the more efficiently errors can be corrected. Although a high longevity is an advantage for a particular genome, replication errors are necessary for the evolution process as a whole. They should be as rare as possible in order to keep the identity of a particular genome, but they play a major role in evolution since each error which changes the information borne by the genome generally results in a change in the corresponding phenotype, referred to as a mutation (except for “synonymous” ones which transform a codon into another one, which specifies the same amino acid). One may think of mutations as randomly exploring the set of possible phenotypes, the chosen phenotypes being further filtered by natural selection. We developed the basic ideas originally expressed in [2] into two main directions: first, analyze the consequences of the hypothesis that error-correcting codes are involved in the genome replication process and compare them with known features of the living world; second, try to identify the errorcorrecting mechanisms that nature implements. Another goal was to convince genetists that information theory and errorcorrecting codes could be useful to their discipline. Indeed, little progress in the two directions mentioned above could be expected without the active collaboration of biologists, especially as regards experimental works which are crucially needed in order to validate or refute hypotheses. This goal is far from being reached, and it is why the research presented here remained as yet speculative. As regards the first direction, no contradictions were found between the hypothesis that natural genomic error-correcting means exist and the properties of the living world. On the contrary, it seems to account for a number of facts, especially of evolution, that conventional theories fail to explain. The subsidiary hypothesis that nature uses nested codes (to be defined below) moreover explains that a hierarchical taxonomy is possible. In the second direction, the concept of soft code, which both weakens and widely extends the engineering concept of error-correcting code, also to be defined below, enables associating error-correcting properties with constraints on DNA and proteins, thus suggesting that many potential genetic error-correcting systems actually exist. The problem becomes having a precise understanding of whether and how they are implemented, i.e., how the dependence induced by the constraints between the nucleic bases is actually used to regenerate the genome. (Incidentally, we found that in the absence of an explicit encoding process, regeneration better describes what in engineering terms is referred to as decoding.) Before we develop these topics, we must give an insight on error-correcting codes: what they are, how they work, and what their main properties are. An Outlook on Error-Correcting Codes Introduction and Geometrical Representation

Error-correcting codes appear in the engineering literature as a highly mathematical topic, which gives little hold on intuition. However, we believe that their understanding does not need a big mathematical apparatus, as examplified by [13], which describes the most successful codes yet known, the turbo codes, in nonmathematical terms. We now try to state the principles behind error-correcting codes in simple words, using a geometrical representation that is very helpful in getIEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

ting an intuitive insight and that is also mathematically rigorous. We shall also provide an introduction to the concepts of regeneration and soft code that are used below. Let us first define an alphabet of size q as a collection of q different symbols which may be any signs or objects that can be unambiguously distinguished, like letters, digits, electric voltages, signal forms, molecules, etc. The smallest size of an alphabet is q = 2, and the main properties of codes can be understood if we assume, as we shall do most often in this section, that the alphabet is binary with its symbols denoted by 0 and 1. Let us now define a word of length n as a sequence of n symbols from an alphabet of size q. Each of its symbols can assume q distinguishable values, so the total number of possible different words is qn (2n in the binary case). It will be very convenient to interpret an n-symbol word as defining a point in an n-dimensional space, each of its coordinates being one of the n symbols. For instance, if q = 2 and n = 3, there are 23 = 8 different possible words, each representing a vertex of a cube. The useful values of n are much larger, but there is no difficulty in extending this definition to n > 3. Inside this n-dimensional space, we may define the Hamming distance d between two words as being the number of coordinates where their symbols differ. For instance, if n = 7, the distance between 1101000 and 0110100 is d = 4. An error-correcting code is a subset of all possible nsymbol words such that the minimum distance between any two of its words is larger than 1. Two n-symbol words may differ in a single coordinate, so an error-correcting code is a strict subset of the set of all n-symbol words. The property that no n-symbol word belongs to the error-correcting code is referred to as redundancy. In the case where n = 3, we may define a code as containing only words with an even number of symbols “1” (of even weight), namely, 000, 011, 110, and 101. The minimum distance between two of its words is d = 2. A code with the largest possible minimum distance for n = 3, i.e., d = 3, only contains two words, for instance 000 and 111. In a communication system using an error-correcting code, only words belonging to this code may be transmitted. As an example, consider a binary code used over a channel where an error consists of changing a 1 into a 0 or vice-versa. Then the channel errors result in a received word, which possibly differs from the transmitted one and is at a Hamming distance from it equal to the number of errors which occurred, say e, to be referred to as the weight of the error pattern. For a binary symmetric channel, i.e., where an error occurs with a constant probability p < 1/2, independently, on each symbol of the word, the probability of an error pattern of weight e is simply Pe = pe (1 − p)(n−e) which, for p < 1/2, is a decreasing function of its weight e. (Assuming p < 1/2 does not restrict generality, since the labeling of the received symbols by 0 or 1 is arbitrary, so it can be chosen such that this inequality holds.) In order to determine the codeword which has most probably been transmitted, we may use as a rule: Choose the codeword the closest to the received word. This rule is expressed in very simple geometrical terms thanks to the definition of a distance in the n-dimensional space; its implementation will be referred to as regeneration. The mere statement of this rule enables us to understand the most important properties that an error-correcting code must possess in order to be efficient. Its words must be far JANUARY/FEBRUARY 2006

37

from each other, so they should be very few as compared with all possible n-symbol words (its redundancy should be high). But the words should also be as evenly distributed in the n-dimensional space as possible, since any concentration of codewords would reduce their mutual distances with respect to the case of a more even distribution. For a given amount of redundancy, endowing a code with this property is by far the most difficult task in the design of an error-correcting code, although its necessity is quite intuitive and its statement is easy. Errorless Communication Is Possible Over a Noisy Channel

It was convenient in the above examples to consider small values of the word length n. Let us now go to the other extreme and assume that n is very large. Then, the law of large numbers tells that the weight of an error pattern is very probably close to its average, namely np (in other words, the frequency of errors measured in a large sample is with high probability close to the error probability). In geometrical parlance, the received point is with high probability close to the “surface” [an (n − 1)-dimensional volume] of the n-dimensional sphere of radius np centered on the transmitted word. If the radius np is smaller than half the minimum distance d between any two words (simply referred to as the minimum distance of the code), then clearly the received word is with high probability closer to the truly transmitted word than to any other, so the above regeneration rule succeeds with high probability. Moreover, the probability of a regeneration error vanishes as n approaches infinity. On the contrary, if np > d/2, a wrong codeword is often closer to the received word and the regeneration rule above generally fails. As the word length n approaches infinity, the probability of a regeneration error approaches 1. The regeneration rule thus fails with low probability if p < d/2n but with high probability if p > d/2n. The transition between the two behaviors is the sharper, the larger n. Notice the paradox: For a given probability p of channel error, increasing the word length n also increases the average number of erroneous symbols in the received word. Nevertheless, increasing n decreases the probability of a regeneration error provided p < d/2n. If this inequality holds, errorless communication of a message through an unreliable channel is possible. This result is in itself paradoxical, and nobody imagined it could be reached anyway before its possibility was proved by information theory. It started the researches on error-correcting codes. We hypothesize that the faithful communication of genomic information precisely uses this possibility, with the genome replication actually consisting of its regeneration as just described. Designing Optimal or Nearly Optimal Error-Correcting Codes

No general solution is known to the problem of designing an optimal error-correcting code for arbitrary values of n, p, and the alphabet size q, so the search for such a code may look hopeless. It is, however, possible to approximately (exactly as n approaches infinity) solve a closely related problem. In geometrical terms, choosing M points at random within the ndimensional space, M an arbitrary integer, results in a code close to the optimum, regardless of the channel error probability p. Shannon used such random coding in the proof of the fundamental theorem of channel coding [1], which asserts that 38 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

“errorless” communication is possible if, and only if, the information rate R is less than a limit referred to as the channel capacity C. The information rate is defined as R = (logM)/n, where M is the number of codewords, and the logarithms are to the base q. The capacity C depends on the channel error probability. (The definition of the information rate follows from the fact that, without redundancy, qk different k-symbol messages can be written with an alphabet of size q, so the availability of M codewords is equivalent to that of all k-symbol messages, with k = logM . Little generality is lost if we assume that k is an integer. The redundancy rate is defined as 1 − R.) For instance, the capacity of the binary symmetric channel considered above is C = 1 + plogp + (1 − p)log(1 − p), where the logarithms are to the base 2. “Errorless” means that, provided R < C, a vanishing probability of error can result from using adequate (but not explicitly specified) codes as n approaches infinity. Further elaboration of this fundamental theorem led to stronger results which, loosely speaking, tell that an arbitrarily chosen code is good with high probability. In a more adamant style: All codes are good. The problem of almost optimum error-correction coding seems, therefore, to be solved and, moreover, in an unexpectedly simple way. It seems, but it is far from being so because a formidable problem remains. Remember that implementing the regeneration rule above implies to find the codeword the closest to the received word. In the absence of any structure, a code is an arbitrary set of M n-symbol words. There is no other way for implementing the rule than to compare each of the M codewords with the single received word to be regenerated. The problem is that for useful values of the codeword length (i.e., n) that are large enough to make the probability of a regeneration error small enough, M is huge. For example, a binary code with n = 1, 000 and R =1/2 contains M = 2500 ≈ 10150 words. Implementing regeneration when an arbitrary code is used thus bumps against a complexity barrier. This problem cannot actually be solved unless the code is given some structure intended to alleviate the regeneration complexity. A large number of codes and code families having a strong mathematical structure were invented, and the literature on such error-correcting codes is plentiful (see, for instance, [14] and the impressive bibliography it contains). However, the results obtained were invariably far from the promise of the fundamental theorem of channel coding. Most experts believed that finding good codes having a tractable structure was hopeless due to an intrinsic incompatibility of goodness and structure. This widely shared opinion was summarized in the folk theorem: All codes are good, except those we can think of. It turns out that this opinion was by far too pessimistic. For instance, we noticed in 1989 that the sole criterion used in order to design a good code was to endow it with a minimum distance as large as possible. We criticized this dogma and suggested that a better criterion could be to look for randomlike codes with the distribution of distances between their words close, in some sense, to that of random codes (regardless of their actual minimum distance) but constructed according to a deterministic process [15], [16]. (Analogously, easily generated pseudorandom sequences, which mimic random sequences, are known and widely used in simulation.) Codes designed according to this criterion should have performance close to the optimum. Soon after it was proposed, in 1993, the pessimistic opinion above was definitively ruined with the advent of turbo JANUARY/FEBRUARY 2006

codes [13], [17], [18]. Turbo codes actually meet the randomlike criterion, although they were not explicitly designed to this end [19]. Their implementation is comparatively simple and well within the possibilities of current technology. Besides being the best codes presently available, turbo codes perform so close to the theoretical limit (the channel capacity) as to render them almost optimal, at least from a practical point of view. Introducing Soft Codes

It would be naïve to believe that error-correcting codes of natural origin would closely resemble those produced by human engineering. We think that they should be more flexible and versatile than man-made codes. We propose to both weaken and extend the concept of error-correcting code to better fit the specific needs of genomic error correction. Broadly speaking, there are two alternative ways for specifying an error-correcting code. First, give a construction rule that associates with any k-symbol message an n-symbol word, with n > k to provide the necessary redundancy. Second, define constraints that are exclusively satisfied by the words of the code. Again, imposing constraints restricts the code to a subset of all n-symbol words, hence providing redundancy. In whatever way a code of length n is defined, it possesses the dichotomic property that any n-symbol word belongs or does not belong to it. The codes used in engineering are generally defined by their construction rule (which is implemented in the encoding operation) from which specific constraints are easily derived and used in the decoding (regeneration) process. Both the construction rule and the constraints are expressed in deterministic mathematical terms. For extending the concept of code to genetics, we propose starting from the specification of a code by its constraints. We assume they can be expressed as incompatibilities or forbidding rules or in probabilistic terms, aside from being possibly expressed as deterministic mathematical equalities. For example, constraints can be imposed on the DNA strand by folding properties or induced by constraints on the proteins (the synthesis of which the DNA directs). In this extended meaning, the codes will be referred to as soft codes. We introduced this concept in [3] and assumed that the hypothesized genomic error-correcting codes are of this kind. We tried to somewhat refine its definition in further papers [4], [5]. With constraints expressed in probabilistic terms, the dichotomic property that a word belongs or not to a given code is lost. The main parameters of a code, like its minimum distance, then become random variables. What little we lose in precision when considering soft codes, we gain very much in flexibility and generality since any constraint which directly or indirectly affects the DNA molecule implies some error-correcting ability. These constraints result in dependency between the symbols of the words, so the knowledge of certain symbols enables reassessing the probabilities of others. Since many such genomic constraints exist, the problem is to identify the means which implement regeneration rather than to find naturally implemented encoding processes. Encoding has become implicit but the actually crucial problem is regeneration (decoding, in engineering words). The optimum regeneration rule stated above then becomes: Choose the string of nucleotides obeying the genomic constraints the closest to the one to be replicated. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

To illustrate the soft code concept, we used in [5] examples from the error-correcting technique, showing that it is relevant to the analysis of decoding processes. We shall try below to identify genomic soft codes. In order to illustrate the soft code concept, we now consider an example foreign to both engineering and molecular biology: natural languages. These languages involve strings of symbols (phonemes for the spoken language, letters of some alphabet for the written one) that are subjected to many constraints. The properties of the vocal tract severely restrict the combinations of phonemes that can be uttered, thus creating phonetic constraints (and inducing morphological constraints in the corresponding written texts). Among all the combinations of phonemes (or letters) that obey such constraints, only a small fraction are words of a given natural language. Let us refer to this constraint as lexical. The words of a language can themselves be combined according to syntactic rules specific to it, although they are possibly rooted in the human brain structure. At a still different level, meanings are associated with the words of any language and combining words according to the syntactic rules results in propositions. Correct propositions as regards these rules can be devoid of any meaning if they fail to obey semantic constraints (e.g., “the cat swept the red theorem” is both syntactically correct and meaningless). The constraints of fundamental nature due to properties of the vocal tract or the human brain, plus the conventional ones which are shared among a linguistic community, restrict the allowed strings of phonemes or letters to a very small subset of all unconstrained strings made of the same phonemes or letters. In other words, any natural language is a highly redundant soft code. But what about error-correction capabilities? A conversation is such a trivial experience that we do not wonder at its success. Indeed, it almost always results in literal understanding even in the presence of a high noise level as in a street, a car, or a plane. Moreover, even in quiet acoustical surroundings, individual phonemes are identified with a large error rate although meaningful sentences made of the very same phonemes are unambiguously understood. We may thus think of the literal understanding of a language as a decoding process. Furthermore, a language is defined by distinct constraints acting at several hierarchical levels. For instance, phonetic constraints, which are due to the structure of the vocal tract, are more fundamental than constraints specific to a given language, which are social conventions inherited from history. We shall refer below to such a structure as a system of nested soft codes. Our daily experience thus witnesses the error-correcting ability of a natural language, although the precise decoding or regeneration mechanisms involved are essentially unknown. They are implemented in the human brain but escape consciousness. Going back to DNA coding, errors resulting from substitution of a wrong nucleic base to another one should not only be considered but also those due to erasures, deletions, and insertions. We shall nevertheless limit ourselves to the substitution errors because this case has been extensively studied by engineers, although deletions and insertions are at least as important in genetics. Error-correcting codes against this type of errors can be designed with properties similar to those of codes against substitution errors but they were much less studied. Similarly, in the absence of a thorough study of soft codes, we may assume for convenience that the main JANUARY/FEBRUARY 2006

39

properties of error-correcting codes are not fundamentally altered and thus remain approximately relevant to the biological soft codes, although the main parameters which determine the performance of a conventional code, for instance, its distance distribution and especially its minimum distance, become random when transposed to a soft code. The consequences of using soft codes as error-correcting means will thus not be significantly different from those of conventional codes as discussed above. Besides being convenient, this assumption may be fairly close to reality, as a consequence of the law of large numbers, if both the code lengths considered are large and the overall code is specified by many independent constraints. Having stated the necessary basic concepts, we are now able to more precisely formulate the hypotheses regarding genomic error-correcting codes, then compare the consequences which can be derived from them with known biological facts, and even use them as predictive tools to help deciding on debated issues.

ture of the living world, namely, that in their own space, genomes are very sparse, so discrete species exist. Uniquely specifying each individual within each of these species would require several tens of nucleotides more, resulting in a genome length less than 100 base pairs. The existence of some kind of error-correcting codes in the genome, at the molecular level or mostly involving short codes, was also suggested in [20]–[23]. The interesting idea that introns are made of check symbols associated with the message borne by the exons was formulated in [24]. The search for a simple linear code described in [25] was unsuccessful, but this negative result is questionable (see below). On the other hand, biological error-correcting mechanisms foreign to the genome replication were discovered (see, for example, [26]). The role of codes in biology has been stressed in [27]. However, Barbieri’s concept of organic codes results from a deep reflection on biological facts but does not refer to the necessity of error correction. Subsidiary Hypothesis and Nested Codes

Hypotheses and Their Consequences Main Hypothesis

Our main hypothesis has been already stated: it consists of assuming the existence of error-correcting means that behave like the theoretically optimal ones, i.e., provide a regeneration error probability that decreases as the code length increases and vanishes as it tends to infinity. A necessary condition for their existence is the presence of redundancy. The number of different genomes of some given length n would be 4n in the absence of redundancy. Even for the shortest genomes, those of viruses, n is at least of several thousands, so 4n is a number so large that it defies imagination. In contrast, we may evaluate the total number of past and present species to about 109 ≈ 415 , so a genome made of 15 nucleic base pairs would suffice to specify all past and extant species. A comparison with the actual genome lengths (ranging from a few million base pairs for bacteria and up to 1 billion base pairs and more for plants and animals) shows that the actual redundancy rate is very high, so the genomes can be far apart from each other in terms of the Hamming distance. That it is actually so explains a striking fea-

I0

I1

I2

I3

Fig. 1. The fortress metaphor: A code is represented as a closed wall that protects what is inside it. I 0, I 1, I 2, and I 3 are successive information messages; I 0 is protected by three codes, I 1 by two codes, I 2 by a single code, and I 3 is left uncoded.

40 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

We must introduce a subsidiary hypothesis before proceeding further. We were led to formulate it because the assumed genomic error-correction means need to provide an unequal error-protection. If we look at the features of living beings, we see that some are conserved with an extreme faithfulness, as witnessed by the permanency of certain genes like the HOX genes, but that other features are much more variable. As a means for introducing genomic variation, sexuality has moreover been favored by evolution in most of the living species. To account for these facts, we were led to assume that the hypothesized error-correcting system consists of nested codes. Notice that a similar scheme has independently been used by Barbieri to describe the organic codes [27]. Nested codes can be more easily described in the case of conventional systematic codes C(n, k), i.e., where a k-symbol information message is encoded into a longer n-symbol word where the k-symbol message explicitly appears in a set of k defined positions. We assume that a first information message I0 of length k0 is encoded according to a code C(n0 , k0 ). Then, a second message I1 of length k1 is appended to the codeword that resulted from the first encoding, and encoding again by a code C(n1 , n0 + k1 ) is performed. This process is repeated t times. The last information message It is left uncoded. This process is depicted in Figure 1 with the fortress metaphor, where each code is depicted as a wall that encloses its encoded information message for t = 3. The component codes of a nested codes system may use different alphabets. Defining nested soft codes is more difficult since the concept of information message vanishes in this case. We may think of the nested code concept in more general terms: the ith encoding creates dependency between the results of i − 1 previous encodings, regardless of the alphabets and the codes which are used. Our above example of a natural language actually illustrates a system of nested soft codes. As an example of genomic nested codes, we may think of constraints induced on the genome by that of proteins as defining a basic soft code; in eukaryotes, constraints due to the wrapping of the DNA double strand in nucleosomes are superimposed and define a more peripheral soft code (see below). Besides assuming that a genomic error-correcting code is made of several nested codes, we furthermore assume that it was built in successive steps where the codes appeared in the order of the JANUARY/FEBRUARY 2006

Our hypothesis also sheds light on the process of biological evolution and on the structure of the living world.

layers, beginning with the innermost one. In other words, we assume that the encodings according to codes C(n0 , k0 ), C(n1 , n0 + k1 ), etc., appeared successively in the geological times or, referring again to Figure 1, that the walls were constructed successively, beginning with the innermost one. According to this viewpoint, older genetic information is better protected than more recent information. When genomic variability is needed, it should correspond to the periphery of the nested codes scheme depicted in Figure 1. As a generator of variability, sexuality should operate at this level. If (as many believe) it provides a defence against infectious agents, this variability is necessary to match the high genomic variability of viruses and bacteria. Consequences of the Hypotheses as Regards Evolution and the Living World

The main arguments to be developed now rely on the assumption of a kind of similarity between living beings and the corresponding genomes considered as codewords, especially regarding their distance properties. In other words, we assume that the genomic space to some extent provides an image of the living world as we perceive it, i.e., that of phenotypes. Although this similarity is rather fuzzy and difficult to make more precise, we notice that it is implicit in many current biological approaches where phenotypes are compared in terms of distances of the corresponding genomes, e.g., for building phyletic trees. We shall give below further arguments in favor of this similarity. In our earliest work on genomic error-correcting means [2], we believed that a relationship could exist between the genome length and the permanency of a species. It seemed that some species known for their very long genomes were also among the less variable ones (e.g., lungfishes or newts). The permanency being for optimal codes the greater the larger the codeword, we thought that the same relationship was likely to exist between the permanency of a species and the length of its genome. Implicit in this belief was the assumption of a constant redundancy rate. The recent improvements in the knowledge of genomes of many species make them appear as highly nonhomogeneous, so this relationship is questionable. The beings with the smaller genomes, especially the pathogenic agents, are actually more variable than beings with longer genomes. Going back to the assumed similarity of the “phenotypic space” and the genomic space, and moreover assuming that genomes use error-correcting means in the form of nested codes, we may adequately account for the discreteness of species and the existence of a taxonomic hierarchy. In contrast, a world of living beings with uncoded genomes would not exhibit such a hierarchy and no taxonomy would be possible. The fact that we live in a world of discrete and taxonomiIEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

cally ordered species, not in a world of chimeras, is a strong argument in favor of our hypotheses in the (hopefully provisional) absence of direct experimental proofs. Indeed, a world of chimeras has been described in [28]: that of bacteria. This does not contradict the above statements; however, since our subsidiary hypothesis of time-successive nested codes leads to identifying the degree of evolution with the number of nested code levels of the genomes, we may expect that the amount of coding is less—hence the structure of distinct species is less strong—in the (ancestral) bacteria than in complex (more recent) beings like plants and animals. A rather puzzling feature of the living world also finds a simple explanation in our hypotheses. It is the trend of evolution towards increased complexity. We may consider as an experimental fact that species having a larger genome than the previously existing ones appeared in many instances during the process of evolution. It can be interpreted as a consequence of the hypothesis that error-correcting means exist in the genome. Indeed, a longer genome is an evolutive burden as regards the speed of replication but is advantageous as enabling a more efficient error correction according to the channel coding theorem of information theory, so its net effect can be to increase the genome permanency (as defined in [3] and above) and, therefore, to provide an immediate evolutive benefit. An increased genome length does not necessarily imply an increase in complexity, but it provides room for it. More complexity in turn enables improving the evolutive fitness of phenotypes and, hence, should be favored by natural selection. Another simple consequence of our hypotheses is that evolution proceeds by jumps (i.e., is saltationist), a still debated issue. It is a straightforward consequence of the distance structure of an error-correcting code. It implies that natural selection does not act on close variants of existing beings but on mutants produced by regeneration errors, hence having a genome at a distance from the original one at least equal to the minimum distance of the code. It hints at a non-Darwinian mechanism for the origin of species, reminiscent of the “hopeful monster” hypothesized by Goldschmidt. With our subsidiary hypothesis, this distance itself depends on the code level in the assumed system of nested codes. Moreover, it accounts for the fact that evolution proceeds along phyletic trees with more frequent branchings, the lowest the level inside the nested codes system, since the probability of a regeneration error is higher the smaller the distance between genomes. Looking for Genomic Error-Correcting Codes

Searching for genomic error-correcting codes in the form of soft codes amounts to listing the several constraints that the genome obeys, each of them being a component soft code in the assumed nested codes system. We shall below consider first those which are directly associated with structural JANUARY/FEBRUARY 2006

41

constraints of DNA, then those induced in DNA by structural constraints of proteins, and finally constraints which result from the role of the genome to direct the construction of a phenotype. Before dealing with these topics, some remarks concerning the alphabets will be useful. Identifying the Alphabets

In engineering problems, the alphabet is often given as a parameter and is endowed with some a priori mathematical structure. This is not the case for the hypothesized genomic error-correcting codes where the alphabets themselves and their possible mathematical structure have to be determined. We make alphabets plural here since we consider nested soft codes, and we already noticed that their component codes can use different alphabets. An apparently obvious choice is that of the quaternary alphabet {A, T, G, C}, but with what mathematical structure is it endowed? Liebovitch et al. [25], for instance, answered this question assuming it to be the ring of integers modulo 4. This choice is arbitrary and the usual structure considered in the literature for defining a linear code is that of a Galois field. It is only when the alphabet size q is a prime that the addition rule modulo q and that of the Galois field are identical. Even with a mathematical structure more appropriate in engineering terms, such an approach is questionable as involving an arbitrary choice. The connection that the concept of soft code establishes between the physical and chemical constraints and the error-correcting properties suggests looking at alphabets having a physicochemical significance. In this respect, it is much more relevant to consider that any quaternary symbol simultaneously belongs to two independent codes over the following two binary alphabets: 1) the alphabet {R, Y}, whose symbols are the chemical structures of nucleic bases, namely, purine (two-cycle molecule, A or G) denoted R, or pyrimidine (single-cycle molecule, T or C) denoted Y and 2) the alphabet {2H, 3H}, where 2H represents the couple of complementary nucleotides A-T, which are tied together by two hydrogen bonds (H-bonds), and 3H the other couple, namely G-C, where the nucleotides are tied together by three H-bonds. The alphabet {R, Y} corresponds to nucleic bases of different physical size, while the second one, {2H, 3H}, indicates how strongly a nucleic base is tied with the complementary one. Then, Forsdyke interpreted a sequence of quaternary symbols as simultaneously bearing two independent binary codes, one over the alphabet {R, Y} and the other one over {2H, 3H} [29]. According to the second Chargaff parity rule, the first code is balanced, i.e., the two symbols R and Y have the same frequency, like almost all codes designed by human engineers. On the contrary, the code over the alphabet {2H, 3H} is not balanced since the frequency of its symbols varies from a species to another one and, for long and inhomogeneous genomes like the human one, from one region to another inside the genome. It could be interpreted as a kind of density modulation, which perhaps is read at several scales. The different number of hydrogen bonds of the two base pairs implies that this density modulation results in a variation of the bonding energy between the two DNA strands in the double helix. Other constraints are naturally expressed in terms of other alphabets. For instance, constraints induced on DNA by the structural properties of the proteins for which it “codes” are likely to involve triplets of nucleic bases, i.e., the codons of the genetic “code.” An alphabet size of 43 = 64 could be con42 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

sidered, but dealing with the synonymous codons that “code” for the same amino acid as a single symbol (resulting in a 21symbol alphabet) directly translates the constraints on the amino acids into constraints on DNA. Genes themselves can even be considered as the symbols of an alphabet [30], [31]. The successive use of alphabets of different sizes is a means for implementing nested codes, as already noted. Soft Codes Associated with Structural Constraints of DNA

The alphabet which is relevant here is more likely to be {R, Y} as introduced in the previous section, namely based on the distinction purine/pyrimidine. The alphabet {2H, 3H} may also be relevant because the ease of separating the two DNA strands is an important factor during the replication process. The experimental analysis of DNA sequences has shown they exhibit long-range dependence. First of all, their power spectral density has been found to behave as 1/ f β , asymptotically for small f, where f denotes the spatial frequency and β is a constant which depends on the species. Roughly speaking, β is smaller the higher the species is on the scale of evolution; it is very close to 1 for bacteria and significantly less for animals and plants [32]. Another study of the mutual dependence in DNA sequences only considered the binary alphabet {R, Y}. An appropriate wavelet transform was used to cancel the trend and its first derivative. The autocorrelation function of the binary string thus obtained has been shown to decrease according to a power law [33]. This implies long-range dependence at variance with, for example, Markovian processes, which exhibit an exponential decrease. Moreover, in eukaryotic DNA, the long-range dependence demonstrated has been related to structural constraints due to the packing of the double-strand DNA into nucleosomes where it is wrapped around histone molecules acting as a spool, which implies bending constraints along the two turns or so of the DNA sequence in each nucleosome [33]. The 1/ f β behavior of the spectrum and the long-range dependence of the DNA sequence restricted to the {R, Y} alphabet are, of course, compatible with each other. Moreover, they both denote (at least if further conditions are fulfilled) the existence of a fractal structure, meaning that the DNA sequence is in some sense self-similar. In other words, a basic motif is more or less faithfully repeated at any observation scale. Therefore, we may think of the message borne by the DNA strand as resulting from multiple unfaithful repetition, which could, in principle, enable the use of many low-reliability replicas of the basic motif symbols for the purpose of regeneration, in terms of which reliable decisions can be taken. This implies a very large redundancy, an obvious property of the DNA message. The existence of such a regeneration process, possibly approximated by majority voting, is as yet a conjecture. It is as yet to be determined whether, and how, nature implements regeneration based on long-range dependence at some stage of the DNA replication process [34]. One may wonder why the regeneration process does not turn this unfaithful repetition into a faithful one by correcting the ‘“wrong” symbols. We may explain why it is not necessarily so by the existence of other soft codes having independent probabilistic constraints within the assumed nested codes system. Then, the most probable symbol of the actual DNA message results from a compromise between the constraints of the several soft codes in which it is involved. JANUARY/FEBRUARY 2006

Soft Codes Induced by Structural Constraints of Proteins

Proteins are not fully described by the polypeptidic chain that the sequence of codons of a gene specifies. They owe their functional properties to their folding according to a unique pattern, which implies many chemical bonds (especially disulphur bridges) between amino acids that are separated along the polypeptidic chain but close to each other in the three-dimensional (3-D) space when the protein is properly folded. For instance, many proteins with an enzymatic function fold into a globular shape. Moreover, proteins are most often made of a number of 3-D substructures (α helices and β sheets, which are themselves included in higher-order structures named domains). These substructures impose strong geometrical, steric, and chemical constraints on the sequence of amino acids, which in turn induce constraints on the corresponding DNA. Due to the central role of genes in directing the synthesis of proteins, such constraints are present in the genome of any living being, whether it is a prokaryote or a eukaryote. Interpreting a Gene with Exons and Introns as a Kind of Systematic Codeword

exons and introns being roughly constant for a given code, introns are much less variable. These properties of eukaryotic genes are precisely those which can be expected from genes acting as systematic error-correcting codes, but the encoding and regeneration processes remain unknown. It is not even known whether the distance properties of these genes are actually used for error correction. Clearly, discovering the encoding and regeneration mechanisms at work here needs the active collaboration of biologists. A Possible Role of “Junk” DNA

Genomes (especially the human genome) often contain very short sequences (e.g., three bases long), which are repeated thousands or even millions of times. Such sequences bear almost no information. Such “junk” DNA may, however, play a role in an error-correction system as separating along the DNA strand more informative sequences which, due to the 3-D structure of the DNA molecule, may be spatially close to each other and share mechanical or chemical constraints (a function which loosely resembles that of interleaving used in the coding technique). On the other hand, the most successful encoding scheme available to engineers is that of turbo codes [17], [18], which can be interpreted as combining three main functions [36]: replication (repeating a symbol), interleaving (permuting a sequence of symbols), and rate-1 encoding (computing output symbols in terms of a sequence of input symbols), as depicted in Figure 2. Each of the blocks of this figure performs one of the three functions which may be expected from a good encoder, namely, providing redundancy, randomness, and mutual dependence, respectively. Replication is the sole function that produces redundancy. The other functions convert mere repetition into distributed redundancy, which is much more efficient regarding error correction. We may thus interpret the scheme of Figure 2 as a kind of paradigmatic encoder. The junk DNA made of a short sequence repeated many times may play the same role as an interleaver. We may think of it as separating along the DNA strand sequences which, due to the 3-D structure of the DNA molecule, are spatially close to each other and can share mechanical or chemical constraints (see Forsdyke [24]). Although the efficiency of such a separator is poor in terms of redundancy (compared with a true interleaver), we already noticed that the genomes are characterized by a very high redundancy, so genomic redundancy may be thought of as “cheap.” In engineering, on the contrary, redundancy often has a cost which limits it to moderate amounts. We notice, moreover, that it is not too difficult to imagine how such an encoder has been generated through the ages, since the separator, if we let it replace the interleaver in Figure 2, results from a sequence being repeated, which is the most basic function of DNA.

Forsdyke suggested in 1981 that introns are made of check symbols associated with the message borne by the exons [24]. The literature generally states that introns are more variable than exons, but a counterexample was provided in 1995 by Forsdyke, who experimentally found that the exons are more variable than introns in genes which “code” for snake venom [35]. It turns out that both the generally observed greater variability of introns and Forsdyke’s counterexample can be explained by the assumption that the system of exons and introns actually acts as a systematic error-correcting code where exons constitute the information message (which directs the synthesis of a protein), and introns are made of the associated check symbols. Interpreted as a regeneration error, a mutation occurs with large probability in favor of a codeword at a distance from the original word equal to the minimum distance of the code or slightly larger. If the exons “code” for a protein of physiological importance, which is the most usual case, it may be expected that only mutations with a few errors within the exons, hence having no or little incidence on the protein, will survive natural selection. The total number of errors is at least equal to the minimum distance of the code. If few errors are located in the exons, most of them must affect the introns.. The situation is completely different in the case of genes that “code” for snake venom. Rodents are the typical prey of snakes. Snakes and rodents are involved in an “arms race.” Some rodents incur mutations that provide an immunity to snake venom; the population of rodents with such mutations increases as they escape their main predators, and the snakes are threatened with starvation unless mutations in their own genes make their Interleaver Rate-1 Encoder n-Replicator venom able to kill mutated rodents [35]. The Input Output genes which “code” for snake venom are thus under high evolutive pressure, since natural Fig. 2. A schematic representation of a rate 1/n turbo encoder. The box selection favors mutated genes producing pro- labeled n-replicator represents a device which successively delivers n times teins as different as possible from the original its input symbol. The interleaver changes the order of the symbols in its input ones. In terms of the Hamming distance, much sequence, and the rate-1 encoder outputs symbols which combine a numof the difference should therefore be located in ber of its successive input symbols. The n-replicator is the sole of the devices the exons. With the total number of errors in in the scheme to generate the necessary redundancy. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

43

Soft Codes from Linguistic Constraints

Biology and Engineering: A Needed Collaboration

We stressed above the contrast between the comparative brevity of the message which is needed for unambiguously identifying a biological species (and even an individual inside it) on the one hand, and the length of actual genomes on the other hand. This contrast has rather obvious reasons since the genome role is by no means restricted to identify a living being: biology interprets it as a blueprint for its construction. The genome of any living being actually contains the recipe for its development and its maintenance. Besides those parts of the genome which direct the synthesis of proteins, i.e., the genes in a restricted sense, and the associated regulatory sequences which switch on or off their expression (i.e., make the gene direct or not direct the synthesis of the protein it specifies), the genome must somehow describe the succession of operations which results in the development and the maintenance of its phenotype. This demands some kind of language. Biologists do not yet know it although some of them claim in newspapers that they “decipher” or “decrypt” genomes. In a sense, many of them deny its existence when they dub “junk DNA” every part of the DNA outside the genes and their regulatory sequences: they declare useless what they do not understand. But, on the other hand, they consistently use the metaphor of a written text to explain the role of the genetic message, at least in popular science books like [11] and many others. This metaphor is quite convincing, but its consequences in terms of genome conservation are overlooked. Indeed, any language involves many lexical, syntactic, and semantic constraints that may be interpreted as soft codes having error-correcting abilities (as we argued above for human languages). Moreover, they appear at several different levels and thus assume the structure of nested soft codes, which we were led to hypothesize for the genetic message. Of course, it remains to understand how these error-correcting abilities are exploited. Current researchers already use tools of formal linguistics (which shares the concept of dependence with information and coding theory) in order to describe the genomes and proteins [37], [38] but ignore the error-correction problem. The connection just outlined between linguistics and error-correcting ability implies that a longer genome is not only useful to decrease the error probability but also provides room for more semantics and, therefore, enables specifying more complex beings. An important and useful tenet of information theory is the separation between information and semantics. However, the hypothesized error-correction mechanisms based on linguistic constraints heavily rely on the genome being a blueprint for the construction and maintenance of a phenotype, so one could consider the error-correction ability of the genetic message as, at least partially, a by-product of its semantics. But this is only a facet of the question. One can equally well argue that this correction ability is its main feature, since without it no transmission of hereditary characters would be possible and life could not have developed. Then, the construction and maintenance of phenotypes would be a mere projection in the physicochemical world of the abstract properties of the genetic message that enable error correction. This is a hen-and-egg problem, as often met in biology. Interestingly, the similarity of the phenotypic and genomic spaces we were led to assume above may have its roots in this relationship.

Nature obviously appears as an engineer of very broad competence, and its achievements are outstanding. Therefore, human engineers should be deeply interested in the products of nature’s engineering, i.e., living things. Similarly, understanding the engineering aspects of life should be a major concern for biologists. However, the methods used by nature on the one hand, and human engineers on the other hand, exhibit a sharp contrast which may explain why biologists and engineers do not more closely collaborate. At variance with human engineers, nature does not use purposeful design but “tinkering,” exhaustive search and natural selection. It ignores time limitation. Continuity of life is its sole (but very difficult) major constraint. There is also a broad difference between nature and engineers as regards spatial and temporal scales. Engineers design and build objects of large physical size within a short time, and these objects have short lifetimes. The most basic properties of living things depend on objects at the molecular scale, especially the genome and the cell replication machinery, and the time scale of nature extends to that of geology, i.e., up to billions of years. Having genuine self-repair capabilities, living beings are moreover much more flexible and resistant to degradation than the products of human engineering, and nature’s achievements often outperform what human engineers can do. It turns out, moreover, that they are understood in almost any case only insofar as human engineers invented similar solutions to problems that nature solved eons ago. That the methods of nature and engineers are so markedly different is perhaps why we can learn so much from nature. Clearly, exaustive search is not a good method for purposely designing an object within some prescribed short time, but it guarantees the absence of any bias. In constrast, no human engineer can claim to be completely free from prejudice. The main distinctive features of living beings are their extreme complexity, which is unmatched in the nonliving world, and (not independently) the fact that, besides matter and energy, they receive and transmit information and heavily rely on its transfer and conservation for their construction and maintenance. This last point also has no equivalent outside the living world and appears as the specific mark which radically differenciates it from the nonliving world. It makes biology especially relevant to information theory, thus prompting biologists to use information theory as a main tool and challenging information engineers to get interested in biology.

44 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Conclusions

The question of how genetic information is faithfully communicated clearly needs to be answered. Dealing with the genome as if it were a permanent object, like those of our daily lives at our time scale, is not tenable. Information theory and the experience gained by engineers for designing and implementing error-correcting codes will help to answer this question properly. The above speculations were intended to this aim but could only rely on published biological works. Many works on the genome were aimed at understanding how it directs the construction of a phenotype but, unfortunately, fewer were devoted to the way it replicates itself. It may be rather futile, however, to question how the genome produces a phenotype if we do not first understand how the genome produces a genome. JANUARY/FEBRUARY 2006

If genetics eventually gets an information-theoretic education, there is little doubt that unexpected error-correcting means will be discovered and that our understanding of evolution, and, therefore, of the living world, will be deeply improved. The speculations presented here are but provisional steps in this direction. We may safely predict that, in this field as in others, nature will reveal itself more inventive and efficient than human engineers. To quote Jerome Wiesner, “No one is visionary enough to match reality.” The extreme importance of information in the living world even suggests that getting an information-theoretic education should be widely beneficial to biology as a whole. Gérard Battail graduated from the Faculté des Sciences (1954) and Ecole Nationale Supérieure des Télécommunications (ENST) in 1956, both in Paris, France. He joined the Centre National d’Etudes des Télécommunications (CNET) in 1959. He worked there on modulation systems and especially on frequency modulation, using fundamental concepts of information theory to understand its behavior in the presence of noise, namely, the threshold effect. In 1966, he joined the Compagnie Française Thomson-Houston (later called Thomson-CSF), where he acted as a scientific advisor to technical teams designing radioelectric devices. There he interpreted channel coding as a diversity system for designing decoders, especially soft-input ones. He also worked on source coding, frequency synthesizers, mobile communication, and other problems related to the design of industrial radiocommunication devices. In 1973, he joined ENST as a professor. He taught modulation, information theory, and coding there. He also had research activities in the same fields with a special emphasis on adaptive algorithms regarding source coding and, for channel coding, on soft-in, softoutput decoding of product and concatenated codes. He was led to criticize the conventional criterion of maximizing the minimum distance of a code and instead proposed a criterion of closeness of the distance distribution with respect to that of random coding. Some of these ideas are at the root of the invention of turbo codes. After his retirement in 1997, he started working on applications of information theory to the sciences of nature. He has especially investigated the role of information theory and error-correcting codes in genetics and biological evolution. Battail has applied for many patents, written many papers, and participated in many symposia and workshops. He also authored a textbook on information theory published by Masson in 1997. He is a member of the Société de l’Electricité, de l’Electronique et des Technologies de l’Information et de la Communication (SEE) and of the IEEE. Before his retirement, he was a member of the editorial board of the Annales des Télécommunications. From 1990–1997, he was the French official member of Commission C of URSI (International Radio-Scientific Union). From June 2001–May 2004, he served as associate editor at large of IEEE Transactions on Information Theory. Address for Correspondence: Gérard Battail, la Chanatte, le Guimand, F-26120 Chabeuil, France. E-mail: [email protected]. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

References [1] C.E. Shannon, “A mathematical theory of communication,” BSTJ, vol. 27, pp. 379–457, pp. 623–656, 1948. [2] G. Battail, “Does information theory explain biological evolution?,” Europhys. Lett., vol. 40, no. 3, pp. 343–348, Nov.1997. [3] G. Battail, “Is biological evolution relevant to information theory and coding?,” in Proc. ISCTA ‘01, Ambleside, UK, 2001, pp. 343–351. [4] G. Battail, “An engineer’s view on genetic information and biological evolution,” Biosystems, vol. 76, no. 1–3, pp. 279–290, 2004. [5] G. Battail, “Can we explain the faithful communication of genetic information?,” presented at the DIMACS working group on theoretical advances in information recording, Mar. 22–24, 2004. [6] O. Avery, M. McCarty, and C. MacLeod, “Studies of the chemical nature of the substance inducing the transformation of pneumococcal types. Induction of transformation by a desoxyribonucleic acid fraction isolated from Pneumococcus Type III,” J. Exp. Med., vol. 79, pp. 137–158, 1944. [7] R.E. Franklin and R.G. Gosling, “Molecular configuration in sodium thymonucleate,” Nature, vol. 171, no. 4356, pp. 740–741, 25 Apr. 1953. [8] J.D. Watson and F.H.C. Crick, “Molecular structure of nucleic acids,” Nature, vol. 171, no. 4356, pp. 737–738, Apr. 25, 1953. [9] E. Chargaff, “How genetics got a chemical education,” Ann. New York Acad. of Sci., vol. 325, pp. 345–360, 1979. [10] R. Dawkins, The Selfish Gene. Oxford, UK: Oxford Univ. Press, 1976. [11] R. Dawkins, The Blind Watchmaker. Harlow: Longman, 1986. [12] D.R. Forsdyke, “Selective pressures that decrease synonymous mutations in Plasmodium falciparum,” Trends in Parasitology, vol. 18, pp. 411–418, 2002. [13] E. Guizzo, “Closing in on the perfect code,” IEEE Spectr., vol. 41, no. 3 (INT), pp. 28–34, Mar. 2004. [14] F.J. MacWilliams and N.J.A. Sloane, The Theory of Error-Correcting Codes. Amsterdam: North Holland, 1977. [15] G. Battail, “Construction explicite de bons codes longs,” Annales Télécommunic., vol. 44, no. 7–8, pp. 392–404, July-Aug. 1989. [16] G. Battail, On Random-like Codes, Lecture Notes in Computer Science No. 1133. New York: Springer, 1996, pp. 76–94. [17] C. Berrou, A. Glavieux, and P. Thitimajshima, “Near Shannon limit error-correcting coding and decoding: Turbo-codes,” in Proc. ICC’93, Geneva, Switzerland, May 1993, pp. 1064–1070. [18] C. Berrou and A. Glavieux, “Near optimum error correcting coding and decoding: Turbo codes,” IEEE Trans. Commun., vol. 44, pp. 1261–1271, Oct. 1996. [19] G. Battail, C. Berrou and A. Glavieux, “Pseudo-random recursive convolutional coding for near-capacity performance,” in Proc. GLOBECOM’93, Commun. Theory Mini-Conf., Houston, TX, 1993, vol. 4, pp. 23–27. [20] G. Cullmann and J.-M. Labouygues, “The logic of the genetic code,” Biosystems, vol. 16, pp. 9–29, 1983. [21] J. Rzeszowska-Wolny, “Is genetic code error-correcting?,” J. Theor. Biol., vol. 104, pp. 701–702, 1983. [22] H.P. Yockey, Information Theory and Molecular Biology. Cambridge, UK: Cambridge University Press, 1992. [23] D.A. Mac Dónaill, “A parity code interpretation of nucleotide alphabet composition,” Chem. Commun., vol. 18, pp. 2062–2063, 2002. [24] D.R. Forsdyke, “Are introns in-series error-detecting sequences?,” J. Theor. Biol., vol. 93, pp. 861–866, 1981. [25] L.S. Liebovitch, Y. Tao, A.T. Todorov, and L. Levine, “Is there an error correcting code in the base sequence in DNA?,” Biophys. J., vol. 71, pp. 1539–1544, 1996. [26] E.E. May, M.A. Vouk, D.L. Bitzer, and D.I. Rosnick, “Coding theory based models for protein translation initiation in prokaryotic organisms,” Biosystems, vol. 76, no. 1–3, pp. 249–260, 2004. [27] M. Barbieri, The Organic Codes. Cambridge, UK: Cambridge Univ. Press, 2003. [28] L. Margulis and D. Sagan, Microcosmos, Four Billion Years of Evolution from Our Microbial Ancestors. New York: Summit Books, 1986. [29] D.R. Forsdyke home page [Online]. Available: http://post.queensu.ca/forsdyke/ [30] S.A. Kauffman, The Origins of Order. New York: Oxford Univ. Press, 1993. [31] O. Milenkovic, “The information processing mechanism of DNA and efficient DNA storage,” presented at DIMACS working group on theoretical advances in information recording, Mar. 22–24, 2004. [32] R.F. Voss, “Evolution of long-range fractal correlation and 1/f noise in DNA base sequences,” Phys. Rev. Lett., vol. 68, pp. 3805–3808, June 1992. [33] B. Audit, C. Vaillant, A. Arneodo, Y. d’Aubenton-Carafa, and C. Thermes, “Long-range correlation between DNA bending sites: relation to the structure and dynamics of nucleosomes,” J. Mol. Biol., vol. 316, pp. 903–918, 2002. [34] G. Battail, “Replication decoding revisited,” in Proc. Information Theory Workshop 2003, Paris, France, pp. 1–5. [35] D.R. Forsdyke, “Conservation of stem-loop potential in introns of snake venom phospholipase A2 genes. An application of FORS-D analysis,” Mol. Biol. Evol., vol. 12, pp. 1157–1165, 1995. [36] J.J. Boutros, “Asymptotic behavior study of irregular turbo codes,” in Proc. DSP’2001, Sesimbra, Portugal, Oct. 2001. [37] R.N. Mantegna, S.V. Buldyrev, A.L. Goldberger, S. Havlin, C.-K. Peng, S. Simons, and H.E. Stanley, “Linguistic features of noncoding DNA sequences,” Phys. Rev. Lett.., vol. 73, pp. 3169–3172, 1994. [38] D.B. Searls, “The language of genes,” Nature, vol. 420, no. 6912, pp. 211–217, Nov. 2002.

JANUARY/FEBRUARY 2006

45

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

The Quest for Error Correction in Biology Recent Developments in Codes and Biology ©EYEWIRE

BY MANISH K. GUPTA

he early excitement of application of coding and information theory to biology could not continue further due to several reasons like underdevelopment of both the fields. Now after 50 years, our understanding of biology is increasing day by day due to genomic research. The goal of this article is to give a brief account of the recent developments in codes and biology. In particular, we focus on the existence of error correction in biology. Computing and communications are two broad areas where information theory has direct impact. The field of information theory begins with the work of Shannon in 1948, and since then, it has found applications and interactions in various fields including biology and chemistry [1]. The early excitement about its applications to biology could not continue further as the following observation was made in 1956 in a conference organized by Yockey:

T

Information Theory is very strong on the negative side, i.e., in demonstrating what cannot be done; on the positive side its application to the study of living things has not produced many results so far; it has not yet led to the discovery of new facts, nor has its application to known facts been tested in critical experiments. To date, a definitive and valid judgment of the value of information theory in biology is not possible. [2] This conclusion seems to be obvious because it was given only three years after the discovery of DNA structure and after nine years of the discovery of Shannon’s information theory. Both the fields were quite young, and it was ahead of time when people were mixing the two. Yockey also wrote a book on this subject [3]. In another development on the counterpart field of information theory, that is, coding theory, there was work motivated by the discovery of the genetic code [4]. Several people including Golomb worked on comma-free codes [4]. At the threshold of a new century, our understanding of biology is now increasing day by day, and with the advancement of sequencing technology, we have the whole human genome stretched out before us. We now have with us the periodic table of biology. There is a need to look again with the new information in our hands for 46 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

the applications of these communications technologies (coding and information theory) to biology. We are fortunate that we can safely divide the field into several subfields. Currently, the discipline can be divided roughly into four major directions: 1) applications of information theory to biology 2) existence of error correction in biological information processing 3) applications of coding theory to biomolecular computing (e.g., DNA computing) 4) applications of coding theory to computational molecular biology and bioinformatics. Of course there is some overlap in these fields. In this article, we focus on second development and provide a brief account of the work. The first idea of possible connections between them (more precisely, the following question: Is there an error correction in the DNA sequence?) arose in the author’s mind during his Ph.D. studies around 1998. After searching the literature, this author became aware of the work of Liebovitch et al. [5]. They could not find simple error-correcting codes in the base sequence of DNA. They were searching for simple error-correcting codes. Clearly, their approach was very basic, and it is unlikely to work. From their work, this author came to know the work of Forsdyke (1981) [6] and Rzeszowska-Wolny (1983) [7]. The first paper looked at error detection in intron sequences, and the second paper looked at the question of error correction in the genetic code. These papers are earlier attempts. There is a mixed response about the error correction in DNA sequences. On one side, there are biologists, like Forsdyke [8], who are optimistic about it and, on the other hand, biophysicists, like Patel [9], who are sure that there is no error correction. Finally in August 2002, a parity code (error-detecting code) was discovered in the nucleotide alphabet by Mac Dónaill [10]. This author also became aware that most of the coding theorists are unaware about this area partly because most of these papers are in biology or chemistry journals. Schneider, together with his colleagues, has tried to develop a theory of molecular machines and molecular information theory (see [11]–[15] and the references therein) in recent years. In fact, more recently, Toby Berger of Cornell University gave a Shannon lecture at the IEEE International Symposium on Information Theory 2002 0739-5175/04/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

in Switzerland on “Living Information Theory.” He describes a mathematical model of neural coalition [16]. Finally, we should mention the following words of von Neumann: It is easy to note that the number of nerve actuation’s which occur in a normal lifetime must be of the order of 1020 . Obviously, during this chain of events, there never occurs a malfunction which cannot be corrected by the organism itself, without any significant outside intervention. The system must, therefore, contain the necessary arrangements to diagnose errors as they occur, to readjust the organism so as to minimize the effects of errors. [17] Brief History and Preliminaries

To make the article self-contained, we include a brief summary of molecular biology, information theory, and coding theory. We start with some fundamental questions. Erwin Schrodinger [18] wrote a book, What Is Life?, in 1944. He was a physicist writing about life before the discovery of DNA structure. After 60 years, we have now another book written by a computer scientist Eric Baum [19] What Is Thought? He tried to explain thought process on the basis of computation. One can ask another question: What is the difference between life and matter? In other words, we can ask what the smallest unit of life is. Perhaps it is a cell or maybe a virus. Is it information? See the review of Casti [20]. Gitt tried to explain even the origin of life in his book In the Beginning Was Information [21]. It is worthwhile to mention that there is a spin-glass model of the origin of life [22], and, surprisingly, spin-glass has a naive connection with coding theory [23]. Ising spin-glasses are just a collection of N particles with spin ±1. In the problem of magnetism, the Ising spin represents whether the microscopic magnetic moment is pointing up or down. The energy of a spin glass depends on the values of the spins and the strengths of the interactions among the particles. The energy of the whole system for a specific configuration is given by the Hamiltonian (see [24] for details). A lot of work has been done on spin glass and coding theory, and, remarkably, this gives Shannon capacity achieving codes. An analogy between the concepts in coding theory and spin glasses is shown below (see Table 1) [24]. For a connection between spin glass and biology see [25]. It appears that nature is already using optimal coding techniques, if any. One has to be very careful while trying to generalize Shannon’s idea of information theory to biology. Shannon’s information theory deals with point-to-point communication, and it is also like interorganism communication. To understand various biological communication systems and biological computing, we need to answer several questions. We have to answer first what information is [26]. Some related work between information theory and biology can be found in [27]–[35]. There are some comments about the role of information theory in biology by Shannon himself and Peter Elias (see [16]). In fact, von Neumann tried to develop the mathematical foundations of biology during last stages of his life [36]. The idea of von Neumann about genetic information is discussed by Chaitin at greater length in [37]. It is also worthwhile to mention the work of Bennett about the biosynthesis of messenger RNA as an example of reversible computation [38].

Communication, now known as information theory. Information theory is about sending the information from here to there (transmission) and sending information from now to then (storage). Information theory sets bounds on what can be done or what cannot be done, but it never tells you how to do that. A constructive counterpart to Shannon’s theory is algebraic and combinatorial coding theory, which tells you how to do it. The father of coding theory is Richard W. Hamming, who was also at Bell Labs at the time of the birth of Shannon’s information theory. It was out of a frustration of errors made by that era’s computers that Hamming created his binary codes for correcting single errors in computers of that time. We should mention that the Hamming codes were known to Fisher [40], [41] in a different context. He discovered binary Simplex codes (dual of Hamming codes) in 1942 and later generalized them to prime powers in 1945. There is an old history for binary codes as well. It appears that the first five-letter binary code was discovered by Francis Bacon in 1605 in advancement of learning called Omnia per Omnia. This was the time when there were only 24 letters in English alphabet. Early in the 19th century in France, Joseph Marie Jacquard designed the first binary-coded punched cards for operating looms. George Boole gave the algebra of propositional calculus that forms the basis of the modern design of computer logic. A French engineer, Emile Baudot, discovered a binary cyclic-permuted code (now, often called Gray code because it was patented by Frank Gray on 17 March 1953 [42]). Gray code represented a major advancement in telegraphy and has various other applications. We will see later how Gray code is connected to molecular biology. Readers who are unfamiliar with the next 50 years of information theory and coding theory can see a commemorative issue of IEEE Transactions on Information Theory published in 1998 [1], [43]. It took almost 50 years to achieve Shannon capacity by iterative decoding procedures, low-density parity check codes, and turbo codes. Coding and information theory is now facing new challenges in wireless communications, multiple-input, multiple-output (MIMO) communication systems, and networking. Is it the right time to ask about biological coding theory, biological information theory, living coding theory, or living information theory? Molecular Biology

This section is based on a book written by Lander and Waterman [44]. Molecular biology grew out of two complementary experimental approaches to studying biological function: genetics and biochemistry. Genetics can be traced Table 1. Analogy between coding theory and spin glasses. Coding Theory

Spin Glasses

Error-correcting code

Spin Hamiltonian

Signal to noise

J02
J 2

Maximum likelihood

Find a ground state

decoding Error probability per bit

Ground state magnetization

Sequence of most

Magnetization at

probable symbols

temperature T = 1

Coding and Information Theory

Convolutional codes

One-dimensional spin-glasses

In 1948, Shannon [39] set the ground work for today’s Information Technology. He gave A Mathematical Theory of

Viterbi decoding

Transfer matrix algorithm

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

47

back to Gregor Mendel, whose experiments on peas generated much interest in 1865 and showed the existence of genes in a mathematical way in living organisms. Fisher analyzed Mendel’s data many years later and concluded that they fit statistical expectation very well [45]. Biochemistry deals with fractionating the molecules in a living organism with a goal of purifying and characterizing the chemical components responsible for carrying out a particular function. It was found that living organisms are composed of carbon, hydrogen, oxygen, and nitrogen. They also contain small amounts of other elements such as sodium, potassium, magnesium, sulfur, manganese, and selenium. These elements are combined in a vast array of complex macromolecules that can be classified into a number of major types: proteins, nucleic acids, lipids (fat), and carbohydrates (starch and sugar). Proteins are molecular miracles made of amino acids. Proteins have the most diverse range of functions (examples include enzymes, which catalyze chemical reactions such as the digestion of food: structure molecules, which make up hair, skin, and cell walls: transporters of substances, such as hemoglobin, which carries oxygen in blood: transporters of information, such as receptors in the surface of cells and insulin and other hormones). Most of the functions of the cell are done by proteins. There are 20 distinct amino acids, each with its own chemical properties. Each protein is defined by its unique sequence of amino acids. There are about 100,000 distinct proteins in the human body. The amino acid sequence of a protein causes it to fold into a particular three-dimensional shape having the lowest energy. This gives a protein its specific biochemical properties, i.e., its function. Predicting the structure of a protein is an extremely challenging problem in mathematical optimization. Readers who want to learn more about molecular biology and biochemistry are referred to the excellent books [44], [46], and [47]. Some other references about biological information processing are [48]–[50]. Existence of Coding Theory in Molecular Biology

This is probably one of the most difficult areas to understand among the previous defined fields. Mojzsis et al. [51] have mentioned that coding and information theory has been in place in biology for at least 3.85 billions years. A very recent connection has been established by Yockey between the origin of life on earth and Shannon’s theory of communication and evolution [52], [53]. One can look for the possible error-correction/detection schemes in living organisms at places where some sort of information processing is going on. Living organisms process the information at various places [54]–[56], although the processing of information and the corresponding Table 2. Biological, functional, computational analogy (from [58]). Living organisms

Task

Computers

Signals from environment Input

Data

Sense organs

High level

Preprocessor

Nervous system + brain

Translation

Operating system + compiler

Electrochemical signals

Low level

Machine code

Proteins

Execution

Electrical signals

DNA

Program

Programmer

48 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

encoding could be quite different [54], [16]. This information processing could be both classical and quantum. We need to find out at what place what type of information processing is going on. Our experience suggests that we should look for a Turing machine first. A good starting place is a series of papers written by Patel [57], [58], [54]. He speculates neatly about the biological information processing and the structure of information encoding [54]. He also gave an analogy between living organisms and computers (see Table 2). This could be a good starting point for us. At some places, living systems process information without coding [59]. While searching for error correction in living organisms, we need to understand error correction very well. In [60], von Neumann proposes to view error “not as an extraneous and misdirected or misdirecting accident, but as an essential part of the process under consideration.” We also need to understand how the information is encoded in living organisms (for example, as a concentration of chemicals etc). In [59], Berger expresses his ideas about coding theory in biology as follows: Given that DNA is a long “block” code in a finite alphabet with some redundancy, there would seem to be the potential for coding there, and as I also point out such coding might be more suitable to a high-latency, interorganism communication such as takes place during mating and gestation. But in a brief discussion with the renowned Francis Crick of Crick & Watson fame about three years ago, he said he was unaware at that time of any solid evidence to that effect. Also in my Shannon lecture write up, I suggest in a long footnote that there may be some form of space-time coding taking place with the emphasis heavily on space. The reason why coding over time is of dubious value is that one must keep firmly in mind that latency is crucial in many perception problems; there is no a prior determined time at which a decision will be taken. Rather, the organism must have a (suboptimal) decision ready at all times based on what’s been processed so far. This need to be “greedy” speaks against the employment of long block and convolutional codes in the case of sensory perception/pattern recognition, too. However, there are now iterative decoders of the turbo code type for certain simple convolutional code and other code families (MDPC families, e.g.) with relatively short constraint lengths which have the advantage that a turbo (i.e., iterative, effectively maximum-likelihood-seeking) decoder (if there can be said to be a decoder in the brain and anyone can locate where) would be capable of producing a suboptimum decision at a moment’s notice. Moreover, this suboptimal decision would become increasingly close to optimum over time if it does turn out that the situation allows for sufficient time prior to decision making. In the following sections, we will consider two of the important connections: one with genetic code and Gray code and the other with the Mac Dónaill code found in purinepyrimidine and hydrogen donor-acceptor patterns in a nucleotide base. The section on codes and quantum biology considers the topological quantum error correction proposed by Porter [61] in microtubule and a suggestion of Patel about Grover’s quantum search algorithm and genetic code [57]. JANUARY/FEBRUARY 2006

The Central Dogma of Molecular Biology

The central dogma of molecular biology was suggested by Crick [62], [63]. It suggests that information flows from DNA to mRNA and to protein (see Figure 1). It was observed by Yockey [53] that this is the property of genetic code. His arguments are based on Shannon’s information theory and the fact that entropy of the DNA sequence is log2 (64) = 6 and the entropy of the protein sequence is log2 (20). A model of central dogma as a communication system has been studied by Yockey (in particular, for a model of the channel see [3, p. 111]). There have been some other attempts by Battail [64], Eigen [65], May [66], and Roman-Roldan et al. [67], viewing it as a communication channel. Some recent views are in [68]. Hopfield [69]–[71] in a series of papers writes about error correction. For more of his work, see the book by MacKay [72]. May, together with her colleagues, has written several papers recently on modeling the process of protein synthesis as a coding theory problem in prokaryotic organisms [73], [74]. The principle hypothesis in her model is: If mRNA is viewed as a noisy encoded signal to interpret the genetic translation, it is feasible to use principles of error-control coding theory in initiation mechanism. Ribosome is viewed as an error control decoder. She has tried even convolution code models [75] and iterative methods [76]. These models have been applied to the Escherichia coli K-12. Clearly these models are a good starting point, but a lot of work is needed [77]. Recently, Rosen and Moore have used finite field framework to find redundant coding structure in DNA [78], [79] and Battail examines the possible existence of soft codes, nested codes, and turbo codes [80], [81]. The work of Battail is related to the more general one of Barbieri regarding the existence of biological codes at different information levels [82]. Gray Code and Genetic Code

Gray code is a binary linear code such that if you write all the code words as an array, then each codeword differs from the next codeword by a Hamming distance of one. For example, at length 2, the set C = {00, 01, 11, 10} is a Gray code of length 2. This particular code was used as a mapping from the ring of integers modulo 4, i.e., Z4 → (Z2 )2 and is a unique map that gives a new definition to binary nonlinear Kerdock and Preparata codes and solves the mystery about their duality [83]. Various other applications of Gray codes are known. There is a rich history of the genetic code [3], [84], [4]. In fact, an interesting and short story about its invention is given in [4]. Genetic code is at the heart of the famous central dogma of molecular biology. Genetic code contains instructions to make protein. DNA stores information in an alphabet of size 4 viz {A, C, G, T}. Each letter represents nucleotide bases adenine (A), cytosine (C), guanine (G) and thymine (T). When a cell decides to make a protein, it reads the portion of DNA that codes for that protein. The portion of the DNA that codes for a particular protein is IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

COOH3N+ C H CH3

Alanine A COO+ H3N C H CH2 CH2 S CH3 Methionine M COOH3N+ C H HC-OH CH3 Threonine T COOH3N+ C H CH2 C O OAspartic Acid D

called the gene for that protein. The reading of the gene is done by a special molecule called RNA polymerase (yet another protein), which then produces a transcript of the DNA sequence in the form of a strand of messenger RNA. RNA is ribonucleic acid, a substance that is like DNA in that it is made up of four nucleotides, which in turn are made of organic bases and ribose phosphate. But the ribose in RNA has a particular oxygen atom where the ribose in DNA does not. mRNA has an alphabet of size 4, {A, C, G, U}, similar to DNA but uracil (U) is in place of thymine (T). A polymer is a molecule that is built up as a chain of smaller molecules, or monomers. Proteins are polymers with the monomers as amino acids. (See Figure 2 for a list of all 20 amino acids). Thus, proteins have an alphabet of size 20, {All 20 amino acids}. The genetic code is a mapping from mRNA to amino acids (see Table 3). Each amino acid is made of three nucleotides called a triplet or codon. The process of creating an mRNA from DNA is called transcription, and the process of making protein from mRNA is called translation. By assigning attributes of 0 or 1, one can get a unique binary vector of length 6 from each amino acid as follows (see [85] and [86] and the references therein). According to chemical type and hydrogen binding, each base in DNA (mRNA) can be categorized. Thus we assign A → 00, G → 01, C → 11, and U → 10.

DNA

mRNA Transcription

Protein Translation

Fig. 1. Genetic information flow.

COOCOOH3N+ C H H3N+ C H CH2 H3C+ CH CH CH2 H3C CH3 CH3 Valine Leucine Isoleucine V L I COO COO COOH3N+ C H H3N+ C H H3N+ C H CH2 CH2 H C Glycine N CH G H Phenylalanine Tryptophan COOF W + H3N C H COO COO CH2 H3N+ C H H3N+ C H CH2 CH2 CH2 SH C C O NH2 O NH2 Cysteine Asparagine N Glutamine Q C COOCOOCOOH3N+ C H H3N+ C H H3N+ C H CH2 CH2 CH2 CH2 CH2 CH2 C CH2 CH2 O OCH2 NH + Glutamic NH3 + C Acid 2NH NH2 E Lysine Arginine K R COOH3N+ C H CH H3C CH3

COOHN C H CH2 2HC CH2 Proline P COOH3N+ C H CH2 OH Serine S COOH3N+ C H CH2

OH Tytosine Y COOH3N+ C H CH2 HC C + HN NH C H Histidine H

Fig. 2. A list of all 20 amino acids’ molecular structures.

JANUARY/FEBRUARY 2006

49

The first attribute is chemical character and the second attribute is the hydrogen-bond character. For example, A and G fall into same chemical type purines, whereas G and C fall into different chemical types (G falls into purines and C falls into pyrimidines). Similarly, A and U have weak hydrogen bonds, and G and C have strong hydrogen bonds. If we apply these maps to our genetic code codons, we will get a unique binary vector of length 6 for each amino acid, and eventually we get a Gray code representation of genetic code; see Figure 3. Note that all of the 64 codons are at the vertices in the figure, while some codons represent the same amino acid. One can associate a number of different Gray codes, depending upon the order of importance of the bits in a codeword [85]. Obviously, the next job is to check how good this representation of genetic code is from a coding theory point of view. This still has to be investigated in the sense of a Gray map. A lot has been said about the symmetry of the genetic code (for example, [3], [87]). Recently, Gonzalez [88] gave another mathematical description of the genetic code, describing the theoretical possibility of parity coding along the sequences of DNA.

plementary pair depending upon the hydrogen bonds between them, for example, G and C form a complementary pair. One can represent the donor-acceptor pattern of each nucleotide as a string of three bits. For example, if a donor is (arbitrarily) represented as 1 and an acceptor as 0, the pattern 100 would encode C and 011 would encode G. Further, if a purine is represented by 0 and a pyrimidine by 1, the full codeword for C would be 100, 1 and for G would be 011, 0. Thus, we can assign the following: C 1001 G 0110 A 1010 T 0101. In other words, nucleotides may be depicted as positions on a hypercube, represented by a cube within a cube. The position of a nucleotide is determined by its donor/acceptor pattern, while the purine/pyrimidine nature determines whether it belongs on the inner cube (pyrimidines) or outer cube (purines). Codes and Quantum Biology

Mac Dónaill Code

Mac Dónaill of Dublin College, Ireland, discovered in August 2002 a binary (4, 4, 2) even parity code in the nucleotide alphabet by assigning a binary vector of length 4 to each base. This assignment was based on hydrogen donor-acceptor patterns found in nucleotides [10], [89]. This work was motivated by Yockey’s arbitrary assignment of a binary vector of length 5 to each base and the work of Szathmary [90]. Here we describe it in detail. We know that nucleotide bases can be classified according to their rings: purines, with two rings; R:(A, G), and pyrimidines, with one ring; Y:(C, T/U). In a DNA molecule, we only have C and T in pyrimidines. These bases form a com-

Table 3. Genetic code (from [44]). 5
END

U

C

A

G

U

C

A

G

3
END

Phe

Ser

Tyr

Cys

U

Phe

Ser

Tyr

Cys

C

Leu

Ser

Stop

Stop

A

Leu

Ser

Stop

Trp

G

Leu

Pro

His

Arg

U

Leu

Pro

His

Arg

C

Leu

Pro

Gln

Arg

A

Leu

Pro

Gln

Arg

G

Ile

Thr

Asn

Ser

U

Ile

Thr

Asn

Ser

C

Ile

Thr

Lys

Arg

A

Met

Thr

Lys

Arg

G

Val

Ala

Asp

Gly

U

Val

Ala

Asp

Gly

C

Val

Ala

Glu

Gly

A

Val

Ala

Glu

Gly

G

50 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Microtubules and Codes

Microtubules (MTs) [91], [92] are cylindrical polymers of the protein tublin and are 25 nanometers in diameter (see Figure 4). In fact, much of the cell cytoskeleton is made of microtubule (MT). They self assemble to determine cell shape and function. It is like a strong tunnel that links distant parts of the cell to each other. Tubulin by virtue of its tertiary structure likes to polymerize and form strong tunnels. Each tubulin is a peanutshaped 8-nm dimer consisting of α and β monomers. The tubulin dimer within MTs is arranged in a skew hexagonal lattice, which is slightly twisted. Each tubulin has an electric dipole moment due to an asymmetric charge distribution. Thus, MT is a lattice of oriented dipoles that can be in different phases including spin-glass phase. It has been proposed that MTs can store both classical and quantum information and actually do the computation. Tubulin work as a cellular automaton in the walls of MTs. Tubulin can exist in two conformations

UAC UAG

AAG UGU

UUG

UAA

AAC UGC

UUC

UAU

UUA

AUC

AAU

AAA

UUU

UCC

AUG UCU

AUU

AUA

UCG UCA GUC GUU GAU AGC AGU ACC ACU GAG GAA GUG GUA ACG ACA AGG AGA CAC CAU CUC CUU

UGG UGA

GAC

CAG CAA GGC GGU GGG GGA CGC CGG

CGU

CGA

GCC GCG

CUA

GCA

CCC CCG

CUG GCU

CCU

CCA

Fig. 3. A Gray code representation of the genetic code.

JANUARY/FEBRUARY 2006

determined by quantum London forces in a hyperbolic pocket or superposition of both conformations (see Figure 5). The motion of an object between two conformational states of tubulin is equivalent to two curvatures in space-time geometry represented as a two-dimensional (2-D) space-time sheet. Thus, MT appears to be most promising candidate for information processing. Several authors have looked at the possibility of classical information processing and quantum information processing in MTs [93], [94]. Therefore, a natural question is what kind of error-correction mechanism do they have? To answer this question, [93] is a good starting point for classical information processing. Penrose, Hameroff, Hagan, and Tuszynski make proposals about quantum computation in brain MTs (see [95] and references therein). In fact, Porter suggested a topological quantum error correction in MTs very recently [61]. Porter assumes special 2-D particles (he calls them anyons) that are involved in topological quantum computing by moving around each other on the wall of MT. His argument about anyon goes as follows. All known particles are “bosons” or “fermions.” Bosons gather together while fermions stay apart. If you swap two bosons, their quantum state will not change; however, if we swap two fermions, their quantum state will be multiplied by −1. The spin-statistics theorem says that these are the only possibilities in three dimension. In two dimension, the phase factor modifying the quantum state can be any complex number of size 1; thus the name “any-on.” None of the elementary particles are anyons, but there can be anyonic “quasiparticles” made of a group of electrons [61]. In Freedman’s model of topological quantum computing, the anyons will be localized patterns of qubit flips created in pairs. Since the MT can be seen as an array of qubits, the creation of topological states requires the prior existence of a domain of coherently coupled dimer-qubits that rings the MT. Once such a “quantum ring” exists, anyonic motions can create a robust multiqubit entanglement. This speculation still must be investigated in more detail [61]. Penrose has suggested that Fibonacci patterns on microtubules may be optimal for error correction [96]. As these ideas are quite nascent, we need rigorous analysis and experimental verifications.

isms have four nucleotide bases and 20 amino acids. The second case shows that today’s genetic code evolved from a simpler one with ten amino acids. So, if there is a quantum computer, a natural question is then what kind of error correction? Recently, a quantum mechanical model of adaptive mutation has been experimented by McFadden and Khalili [98].

25 nm 15 nm

β α

α β α

(2Q + 1)sin−1




1 √ N

 =

β

β α

β α

β α

8 nm

α β α

β

β α

β α

β α

β

β α

α

β α

α

β α

β α

Grover’s Search Algorithm and Genetic Code

Patel studies DNA replication and protein synthesis from a computer science point of view [57]. According to his proposal, there is a quantum computer working behind four nucleotide bases and 20 amino acids. These numbers arise as a solution to an optimization problem. Grover gave an algorithm for searching an unordered database of N objects on a quantum computer [97]. More precisely, this optimal quantum search algorithm relates the number of objects N that can be distinguished by a number of yes/no queries Q according to

α β

β

Fig. 4. A microtubule [94].

π . 2

The solutions of this for small values of Q have special significance for the number of building blocks involved in genetic information processing according to Patel

Q = 1, N = 4; Q = 2, N = 10.5; Q = 3, N = 20.2. Identification of a binary quantum query with nucleotide base-pairing gives a natural explanation of why living organIEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Fig. 5. Tubulin can exist in superposition of both conformations [96].

JANUARY/FEBRUARY 2006

51

Many such hypotheses are emerging in quantum biology. Still, one has to investigate the error-correction mechanism in such systems. Conclusions

Information processing in biology is a fast-emerging field, and coding and information theory for biological systems is still in its infancy. We hope in the future that this new field will become very important to understand how biology is doing information technology at the molecular level. The quest for information processing principles in life sciences has begun seriously (for example, a main aim of the Howard Hughes Medical Institute’s recently opened Janelia research farm is “the identification of the general principles that govern how information is processed by neuronal circuits” [100]. We have more questions than answers at this moment in this area. We need to search for classical and quantum codes and, in some cases, different types of coding. Perhaps a cellular automata model will be helpful in some cases. We need coding and information theorists to look at this. In particular, we need solid mathematical foundations. If we can solve the problem of protein folding with such techniques, it will change the future of medicine. We need the analogue of Shannon’s theorems, and we need to classify the biological communication channels. Certainly, we need to extend the domain of coding and information theory in order to understand the biological communication systems [99]. Today, a revolution in biology is being led not only by biologists but by computer scientists, engineers, and mathematicians (one such example is Eric S. Lander, who was the key person involved in the human and mouse genome projects. He was trained as a coding theorist and not as a biologist). Acknowledgments

The author would like to thank number of people who had sent their pre-/reprints, and who had discussions with the author. In particular, the author wants to thank Cris Calude, D.A. Forskyde, Hesham El-gamal, T. Aaron Gulliver, Lila Kari, D. A. Mac Dónaill, D.J.C. MacKay, A. Patel, Patrick Sole, Jack Tuszynski, and H.P. Yockey. The author is thankful to Stuart Hameroff for allowing his figures to be included in the paper and to the anonymous referees for helpful suggestions. Instead of redrawing, some figures are taken from the public domain. The author is thankful to Navin Kashyap and to the Mathematics and Statistics Department at Queens University, Canada, for financial support. Manish K. Gupta is currently serving as an adjunct assistant professor and postdoctoral fellow in the Mathematics Department, Queens University, Canada. Gupta received his B.S. degree in physics, chemistry, and mathematics and an M.S. degree in mathematics from the University of Lucknow, India. He earned his Ph.D. in mathematics in 2000 from the Indian Institute of Technology, Kanpur, India. He worked as a Marsden research fellow at the University of Canterbury, Christchurch, New Zealand, in a quantum error-correction project of the Royal Society of New Zealand from 2000–2002. From August 2002–May 2004, he was a faculty associate and postdoctoral fellow at 52 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

the Department of Computer Science and Engineering at Arizona State University (ASU). and from May 2004–2005 he workedat the Information Processing Systems Laboratory, Ohio State University (OSU) as a postdoctoral fellow/lecturer. He taught several courses at the Department of Computer Science and Engineering at ASU and the Department of Electrical and Computer Engineering at OSU. His research interests include information processing in biology, coding, and information theory, DNA, and quantum computing. He is a Member of the IEEE, and of the IEEE Information Theory, Communication and Computer Societies, the International Society for Computational Biology, and the International Society for Nanoscale Science, Computation and Engineering. He is a coauthor of a forthcoming research monograph in quantum error correction and has written several research articles. He has served as a reviewer to several international journals in mathematics and communications and has also given several invited talks in the United States, Singapore, New Zealand, Korea, and India. Address for Correspondence: Manish K. Gupta, Room 216, Department of Mathematics and Statistics, Queens University, Kingston, Ontario K7L3N6 Canada. Phone: +1 613 533 2409. Fax: +1 613 533 2964. E-mail: [email protected]. References [1] S. Verdu, “Fifty years of Shannon theory,” IEEE Trans. Inform. Theory, vol. 44, no. 6, pp. 2057–2078, 1998. [2] H.P. Yockey, Symposium on Information Theory in Biology,. New York: Pergamon Press, 1956. [3] H.P. Yockey, Information Theory and Molecular Biology. Cambridge, UK: Cambridge Univ. Press, 2005. [4] B. Hayes, “The invention of the genetic code,” Amer. Scientist, vol. 86, no. 1, pp. 8–14, 1998. [5] L.S. Liebovitch, Y. Tao, A.T. Todorov, and L. Levine, “Is there an error correcting code in the base sequence in DNA?” Biophys. J., vol. 71, no. 3, pp. 1539–1544, 1996. [6] D.R. Forsdyke, “Are introns in-series error-detecting sequences?,” J. Theoretical Biol., vol. 93, no. 4, pp. 861–866, 1981. [7] J. Rzeszowska-Wolny, “Is genetic code error-correcting?,” J. Theor. Biol., vol. 104, pp. 701–702, 1983. [8] M.K. Gupta, private communication with Donald Forsdyke, 2001. [9] M.K. Gupta, private communication with Apoorva Patel, 2001. [10] D.A. Mac Dónaill, “A parity code interpretation of nucleotide alphabet composition,” Chem. Commun., no. 18, pp. 2062–2063, 2002. [11] T.D. Schneider, G.D. Stormo, L. Gold, and A. Ehrenfeucht, “Information content of binding sites on nucleotide sequences,” J. Mol. Biol., vol. 188, no. 3, pp. 415–431, 1986. [12] T.D. Schneider and R.M. Stephens, “Sequence logos: A new way to display consensus sequences,” Nucleic Acids Res., vol. 18, no. 20, pp. 6097–6100, 1990. [13] T.D. Schneider, “Sequence logos, machine/channel capacity, Maxwell’s demon, and molecular computers: A review of the theory of molecular machines,” Nanotechnology, vol. 5, no. 1, pp. 1–18, 1994. [14] T.D. Schneider, “Theory of molecular machines. I. Channel capacity of molecular machines,” J. Theor. Biol., vol. 148, no. 1, pp. 83–123, 1991. [15] T.D. Schneider, “Theory of molecular machines. II. Energy dissipation from molecular machines,” J. Theor. Biol., vol. 148, no. 1, pp. 125–137, 1991. [16] T. Berger, “Living information theory,” IEEE Information Theory Soc. Newslett., vol. 53, no. 1, pp. 1, 6–19, 2003. [17] J. von Neumann, The Computer and the Brain. New Haven, CT: Yale Univ. Press, 1958. [18] E. Schrodinger, What Is Life? Cambridge, UK: Cambridge University Press, 1944. [19] E.B. Baum, What Is Thought? Cambridge, MA: MIT Press, 2004. [20] J.L. Casti, “Steve Grand’s creation: Life and how to make it (review),” Nature, vol. 409, pp. 17–18, 2001. [21] W. Gitt, In the Beginning Was Information. Bielefeld, Germany: CLV, 2001. [22] G. Rowe, Theoretical Models in Biology: The Origin of Life, the Immune System, and the Brain. New York: Oxford Univ. Press, 1994. [23] H. Nishimori, Statistical Physics of Spin Glasses and Information Processing: An Introduction. New York: Oxford Univ. Press, 2001. [24] N. Sourlas, “Statistical mechanics and error-correcting codes,” 1998 [Online]. Available: http://arXiv.org/abs/cond-mat/9811406 [25] D.L. Stein, Spin Glasses and Biology. Singapore: World Scientific, 1992.

JANUARY/FEBRUARY 2006

[26] R.L. Constable, “Notes on what is information? Workshop,” Cornell Univ., Ithaca, NY, Tech. Rep. 14853–7501, 2001. [27] B. Hassenstein, Information and Control in the Living Organism. London: Chapman & Hall, 1971. [28] L.L. Gatlin, Information Theory and the Living System. New York: Columbia Univ. Press, 1972. [29] R. Baddeley, P. Hancock, and P. Foldiak, Information Theory and the Brain. Cambridge, UK: Cambridge Univ. Press, 2000. [30] C. Adami, “Information theory in molecular biology,” Physics Life Rev 1, pp. 3–22, 2004. [31] S. Ji, “Molecular information theory: Solving the mysteries of DNA,” in Modeling in Moldecular Biology (Natural Computing Series), G. Ciobanu and G. Rozenberg, Eds. Berlin: Springer, 2004, pp. 141–150. [32] J. Avery, Information Theory and Evolution. Singapore: World Scientific, 2003. [33] O. Milenkovic and B. Vasic, “Information theory and coding problems in genetics,” in Proc. IEEE Information Theory Workshop, Oct. 2004. [34] S. Hussini, L. Kari, and S. Konstantinidis, “Coding properties of DNA languages,” Theoretical Comput.Sci., vol. 290, no. 3, pp. 1557–1579, 2003. [35] Stambuk, “On circular coding properties of gene and protein sequences,” Croatia Chemica ACTA, vol. 4, no. 4, pp. 999–1008, 1999. [36] G. Chaitin, “Information-theoretic computation complexity,” IEEE Trans. Inform. Theory, vol. 20, no. 1, pp. 10–15, 1974. [37] G.J. Chaitin, “To a mathematical definition of life,” ACM SICACT News, vol. 4, p. 12, 1970. [38] C.H. Bennett, “Logical reversibility of computation,” IBM J. Res. Develop, vol. 17, no. 6, p. 525, 1973. [39] C.E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, pp. 379–423, 623–656. [40] R.A. Fisher, “The theory of confounding in factorial experiments in relation to the theory of groups,” Ann. Eugenics, vol. 11, pp. 341–353, 1942. [41] R.A. Fisher, “A system of confounding for factors with more than two alternatives, giving completely orthogonal cubes and higher powers,” Ann. Eugenics, vol. 12, pp. 2238–2290, 1945. [42] F. Gray, “Pulse code communication,” U.S. Patent 2 632 058,” Mar. 17, 1953. [43] A.R. Calderbank, “The art of signaling: Fifty years of coding theory,” IEEE Trans. Inform. Theory, vol. 44, no. 6, pp. 2561–2595, 1998. [44] E.S. Lander and M.S. Waterman, Calculating the Secrets of Life. Washington, DC: National Research Council, 1995. [45] R.A. Fisher, The Genetical Theory of Natural Selection. Oxford, UK: Oxford University Press, 1930. [46] D. Voet and J.G. Voet, Biochemistry. New York: Wiley, 1995. [47] J.D. Watson, T.A. Baker, S.P. Bell, A. Gann, M. Levine, R. Losick, Molecular Biology of the Gene. San Francisco, CA: Benjamin Cummings, 2005. [48] G. Bock and J. Goode, Complexity in Biological Information Processing. New York: Wiley, 2001. [49] S. Fraga, K.M.S. Saxena, and Manuel Torres, Bio-molecular Information Theory. New York: Elsevier, 1978. [50] H.C. Luttgau and R. Necker, Biological Signal Processing. Weinheim, Germany: Sonderforschungsbereiche, 1989. [51] S.J. Mojzsis and A.G. Kishnamurthy, “Before RNA and after: Geological and geochemical constraints on molecular evolution,” in The RNA World, The Nature of Modern RNA Suggests a Prebiotic RNA. Boca Raton, FL: Cold Spring Harbor Laboratory Press, 1998, pp. 1–47. [52] H.P. Yockey, “Origin of life on earth and Shannon’s theory of communication,” Computers and Chemistry, vol. 24, no. 1, pp. 105–123, 2000. [53] H.P. Yockey, Information Theory, Evolution and the Origin of Life: Fundamentals of Life. New York: Elsevier, 2002, pp. 335–348. [54] A. Patel, “Carbon—The first frontier of information processing,” J. Biosci., vol. 27, no. 3, pp. 207–218, 2002. [55] J.A. Tuszynski, “Entropy versus information: Is a living cell a machine or a computer?,” in Proc. Int. Conf.Comput. Information Technol., 2001. [56] J.A. Tuszynski, “Biomolecular quantum computers,” in Molecular Computing, T. Sienko, Ed. Cambridge, MA: MIT Press, 2001. [57] A. Patel, “Quantum algorithms and the genetic code,” Pramana-J. Physics, vol. 56, no. 2–3, pp. 367–381, Feb./Mar. 2000. [58] A. Patel, “Mathematical physics and life,” Mathematical Sciences Series: Selected Topics in Computing and Information Science, J.C. Misra, Ed., vol. 4. New Delhi, India: Narosa, 2003, pp. 270–293. [59] M.K. Gupta, private communication with Toby Berger, Sep. 2003. [60] J. von Neumann, “Probabilistic logics and the synthesis of reliable organisms from unreliable components,” in Automata Studies. Princeton, NJ: Princeton Univ. Press, 1956. [61] M. Porter, “Topological quantum error correction: Applications to microtubules,” in Proc. Quantum Mind Conf., Tucson, Arizona, 2003. [62] F.H.C. Crick, “The origin of the genetic code,” J. Mol. Biol., vol. 38, no. 3, pp. 367–379, 1968. [63] F. Crick, “Central dogma of molecular biology,” Nature, vol. 227, pp. 561–563, 1970. [64] G. Battail, “Does information theory explain biological evolution?,” Europhysics Lett., vol. 40, no. 3, pp. 343–348, 1997. [65] M. Eigen, “The origin of genetic information: viruses as models,” Gene, vol. 135, no. 1–2, pp. 37–47, 1993. [66] E.E. May, “Comparative analysis of information based models for initiating protein translation in escherichia coli K-12,” M.S. thesis, NC State Univ., Raleigh, NC, 1998.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

[67] R. Roman-Roldan, P. Bernaola-Galvan, and J.L. Oliver, “Applications of information theory to DNA sequence analysis: A review,” Pattern Recognition, vol. 29, no. 7, pp. 1187–1194, 1996. [68] E.E. May, M.A. Vouk, D.L. Bitzer, and D.I. Rosnick, “A coding theory framework for genetic sequence analysis,” in Proc. Workshop Genomic Signal Processing Statistics, 2002. [69] J.J. Hopfield, “Kinetic proofreading: A new mechanism for reducing errors in biosynthetic process requiring high specificity,”Proc. Nat. Acad. Sci. USA, vol. 71, no. 10, pp. 4135–4139, 1974. [70] J.J. Hopfield, “The energy relay: A proofreading scheme based on dynamic cooperativity and lacking all characteristic symptoms of kinetic proofreading in DNA replication and protein synthesis,” Proc. Nat. Acad. Sci. USA, vol. 77, no. 9, pp. 5248–5252, 1980. [71] J.J. Hopfield, “Origin of the genetic code: A testable hypothesis based on tRNA structure, sequence, and kinetic proofreading,” Proc. Nat. Acad. Sci. USA, vol. 75, no. 9, pp. 4334–4338, 1978. [72] D.J.C. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge, UK: Cambridge Univ. Press, 2003. [73] E.E. May, Analysis of Coding Theory Based Models for Initiating Protein Translation in Prokaryotic Organisms, Ph.D. dissertation, NC State Univ., Raleigh, NC, 2002. [74] E.E. May, M.A. Vouk, D.L. Bitzer, and D.I. Rosnick, “Coding theory based models for protein translation initiation in prokaryotic organisms,” J. BioSyst., vol. 76, no. 1–3, pp. 249–260, 2004. [75] E.E. May, M.A. Vouk, D.L. Bitzer, and D.I. Rosnick, “Constructing optimal convolutional code models for prokaryotic translation initiation,” in Proc. 2nd Joint EMBS/BMES Conf., 2002, vol. 3, pp. 2188–2189. [76] E.E. May, “Use of iterative methods in biological coding theory: Applications,” in Proc. IMACS 03, 2003. [77] E.E. May, “Towards a biological coding theory discipline,” New Thesis, vol. 1, no. 1, pp. 19–38, 2004. [78] G.L. Rosen, “Finding near-periodic DNA regions using a finite-field framework,” in Proc. IEEE Workshop Genomic Signal Processing (GENSIPS), May 2004. [79] G.L. Rosen and J.D. Moore, “Investigation of coding structure in DNA,” in Proc. IEEE Int. Conf. Acoustics, Speech Signal Processing (ICASSP), Apr. 2003. [80] G. Battail, “An engineer’s view on genetic information and biological evolution,” Biosystems, vol. 76, no. 1–3, pp. 279–90, 2004. [81] G. Battail, “Can we explain the faithful communication of genetic information?,” in Proc. DIMACS Working Group Theoretic. Advances Information Recording, Mar. 2004. [82] M. Barbieri, The Organic Codes. Cambridge, UK: Cambridge Univ. Press, 2003. [83] A.R. Hammons, P.V. Kumar, A.R. Calderbank, N.J.A. Sloane, P. Sole, “Thelinearity of Kerdock, Preparata, Goethals, and related codes,” IEEE Trans. Inform. Theory, vol. 40, no. 2, pp. 301–319, 1994. [84] L.E. Kay, Who Wrote the Book of Life? A History of the Genetic Code. Stanford, CA: Stanford Univ. Press, 2000. [85] M.A. Jimenez-Montano, C.R. Mora-Basanez, and T. Poschel, “On the hypercube structure of the genetic code,” in Proc. 3rd Int. Conf. Bioinformatics Genome Res.,1994, p. 445. [86] M.A. Jimenez-Montano, C.R. Mora-Basanez, and T. Poschel, “The hypercube structure of the genetic code explains conservative and non-conservative amino acid substitutions in vivo and in vitro,” BioSyst., vol. 39, no. 2, pp. 117–125, 1996. [87] I. Stewart, “Broken symmetry in the genetic code?,” New Scientist, vol. 1915, p. 16, Mar. 1994. [88] D.L. Gonzalez, “Can the genetic code be mathematically described?,” Med. Sci. Monit., vol. 10, no. 4, pp. HY11–17, 2004. [89] D.A. Mac Donaill, “Why nature chose A,C, G and U/T, an error-coding perspective of nucleotide alphabet composition,” Origins of Life and Evolution of the Biosphere, vol. 33, pp. 433–455, 2003. [90] E. Szathmary, “What is the optimum size for the genetic alphabet?,” in Proc. Natl. Acad. Sci. USA, vol. 89, no. 7, p. 2614, 1992. [91] P. Cappuccinelli and N. Ronald Morris, Microtubules in Microorganisms. New York: Marcel Dekker, 1982. [92] P. Dustin, Microtubules. Berlin: Springer, 1984. [93] J. Faber, L.P. Rosa, and R. Portugal, “Information processing in brain microtubules,” in Quantum Mind Conf., 2003. [94] S. Hameroff and J. Tuszynski, Search for Quantum and Classical Modes of Information Processing in Microtubules: Implications for the Living State. Singapore: World Scientific, 2003. [95] S. Hagan, S.R. Hameroff, and J.A. Tuszynski, “Quantum computation in brain microtubules: Decoherence and biological feasibility,” Physical Rev. E, vol. 65, no. 6, pt. 1, pp. 061901, 2002. [96] S. Hameroff home page [Online]. Available: http://www.quantumconsciousness.org [97] L.K. Grover, “A fast quantum mechanical algorithm for data base search,” in Proc. Annual ACM Symp.Theory Computing (STOC), 1996, p. 212–219. [98] J. McFadden and J. Al-Khalili, “A quantum mechanical model of adaptive mutation,” BioSyst., vol. 50, no. 3, pp. 203–211, 1999.. [99] A. Patel, “Information processing beyond quantum computation” [Online]. Available: http://arxiv.org/abs/quant-ph/0306158 [100] Janelia Farm Research Campus [Online]. Available: http://www. hhmi.org/janelia

JANUARY/FEBRUARY 2006

53

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

Digital Parity and the Composition of the Nucleotide Alphabet Shaping the Alphabet with Error Coding © EYEWIRE

´ ´ BY DONALL A. MAC DONAILL

We not only want to know how nature is (and how her transactions are carried through), but we also want to reach, if possible, a goal which may seem utopian and presumptuous, namely, to know why nature is such and not otherwise. Albert Einstein [1]

hen in 1953, James Watson and Francis Crick published their insight into the structure of deoxyribonucleic acid [2] and the implicit explanation of how A, C, G, and T (Figure 1) interact to form a replicating system, it signaled not only a new era in biology but, more generally, in society. The half century or so following Watson and Crick’s seminal paper has produced an almost unimaginable wealth of data on the molecular mechanisms of biological systems, and as our grasp of the mysteries of molecular biology has deepened, DNA become one of the most powerful cultural icons of the 20th century [3]. Yet, for all the impact of Watson and Crick’s discovery on modern society, the core concepts in DNA replication are surprisingly simple. Information is expressed as a one-dimensional string of letters written using the bases A, C, G, and T, where A and G are the larger bases, termed purines, and C and T are the smaller monocyclic bases, termed pyrimidines. The order of the letters is preserved by chemical attachment to a sugar-phosphate backbone. It can be observed, for example, that the size and shape of nucleotide T complements the corresponding features in A, so that A and T fit snugly together (Figure 1), with their association stabilized by hydrogen-bonds (weak stabilizing interactions between hydrogen atoms (H) and regions rich in accessible electron density. Similarly, nucleotides C and G complement each other, and this familiar presentation of A to T and C to G is commonly labeled the Watson-Crick arrangement. Replication of a DNA strand proceeds by using the original strand as a template against which a second, complementary strand is constructed by inserting the complementary nucleotide—say G for C or A for T—of whatever nucleotide is written in the primary strand. The resulting daughter strand may be regarded as a “negative” of the original strand. When this daughter

W

54 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

strand is itself used as the template, the result, in the absence of error, is a perfect copy of the original strand. Somewhat less heralded is the fact that the same 50 years has witnessed considerably less progress regarding the why of molecular biology. Is hydrogen-bonding necessary for nucleic acid replication, or might some other molecular interaction prove suitable? Why is the genetic code a triplet code and not a doublet code or even a quadruplet code? And, of course, the question we expand on here: Why are there four letters in the genetic alphabet and why A, C, G, and T in particular? It is one thing to explain how a system works, but it may prove quite another to explain why a particular strategy was favored over other conceivable solutions. Only when we can answer questions such as these with some certainty, can we begin to be satisfied with our grasp of the processes underlying life. Reverse engineering offers a conceptually simple strategy, which may go some way to addressing questions such as these. By modifying a feature in the system of interest and observing the consequences, the benefit afforded by that feature, often far from obvious, may be identified. This final question, relating to the composition of the natural alphabet, is, as observed by Crick, one of the most fundamental issues in our understanding of the emergence of living matter [4]. Eschenmoser has adopted this in pursuing a series of studies exploring the chemical etiology of nucleic acid structure: The strategy is to conceive (through chemical reasoning) potentially natural alternatives to the nucleic acid structure, to synthesize such alternatives by chemical methods, and to compare them with the natural nucleic acids with respect to those chemical properties that are fundamental to the biological function of RNA and DNA. [5] The sugar employed in DNA is deoxyribose, a sugar containing five carbon atoms and therefore a pentose, but the particular basis for nature’s preference for this sugar over the many alternatives is not self-evident. In order to explore this, Eschenmoser synthesized a hexose analogue, a larger sugar with six carbon atoms, and observed that complementary strands of hexose-DNA do not fit together according to classical Watson-Crick rules, and that helices based on purinepurine associations, including A:A and G:G, are possible [6]. 0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

Thus, it would appear that the “choice” of ribose was not arbitrary, and that modifications of the polymer backbone, in this case the sugar, can in fact significantly affect nucleotide association and recognition. Further studies by Eschenmoser addressed the choice of the particular pentose, and it was observed that nucleic acids derived from alternative pentoses, that is, other than ribose, could result in even-stronger Watson-Crick pairing than in either RNA or DNA, with pyranosyl RNA in particular pairing exclusively in the WatsonCrick mode. Eschenmoser was able to conclude “that Nature did not choose her genetic system by the criterion of maximal base-pairing strength” [7]. Recently, and adopting a similar approach, a more complete explanation for the choice of ribose was offered by Springsteen and Joyce, who explored the reaction of cyanamide with ribose and a variety of other sugars. They reported a preferential formation of ribosecyanamide, which can react with cyanoacetylene to form pyrimidine nucleosides), and a particular propensity of ribosecyanamide to crystallize in aqueous solution [8]. The size of the nucleotide alphabet is a related problem, which might be amenable to a reverse-engineering approach. One expects, a priori, that in the absence of other constraints, a larger alphabet is to be preferred over a smaller one since the informational significance of a letter increases with alphabet size. It is in fact quite easy to conceive how the nucleotide alphabet might, in principle, be expanded through the inclu-

H N

N N

O

H

H

N

R

N N

N

R

O A N N

H

N

O

H

N

H

K H

O

N

O

H

N

N

N N

N N

N R

X

T

H

sion of nucleotides similar in size and shape to those employed in replication but differing from the natural alphabet in the patterns of hydrogens and lone pairs (e.g., Figure 2). (In the chemical literature, for reasons relating to acid-base chemistry, such patterns are often referred to as donor-acceptor patterns, where the hydrogens attached to oxygen, O, or nitrogen, N, are potential hydrogen donors, and the electron-rich lone pairs, which may accept hydrogens from elsewhere, are potential hydrogen acceptors.) That the natural alphabet, the product of billions of years of evolution, consisted of just the four letters, A, C, G, and T, suggests that this particular set of nucleotides is somehow optimal, or close to optimal, although the reasons are not self-evident. Adopting a reverse-engineering philosophy, the laboratories of both Benner [9] and Switzer [10] explored expanded alphabets, considering the noncanonical pairs κ :X and iC:iG (Figure 2). A comparison of Figures 1 and 2, will reveal that the κ :X pair is quite similar to the naturally occurring pair A:T, the most significant difference being that the hydrogen/lone-pair or donor-acceptor (D/A) pattern has been exchanged between the larger and smaller nucleotides. As their symbols suggest, iC and iG, or iso-C and iso-G, are similar to the naturally occurring C and G, differing from them in having inverted hydrogen/lone-pair patterns. The results, which showed that the additional nucleotides were accepted and copied appropriately by the natural replication apparatus, were very significant, as they implied that larger alphabets were in fact possible. One possibility was that the natural genetic alphabet might not after all be optimal and that nature had failed to discover the additional nucleotides, possibly because the biochemical apparatus which had evolved around the canonical alphabet was such that the extension of the alphabet was practically impossible. One can easily imagine that such a situation might in fact arise, but it is nonetheless

R

N

R

N

H

N H

H

O

N

H C

R H

G

Fig. 1. The nucleotide alphabet, A (adenine), T (thymine), C (cytosine), and G (guanine), in conventional chemical notation. Interpreting the molecular representation as a graph, the vertices correspond to atoms, and the edges, or solid lines, to chemical bonds. By convention, carbon atoms (C) are not explicitly represented, and any vertex where the occupying atom is unspecified is taken to be a carbon. The shaded lobes are termed lone pairs, referring to a pair of electrons not involved in chemical bonds. Lone pairs are rich in electron density and participate in weak bonding with hydrogens (H), which are attached to nitrogen (N) or oxygen (O) atoms. Such interactions are termed hydrogen bonds, and are indicated in the figure by broken lines. The symbol R represents the sugar-phosphate backbone, to which the nucleotides are attached.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

iC iG

H N N

R

N

H

O

N

H

N N

N O

H

N

R H

Fig. 2. Additional nucleotides κ and X [9] and iC and iG [10]. These letters differ form the canonical set (Figure 1) in the hydrogen/lone-pair patterns. The symbol R represents the sugar-phosphate backbone, to which the nucleotides are attached.

JANUARY/FEBRUARY 2006

55

Information transmission and information processing, central features of the living state, are subject to rules relating to error-coding theory.

appropriate to seek positive causes that might constrain the composition of the nucleotide alphabet. Most studies addressing the composition of the alphabet have explored what we may loosely consider as “hardware” or physicochemical issues, such as the prebiotic availability of nucleotides [11], the role of hydrogen-bonding [12], tautomerism [13], or the effect of thermodynamic binding and mismatch energies [14]. But could there also be a “software” aspect to this and other problems in molecular biology? Stahl and Goheen suggested as much, demonstrating as far back as 1963 that some molecular biological processes could, in a strict and formal sense, be interpreted as computational processes [15], but their insight failed to have the impact that, in retrospect, one might have expected. Forsdyke invoked Hamming’s error-coding theory [16] as a potential explanation for the role of introns [17], an idea subsequently investigated by Liebovitch et al. [18]. Others considered the structure information within the genetic code [19]. One may detect in these and the work of Battail [20], [21] and, more recently, in that of Mac Dónaill [22] and May et al. [23] the emergence of a “software” aspect to molecular biological systems, succinctly summarized by Dawkins [24] as follows: If you want to understand life, don’t think about vibrant, throbbing gels and oozes, think about information technology. The key concept is that information transmission and information processing, central features of the living state, are subject to rules relating to error-coding theory, quite independent of any constraint relating to the particular medium in which life is encoded. In other words, life exists at the intersection of chemistry and informatics; see Figure 3. Nucleic acid replication, the central reaction responsible for the transmission of hereditary information, is the quintessential molecular biological information transmission phenomenon, and it is here that one might expect “software” or informatics considerations to play a constraining evolutionary role in an

Chemistry or “Hardware”

Molecular Biology

Informatics or “Software”

Fig. 3. A schematic representation of the relationship of molecular biology to chemistry and informatics.

56 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

emergent nucleotide alphabet, in addition to any physicochemical or “hardware” constraints. A couple of studies approached this aspect of nucleotide alphabet composition. Szathmáry recognized the importance of hydrogen donor-acceptor (D/A) patterns [25], while a potential role for error-coding theory was implicitly suggested by Yockey [26] in the context of a discussion on error-coding theory in molecular biology. Yockey even assigned 5-b numerical representations to A, C, G, and U/T, but the assignments were arbitrary, and based on equating each nucleotide with elements of a four-letter code from a simple problem in error detection. For an error-coding analysis to usefully address the problem of alphabet composition, the representation of nucleotides should reflect the expression of information or patterns inherent in the nucleotides, and it is in this respect Yockey’s assignments were lacking. A potential role for informatics in constraining the composition of the alphabet seems quite plausible. In the model discussed below, we summarize an approach which acknowledges Yockey’s implicit suggestion of a role for error-coding theory, but one in which the association between codewords and nucleotides is not arbitrary but based on patterns in the nucleotides themselves, echoing Szathmáry’s insight into the significance of hydrogen/lone-pair patterns [22], [27]. A digital representation of nucleotides is constructed, and the problem of alphabet composition is approached from the perspective of error-coding theory. Error-Coding Theory

Error-coding theory is concerned with error detection and correction in data transmission and storage systems and was proposed by Hamming more than half a century ago [16]. In its most elementary form, it involves the judicious selection of a set of binary numbers with a view to minimizing the possibility that a transmission error could go undetected, and that, subject to appropriate conditions, an error might be corrected. In the example depicted in Figure 4(a), the set of two-digit codewords C1 = {00,01,10,11}, encompassing the set of all-two-digit binary numbers B2 , is employed to represent the four cardinal directions. Although economical in the sense that two-digits are employed to transmit 2 b of information, the code is error prone; if noise flips a bit in a codeword ci ∈ C1 , it necessarily changes it into another codeword, cj ∈ C1 . In the example when 01 = down is transmitted, but 00 = up is received, the received word may go undetected since it belongs to the set of possibly transmitted words. A simple but effective remedy is to add an additional bit such that all codewords have the same parity. Parity is determined by counting the number of bits set to 1. Figure 4(b) depicts the creation of the even-parity code C2 , a subset of the available 3-b numbers in B3 in which no two members are adjacent. An error in any single bit necessarily JANUARY/FEBRUARY 2006

11

Down Right Transmitter Up 00

Left 10

Down 01 00 +

Up 00

Receiver

⊃

B2

01

C1 = B2

C2 B3

00

000 011

01

Code C1 = B2

f: B2

10

Noise

110

001

(b) Receiver

B3

111 01

Left 101

011 Encoder

Right 110 Code C2

010

⊃

011

101

11

(a) Down

B3

001

+

Codeword? Yes

No B3 Noise

000

100 Up

(c)

Fig. 4. (a) The set of four cardinal directions are depicted together with binary interpretations, forming the code C 1. All four possible codewords in B 2 are employed, and a change in any one bit will change one codeword into another recognized codeword. In the schematic representation of transmission, the transmitter converts the input Down to the corresponding codeword 01. However, a transmission error converts 01 into 00, and the error remains undetected since the received word 00 also belongs to code C 1. (b) The addition of a parity bit to the elements of C 1 to yield the even-parity code C 2 is represented. In this case, a change in any one bit will change one codeword into a noncodeword, and such an error would be recognized. Such an eventuality is depicted in (c), where an equivalent transmission error to that in (a) converts the codeword 011 (even) to the noncodeword 001 (odd), so that the error is detected.

changes a codeword ci ∈ C2 into a noncodeword cj ∈/ C2 . The error can therefore be detected, although in this code, no correction is possible. It is important to note that not all subsets of B3 would be equally effective; for example, the transmission error depicted in Figure 4(c) would not have been detected had the nonparity code, say C3 = {000, 001, 011, 110}, containing both 001 and 011, been employed. The difference in codes C2 and C3 lies in the distance between the constituent codewords, which may be usefully expressed in terms of the Hamming distance, ∂, defined as the number of bits in which two codewords differ. It is equivalent to the number of bits set to 1 in the Boolean exclusive or product XOR. Inspection will show that the Hamming distance between any two codewords in code C2 [Figure 4(b)], e.g., 011 and 110, is equal to 2. By contrast, the distance between codewords 001 and 011 in C3 above is equal to 1, and the noise-induced conversion of 001 to 011 would go undetected.

The noncanonical nucleotides considered by Benner [9] and Switzer [10] (Figure 2) are not the only ones possible. As nucleotides may present either a hydrogen donor or acceptor in each of three positions, eight different D/A patterns are available. As each pattern may be separately expressed on purines and pyrimidines, a total of 16 distinct patterns is possible, each corresponding to a potential nucleotide or nucleotide analogue (Figure 6). The binary interpretation of these nucleotides spans the binary space B 4 . Error-coding theory informs us that not all sets of codewords are equally robust with respect to error resistance. If the recognition features of nucleotides may be reasonably interpreted as codewords, then perhaps not all sets of

H N

H

0

N

Numerical Interpretation of Nucleotides

In nucleotide recognition hydrogens (hydrogen donors) are always opposed by lone pairs (hydrogen acceptors), and the monocyclic pyrimidines are always opposed by the two-cycle purines. This “lock-and-key” nature of molecular recognition readily admits a binary representation, and hydrogen/lone-pair (or donor/acceptor) patterns can be expressed in terms of zeros and ones, and purines and pyrimidines may be interpreted as 0 and 1, respectively. Thus, 4 b are sufficient to capture nucleotide recognition patterns (Figure 5). The particular choice of 0 or 1 for donors or acceptors, purines or pyrimidines, is of course arbitrary. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

1

b3

b2

0 O 1

H

N N

N

R

N

N R

O

0

b1

H N

1

H b0: Pyrimidine = 1

b0: Purine = 0

b3

b2

b1

b0

b3

b2

b1

b0

1

0

0

1

0

1

1

0

C = (100,1)

G = (011,0)

Fig. 5. Binary interpretations of nucleotides C and G.

JANUARY/FEBRUARY 2006

57

Nucleic acid replication is the quintessential molecular biological information transmission phenomenon.

nucleotides are equally error resistant, implying a mechanism by which one alphabet might be preferred by evolution over others. Arranging nucleotides according to their numerical parity (Figure 6) reveals that members of the natural alphabet,

S=(000,0)

L=(111,1) H O

H

N

O

H

N

H

N

N N R

O

N O

R H

G=(011,0)

C=(100,1) H O

N N R

H N

N H

N

N H

O

N

N R

H T=(010,1)

aA=(101,0) H N

N N

H N

N

R

O

H

N

O

H

N

N H

R

N

N N

U/T, C, G, aA (an idealized form of A), belong to the set of even-parity nucleotides. (Nature employs adenine, A, Figure 1, and not amino-adenine aA as depicted in Figure 6. However, by using aA we have a 4-b codeword in common with all the other potential codewords, simplifying exposition of the model without affecting the outcome. The Λ=(111,0) Σ=(000,1) difference between A and aA is disH cucssed in “Chemical Limitations.”) O For example, G and C correspond to N H N the even-parity codewords 0110 and 1001, respectively, whereas κ and X, N O O H the noncanonical bases considered by R N Benner et al. [9], correspond to 0100 and 1011, both odd parity. N H R O Viewed in their binary interpretation, H the recognition features of the natural δ=(100,0) β=(011,1) alphabet appear to be structured as a parity code, in which the size of a H O nucleotide, its purine/pyrimidine N H N nature, is related to the hydrogen D/A pattern as a parity bit. As one of the N N H N N R most elementary structures affording N error resistance, a parity structure is R H N O Me also arguably the form most likely to be H first discovered by nature. The question, therefore, is if and how such a X=(010,0) κ=(101,1) structure might afford advantage or H whether the observed structure is a H N mere coincidence, i.e., a frozen evoluO N tionary accident. N

H

O

H

N

iG=(110,0) H N

R H

H

N

Parity and Error Resistance

N

R

Γ=(001,0)

iC=(001,1) O

H

α=(110,1) H O

H

N

N

H

N

N

O

R

N N R

N

N

N

H

R

N

N H

O

N

N N H

R H

(a)

H (b)

Fig. 6. The set of all 16 possible nucleotides or nucleotide analogues, together with corresponding 4-b interpretations: (a) even-parity nucleotides and (b) odd-parity nucleotides. For labels see [27].

58 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

In a conventional transmission context the advantage of a parity code relates to the number of bits which must be changed to convert one codeword into another. No two like-parity members are adjacent (Figure 7, reproduced from [28]), and since an error in the transmission of any single bit changes the parity of the transmitted element, the corrupted codeword can be identified as not belonging to the alphabet. In terms of the Hamming distance, the minimum distance between any likeparity codewords is 2. A close molecular analogy may be observed in the phenomenon of tautomeric instability, in which the arrangement of hydrogen donors and acceptors—and, hence, the JANUARY/FEBRUARY 2006

expressed information—is altered. Nucleotide G for example, equivalent to 0110 (even parity), has among its tautomers [Figure 8(a)] a pattern equivalent to 0010 (odd parity), expressing the recognition pattern of
= (001,0). Here, however, the analogy ends, since although a paritychecking mechanism that might detect such tautomeric forms is conceivable, no such mechanism has been as yet been detected; and the advantage afforded by the parity code structure must be sought elsewhere. When the role of hydrogen bonding in nucleic acid replication is considered, it is usually in terms of complementary hydrogen/lone-pair patterns which serve to stabilize the associations in C:G and A:U. Somewhat less recognized is the role of patterns in encoding the extent of repulsion between noncomplementary pairs. Inspection will reveal that in an alphabet composed of like-parity letters only, a purine presented with a noncomplementary pyrimidine finds the association resisted in two of the three hydrogen/lone-pair positions, e.g., G presented with T [Figure 9(a)]. However, in an alphabet composed of mixed parity nucleotides, where of course the purine/pyrimidine feature no longer mimics a parity bit, noncomplementary associations may be opposed in just a single position, as between C and X [Figure 9(b)]. Here, the single set of opposed lone pairs is insufficient to preclude binding [29], so that whereas G and U experience a repulsive interaction, C can bond not only with its complement G but also with X. An alphabet in which X and C coexisted would be expected to experience a high error rate, errors which would be avoided in an alphabet composed of nucleotides of like parity. The parity-code argument suggests, therefore, that error-resistant alphabets may draw from the eight even-parity letters [Figure 6(a)] or the eight odd-parity letters [Figure 6(b)] but not both. The model sets a constraint to which the processes culminating in the development of a replicating alphabet, and ultimately the origin O N of life, were subject. An emergent replicating alphabet, based perhaps on just a N H N single complementary pair, in principle R N could be of either parity; however once H N booted, the evolutionary advantage of H increasing alphabet size could only be G= (011,0) pursued by the inclusion of like-parity elements. Thus, constraints relating to information, and not to the physicoH chemical nature of nucleotides, preclude the natural alphabet of A, C, G, and T H N N (even parity) from expansion through N the incorporation of odd-parity elements H N R such as κ and X. N O

G=(011,0) L=(111,1)

100

aA=(101,0)

iC=(001,1)

iG=(110,0) U=(010,1) C=(100,1)

S=(000,0)

Fig. 7. The figure shows the subset of even-parity nucleotides interpreted as 4-b digits (in B 4) depicted as positions on a hypercube. It is convenient to partition the hypercube into two 3-D cubes; the outer cube represents purines (final bit = 0) and the inner cube pyrimidines (final bit = 1). The particular location of a nucleotide or codeword on a cube is determined by the three leftmost bits, expressing the binary representation of hydrogen/lone-pair patterns, which are used as coordinates. It may be observed that the distance between any two codewords (or nucleotides) is at least two bits. Figure adapted from [29].

H O

H O

N N

N

R

N H N

iG= (110,0)

N N

N

R

N H N

H

H

G-T1= (001,0)

G-T2= (101,0)

(a) H

H

H N

H N

N N

N N

N N

N R

N

R

O

H O

Chemical Limitations

From an informatics perspective, therefore, it seems that the optimum alphabet would consist of eight letters and a corresponding information density of 3 b/letter (log2 8 = 3). However, the natural alphabet is not an abstract informational construct, and, when expressed in a molecular medium, it will be bounded by the physicochemical limitations of a molecular medium.

010

001

iG-T1= (101,0)

H iG-T2= (100,0)

(b)

Fig. 8. (a) Some tautomeric forms of (a) G and (b) iG, together with corresponding binary interpretations. The tautomer G-T2 expresses a pattern equivalent to that on aA and would therefore match with T, while the tautomer G-T1 corresponds to the odd-parity nucleotide
. The pattern expressed by iG-T1 also mimics A (or aA), and again would be capable of matching with T. It readily interconverts, through internal rotation with tautomer iG-T2, yielding a pattern corresponding to δ.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

59

H O

N N

N

O

H

N N

N

H

R O

H

H G = (011, 0)

N

H

H

N

N

R

N

N N

O

N R

T = (010,1) (a)

H

N O

O

X = (010,0)

R

C = (100,1) (b)

Fig. 9. (a) Association of even-parity nucleotides, U and G, (b) mixed-parity association of C (even) and X (odd). The dashed lines indicate an attractive hydrogen-bonding interaction; the crosses represent repulsive interactions between opposed lone pairs or opposed hydrogen atoms.

Λ=(111,0) β=(011,1) Γ=(001,0)

010

001

κ=(101,1)

100 α=(110,1)

X=(010,0)

S=(000,1)

δ=(100,0)

Fig. 10. Odd-parity nucleotides depicted on a hypercube. The subset of even-parity elements in the space of 4-b digits, B 4, depicted as positions on a hypercube; adapted from [29].

β -T1 H

H

O

The Choice of Parity: Odd Versus Even

H

β -T2 O

N

N

N

R

N

H

H

N

R

N H

Fig. 11. Rotation about the -OH group changes the hydrogen/lone-pair pattern. As the bit pattern is unstable, tautomeric forms possessing such -OH groups are undesirable.

60 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

A viable molecular medium for genetic information demands a measure of chemical robustness with respect to both chemical degradation, in which nucleotides are destroyed and, perhaps more importantly, against pattern instability, which might cause one letter to be read for another. Elementary chemical considerations inform us that the D/A motif of three lone pairs, encoded in S = (000,0) can only be expressed by oxygen situated between two keto groups (Figure 6). This particular motif is in fact a carboxylic acid anhydride, and is readily subject to hydrolysis in an aqueous environment. Being hydrolytically unstable, S is excluded from viable alphabets, accompanied by its complement, L = (111,1). Unlike G, the nucleotide iG proves tautomerically unstable [13], having accessible forms mimicking other nucleotides [Figure 8(b)]. Thus, whereas κ and X are excluded for error-coding reasons, iG and iC are excluded for reasons of tautomeric instability. The potential alphabet of eight evenparity letters is reduced to aA, C, G, and T by physicochemical constraints and differs from the natural alphabet, which employs adenine A (Figure 1) in preference to aA. The twoamino group in aA (the lower – NH2 unit, Figure 6) would be required to oppose association with iC. However, in an alphabet from which iC has been deselected, this proves superfluous, and the natural alphabet of A, C, G, and T is essentially degenerate with the optimal even-parity alphabet (a more detailed discussion of the argument maybe found in [27].)

The parity-code model simply requires that a nucleotide alphabet be composed of nucleotides of like parity. While the arguments above explain the particular composition of the even-parity alphabet, the possibility of an odd-parity alphabet is also admitted (Figures 6 and 10). It is difficult to state with any certainty why nature selected the even-parity solution, and it must be recognized that it may simply be an accident of evolution. Nevertheless, tautomeric instability offers a possible explanation; quantum chemical simulations at the PM3 semiempirical level of approximation suggest that of the oddparity letters depicted in Figure 6, α and β are tautomerically unstable (Figure 11) [30]. In fact, the tautomeric forms of β labeled β-T1 and β-T2 (Figure 11) are thermodynamically more stable than the reference form β. Moreover, just as in the JANUARY/FEBRUARY 2006

even-parity alphabet, the 000 motif, corresponding with the three lone pairs, nucleotide
, is vulnerable to hydrolysis. Thus, α, β, and
, together with their complements, are eliminated, and the viable odd-parity alphabet is limited to κ and X. Of the two competing parities, the even-parity set has four physicochemically viable members, each expressing 2 b/letter, compared to the odd-parity set containing just two viable letters and expressing 1 b/letter. The information necessary to express some biological functionality would be more succinctly expressed by the even-parity alphabet, with concomitant advantage in fidelity and efficiency, offering perhaps a partial explanation of why the natural alphabet is even parity. It should also be noted that the elements of the hypothetical oddparity alphabet depicted in Figure 6 are not definitive, and as the basis of tautomeric instability is not always self-evident, it is possible that analogues with equivalent recognition patterns, yet possessing desirable tautomeric properties, might be forthcoming. However, if we assume for the moment that the preliminary conclusions are sound, then there may exist elsewhere a primitive biology based on κ and X or their close analogues. We might expect that, possessing an information density of just 1 b/letter, development of a genetic code is less likely, and the system would remain trapped in an RNA world. Conclusions

In retrospect, a potential role for error coding in shaping the nucleotide alphabet seems obvious, and yet, with two notable exceptions, it appears to have been largely ignored; Szathmáry recognized the relationship between D/A patterns and replication errors [25], while Yockey implicitly implied a role for error coding in nucleotide transmission, assigning 5-b representations to nucleotides [26]. Unfortunately, these assignments had no physicochemical basis, being based on mapping the natural alphabet to a code employed in an error-coding text [27]. The model outlined in this paper melds these approaches, embracing a role for error coding, but one based on hydrogen/ lone-pair patterns. The attraction of the error-coding description is that it offers a strikingly simple explanation of nature’s choice of alphabet from among the set of potential nucleotides; optimal alphabets correspond to those in which the purine/pyrimidine feature relates to the D/A pattern as a parity bit. When this error-coding approach is coupled with chemical constraints, the natural alphabet of A, C, G, and T emerges as the optimal solution for nucleotides. Dónall A. Mac Dónaill is a senior lecturer in advanced materials in the School of Chemistry, in the University of Dublin, Trinity College, Ireland. He received his bachelor’s degree in chemistry in 1980 and completed his Ph.D. on the quantum chemical simulation of molecular solvation environments in 1984 in Trinity College. From 1984–1986 he was a postdoctoral fellow in chemistry in the University of Western Ontario, in London, Ontario, where he worked on the simulation of fast-ion conductors, with a particular interest in superionic oxides. In 1986, he took up a faculty position in the new degree program in materials science at Trinity College. In 1989–1990 he took a Hitachi Fellowship (HIVIPS) in Advanced Computation, at Hitachi Central Research Laboratory, Kokubunji, Tokyo, where he worked on the development of fast algorithms for application in materials IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

simulation. From 1996–2002 he was the first director of the computational chemistry degree program. During this same period, he became increasingly interest in the interpretation of molecular biological phenomena as computational processes. Address for Correspondence: Dónall A. Mac Dónaill, School of Chemistry, Trinity College, Dublin 2, Ireland. Phone: +353 1 608 1456. Fax: +353 1 671-2826, E-mail: [email protected]. References [1] A. Einstein, in Festschrift für Aurel Stodola. E. Honegger, Ed. Zürich: Orell Füssli Verlag, 1929, p. 126. [2] J.D. Watson and F.H.C Crick, “Molecular structure of nucleic acids,” Nature, vol. 171, pp. 737–738, 1953. [3] D. Nelkin and M.S. Lindee, The DNA Mystique: The Gene as Cultural Icon. New York: W.H. Freeman, 1995. [4] F.H.C. Crick, “Origin of the genetic code,” J. Mol. Biol. vol. 38, no. 3, pp. 367–379, 1968. [5] A. Eschenmoser, “Chemical etiology of nucleic acid structure,” Sci., vol. 284, no. 5423, pp. 2118–2124, 1999. [6] A. Eschenmoser, “Hexose nucleic acids,” Pure Applied Chemistry, vol. 65, no. 6, pp. 1179–1193, 1993. [7] N. Hall, “The quest for the chemical roots of life,” Chem. Comm. (11), pp. 1247–1252, 2004. [8] G. Springsteen and G.F. Joyce, “Selective derivatization and sequestration of ribose from a prebiotic mix,” J. Am. Chem. Soc., vol. 126, no. 31, pp. 9578–9583, 2004. [9] J.A. Piccirilli, T. Krauch, S.E. Moroney, and S.A. Benner, “Enzymatic incorporation of a new base pair into DNA and RNA extends the generic alphabet,” Nature, vol. 343, no. 6253, pp. 33–37, 1990. [10] C.Y. Switzer, S.E. Moroney, and S.A. Benner, “Enzymatic incorporation of a new base pair into DNA and RNA,” J. Am. Chem. Soc., vol. 111, no. 21, pp. 8322–8323 1989. [11] G. Zubay and T. Mui, “Prebiotic synthesis of nucleotides,” Origins Life Evol. Bio., vol. 31, no. 1–2, pp. 87–102, 2001. [12] K.M. Guckian, T.R. Krugh, and E.T. Kool, “Solution structure of a nonpolar, non-hydrogen-bonded base pair surrogate in DNA,” J. Amer. Chem. Soc., vol. 122, no. 29, pp. 6841–6847, 2000. [13] C. Roberts, R. Bandaru, and C. Switzer, “Theoretical and experimental study of isoguanine and isocytosine: Base pairing in an expanded genetic system,” J. Am. Chem. Soc., vol. 119, no. 20, pp. 4640–4649, 1997. [14] E. Szathmáry, “4 Letters in the genetic alphabet—a frozen evolutionary optimum,” in Proc. Roy. Soc. Lon Ser., 1991, vol. B 245, no. 1313, pp. 91–99. [15] W.R. Stahl and H.E. Goheen, “Molecular algorithms,” J. Theor, Biol., vol. 5, no. 2, pp. 266–287, 1963. [16] R.W. Hamming, “Error detecting and error correcting codes,” Bell Syst. Technical J., vol. 29, no. 2, pp. 147–160, 1950. [17] D. Forsdyke, “Are introns in-series error-detecting sequences?,” J. Theor. Biol., vol. 93, no. 4, pp. 861–866, 1981. [18] L.S. Liebovitch, Y. Tao, A.T. Todorov, and L. Levine, “Is there an errorcorrecting code in the base sequence of DNA?,” Biophys. J., vol. 71, no. 3, pp. 1539–1544, 1996. [19] J. Rzeszowska-Wolny, “Is genetic code error-correcting?,” J. Theor. Biol., vol. 104, no. 4, pp 701–702, 1983. [20] G. Cullmann and J.-M. Labouygues, “Noise immunity of the genetic code,” Biosyst., vol. 16, no. 1, pp. 9–29, 1983. [21] G. Battail, “Does information theory explain biological evolution?,” Europhys. Lett., vol. 40, no. 3, pp. 343–348, 1997. [22] D.A. Mac Dónaill, “A parity code interpretation of nucleotide alphabet composition,” Chem. Comm., no. 18, p. 2062–2063, 2002. [23] E.E. May, M.A. Vouk, D.L. Bitzer, and D.I. Rosnick, “Coding theory based models for protein translation initiation in prokaryotic organisms,” Biosyst., vol. 76, no. 1–3, pp. 249–260, 2004. [24] R. Dawkins, The Blind Watchmaker. Longman Scientific & Technical, 1986. [25] E. Szathmáry, “What is the optimum size for the genetic alphabet?,” in Proc. Natl. Acad. Sci., USA, vol. 89, no. 7, pp. 2614–2618, 1992. [26] H.P. Yockey, Information Theory and Molecular Biology. Cambridge, UK: Cambridge Univ. Press, p. 102, 1992. [27] D.A. Mac Dónaill, “Why nature chose A, C, G and U/T: An error-coding perspective of nucleotide alphabet composition,” Origins Life Evol. Bio., vol. 33 no. 4-5, pp. 433–455, Oct. 2003. [28] D.A. Mac Dónaill, “The concept of parity in nucleotides: Implications for the possible existence of alternative alphabets,” in Proc. 2nd European Workshop on Exo-Astrobiology, Graz, 2002, pp. 99–102. [29] D.A. Mac Dónaill and D. Brocklebank, “An ab initio quantum chemical investigation of the error-coding model of nucleotide alphabet composition,” Mol. Phys., vol. 101, no. 13, pp. 2755–2762, 2003. [30] D.A. Mac Dónaill, “Tautomerism as a constraint on the composition of alternative nucleotide alphabets,” Artificial Life VIII—Proc. 8th International Conference on Artificial Life, Sydney, 2002, pp. 106–110.

JANUARY/FEBRUARY 2006

61

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

Examining Coding Structure and Redundancy in DNA

© EYEWIRE

How Does DNA Protect Itself from Life’s Uncertainty?

BY GAIL L. ROSEN

he genetic code instructs proteins on the translation of nucleotides to amino acids, but this example is only one of many signals encoded in DNA. It is well known that these protein-coding regions have the lowest mutation rates in the DNA strand. So, the question arises: how does DNA protect itself from error? A review of DNA signal content, redundancy, and mutational mechanisms is presented. Then, mutation-robust methods are developed to detect a linear coding structure and approximate tandem repeats. Ever since the introduction of the Watson-Crick model of DNA, scientists have been trying to decipher the long sequence of millions (or, for complex organisms, billions) of bases. The genetic code, the mapping of nucleotide triplets (codons) to amino acids, or “protein-coding,” was one of the first discoveries. Signals in DNA could then be paralleled to digital signals. After 30 years, many functions and signals in DNA still remain unknown, and scientists have conjectured that nonprotein-coding regions, which compose 97% of human DNA, are unused junk [1]. On the contrary, recent studies reveal that binding sites and initiation signals exist in these nonprotein-coding areas, and mutation errors in these regions cause diseases [2]. Nonprotein-coding regions contain a finite amount of “algorithmic” content [1], [3]. Discovering the signals and function in these areas is just the beginning of genome discovery. In this article, we develop a method to uncover an errorcorrection coding structure in the nucleotide sequence, and show that our framework is efficient for detecting approximate tandem repeats, such as microsatellite regions.

T

AA and TT, are placed at certain phases from each other and cause an average periodicity of 10.55 ± 0.01 base pairs in the DNA sequence; AG and CT also aide to the helical twist [4]. A fascinating nucleotide series is the telomere, the end of the chromosome used to buffer genes from the environment. Due to the way the replication mechanism truncates, the DNA strand shortens each iteration; human DNA shortens by 50 base pairs (bp) on every cell division [5]. To prevent nucleotide loss from eventually interrupting a gene, telomerase elongates a chromosome’s ends with repetitive sequences such as TTAGGG, sometimes for thousands of bases [5]. As we age, telomerase expression weakens, genes no longer have protection from being cropped, and cells die. On the contrary, when telomerase is overexpressed, cells tend to live much longer and divide more frequently, resulting in cancer [6]. Ninety percent of tumoric growths exhibit excessive amounts of telomerase! Sequence periodicity and repeats play a vital role in the stability of the overall structure. Some DNA regions are correlated to specific functions or signals, and a famous function is that of protein coding, also known as the coined “genetic code.” These identified patterns and sites already give seemingly random DNA a clear deterministic structure. Schneider presents a comprehensive list of DNA signals recognizable by pattern and information content [7]. In our methods, we begin to examine the underlying redundancy and tandem repeats present in the nucleotide sequence. Mutations and the Replication Process

DNA Composition and Repeats

DNA is composed of four bases or nucleotides. A (adenine) and G (guanine) are considered purines (R), and T (thymine) and C (cytosine) are considered pyramidines (Y), with purines being the larger of the two. This size imbalance between nucleotides creates an affinity between purines and pyramidines, and stability is only reached with complementary pairing: A bonds to T (two weak hydrogen bonds) and C bonds to G (three weak hydrogen bonds). The weak complementary bonds make DNA easy to unzip in replication, but they can also make it susceptible to interfering molecules; thus for error protection in its stable state, the double strand curls into a helix. It has been found that certain nucleotide repeats help DNA to wrap into the curved state. The dinucleotides, 62 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Scientists give a rough error rate of 10−10 mutations/ nucleotides when DNA is copied. So, what are these mutations and how can we quantify them? Substitution mutations mostly occur due to 1) accidental bonding of Brownianmotioned biological elements to DNA or 2) electromagnetic radiation providing enough energy to break bonds in the structure. As an example of 1), one of the most common mutations is the hydrolysis of C to T, known as cytosine deamination. Water molecules do not have as easy an access to nucleotides in DNA’s stable helical structure as they do when DNA is unzipped for replication. In fact, cytosine deamination is 100 times more likely in replication [8]. Temperature, geometry, and environment are key factors in studying DNA mutation rates. 0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

In addition to errors/mutations caused by clumsy molecules bumping into DNA, replication itself (or the copying mechanism) can introduce errors that appear structured. For example, microsatellite regions, an excess of repetitive sequences, result from replication slippage [9]. Microsatellites in human DNA are associated with 14 neurodegenerative genetic disorders found in [2]. Repeats from telomerase slippage causes increased cell division and highly correlates with malignant cancer growth. The replication procedure alone has an error rate of 10−3 to 10−5 [8], but DNA has an internal “proofreading” mechanism. When copied, the helical structure unzips and forks into two separate strands; complementary bases then attach themselves to complete the new ladders. When a substitution error occurs, usually a purine replaces a purine (C → T) or a pyramidine replaces a pyramidine (A → G) in the complementary attachment. This causes a kink to develop due to the mismatch, and no more bases are added until the correct nucleotide is restored. This simple proofreading reduces the error rate to approximately 10−10 [8]. Can understanding these repair pathways lead to better error-correcting technologies? Overall, it is important for the computational biologist to be wary of the various mutational errors when examining DNA sequences.

A = 1 + j, T = 1 − j, G = −1 + j, and C = −1 − j [11]. The geometric interpretation of this representation still imposes a structure such that the Euclidean distance between A and C is greater than the distance between A and T, yet for the application, nucleotide quantization to amino acids, it is useful [11]. Various representations, including the one proposed in this article, can be seen in Table 1. Symbolic statistical techniques, using Markov models to represent the various nucleotide states, have been developed to predict gene sequences [14]. But a representation is needed that allows deterministic mathematical operations on a finite set of elements. A field has addition, multiplication, and their inverse operations (subtraction and division) unlike groups or rings [15]. If one wishes to have these four operations available to analyze a sequence of symbols, a finite field framework is preferred. In [13], we propose a mapping of nucleotides to a Galois field of four, noted as GF(4) [15]. Since GF(4) is an extension field of GF(2) (any GF(2) binary pair corresponds to one of four GF(4) symbols) , we can create labels (Table 2) for the nucleotide elements with GF(2)’s primitive polynomial:

Nucleotide Representation

The abstraction of elements to integer labels is an attractive property of the finite field representation. The polynomial in (1) can be manipulated in addition, multiplication, subtraction, and division in GF(4). Refer to [15] for a detailed derivation. For reference, we show the resulting addition and multiplication operation tables in Table 3.

When analyzing DNA, the mathematical representation of the nucleotides, {A, T, C, G}, is the fundamental first step. It has even been contemplated why nature chose such an alphabet in [10]. Many representations have been proposed and adapted to the type of analysis. For example, purines (A and G) and the pyrimidines (C and T) can be represented with a binary representation. In addition, a simple representation can be chosen for the four bases such as A = 0, G = 1, C = 2, T = 3 (modulo operations), but this implies a structure on the nucleotides such that T > A and C > G. For a model of the translation process, Anastassiou defines a complex representation to the nucleotides:

α 2 + α + 1 = 0.

(1)

Information Theoretic Studies

Inspired by information theory, Gatlin developed entropy and divergence measures to quantify complexity in DNA [16]. The entropy, or information capacity of a sequence, is maximized when all four nucleotides are equiprobable:

Table 1. Table of DNA mathematical representations found in the literature. An example sequence, GCATT, with its complement and characteristic property given for each representation. Example Sequence: G C A T T | | | | | A A T G C Simple integer

Complement Representation

Sequence GCATT

AATGC

Property

A = 0, G = 1, C = 2, T = 3

12033

00312

Uses modulo operations

A = 1 + j, G = −1 + j,

−1 + j, −1 − j,

1 + j, 1 + j, 1 − j,

Reverse and conjugate

C = −1 − j, T = 1 − j

1 + j, 1 − j, 1 − j

−1 + j, −1 − j

to get complement

A = −1.5, G = −0.5,

−0.5, 0.5, −1.5,

−1.5, −1.5, 1.5,

Reverse and negate to

C = 0.5, T = 0.5

1.5, 1.5

−0.5, 0.5

Si [n] = 1 where S[n] = i

A: 0 0 1 0 0

A: 1 1 0 0 0

Four-dimensional

Si [n] = 0 where S[n] = i

G: 1 0 0 0 0

G: 0 0 0 1 0

representation

C: 0 1 0 0 0

C: 0 0 0 0 1

T: 0 0 0 1 1

T: 0 0 1 0 0

12033

00312

assignment Complex assignment (QPSK) [13] PAM representation [13] Binary indicator sequence [12]

Galois field assignment [15]

A = 0, C = 1, T = 2, G = 3

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

get complement

Uses symbolic Galois field operations

JANUARY/FEBRUARY 2006

63

H=−




pi log2 (pi)

i

=−

4
1 i=1

4

log2

  1 = 2 bits. 4

(2)

code model is effective in recognizing the ribosomal binding site, and the convolutional model easily distinguishes between translated and untranslated sequences. May et al. show that coding models are effective in signal recognition and inspire us to ask whether there is an inherent coding structure in DNA. Determination of an Underlying Linear Code

In many species, the bases are not equiprobable, but temperature dependent. Three bonds exist in C and G bases, while only two exist in A and T. Thus, it takes more energy to break the bond between C and G, and it has been found that GC content is higher in a warmer-environment than colder-environment organisms. For example, Micrococcus Lysodeikticus, which inhabits warm spots, has the following base frequencies: Pr(C) = Pr(G) = 0.355 and Pr(A) = Pr(T) = 0.145 [16]. By way of (2), the entropy for this organism is 1.87 b and this nucleotide imbalance implies redundancy. A simple entropy measure like (2) indicates nucleotide bias in a sequence. In recent years, new measures have been developed such as entropic profiles of various-length genomes [17]. Schneider illustrates DNA nucleotide bias for each nucleotide position through an easy-to-read sequence logo graph [18]. Techniques for studying information content and bias have begun to quantify DNA’s implicit structure. In our work, we show how coding theory and signal processing methods can be used to investigate this structure. Coding Models of DNA

Since DNA is a finite, symbolic sequence, it is a natural to extend the use of coding theory to sequence analysis. Battail has stated that DNA evolves from a series of repeats heavily altered by mutation, such as ill-conserved introns, and he presents a replication decoding framework [19]. His “multiple unfaithful repetition” model only uses partial knowledge of the coding constraints in order to decode a message; this property makes the model attractive since little is known about the DNA encoding structure. Inspired by this model, we use partial knowledge methods in our work. Also, much research has been done by May et al. to study E. coli translation initiation sequences using block and convolutional coding models [20]–[23]. mRNA is viewed as a noisy encoded signal, and the ribosome, which translates the sequence, is seen as the decoder. Several biological and chemical factors are used to parameterize the ribosomal decoding model. The block Table 2. Exponential root representation, polynomial representation, numerical label, and nucleotide label for the GF(4) representation. α0 = 1 ⇔ 1 ⇔ C α1 = α ⇔ 2 ⇔ T α =α+1 ⇔3 ⇔G 2

0=0⇔0⇔A

Table 3. Addition and multiplication tables in GF(4). + 0 1 2 3

0 0 1 2 3

1 1 0 3 2

2 2 3 0 1

3 3 2 1 0

x 0 1 2 3

0 0 0 0 0

1 0 1 2 3

2 0 2 3 1

3 0 3 1 2

64 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

As discussed in the first section, DNA’s repair mechanism detects and fixes irregularities in the sequence and significantly reduces the error rate of the replication process. Also, the bonds between the complementary pairs and the shape of the strands introduce constraints on the sequence, making the sequence less random than previously thought. The genetic code introduces the strictest rules in regions where every three nucleotides produces an amino acid; the 64 nucleotide combinations correspond to 20 amino acids and imply inherent errorprotection. This has led Battail and others [13], [24] to consider the existence of error protection in the assembly of nucleotides and that there might be more to DNA repair than just polymerase detection of irregularities in the sequence. Is there a universal block or convolutional code in the sequence where the proofreading mechanism is the decoder? Already, Mac Dónaill has hypothesized that a parity check code is present in the chemical bonds of the four bases [10], providing a foundation for further investigations into sequencing coding structure. Liebovitch presents the first search for an error-correction code in DNA using a single parity-bit search method [24]. While his methodical coding-theory-based investigation does not reveal the presence of a consistent single parity-bit code, the experiment provides inspiration for future investigations and context for the complexity of the problem. Thus, there is a need for a general approach to find k-parity bits placed in any order in any n-size code to discern an (n, k) block coding structure from a DNA sequence. We introduce subspace partitioning (SP), developed from classical coding theory, as a way to search/test for such codes without prior knowledge of the n or k values, which are usually known in communication channel error-correction codes. In biology, we lack these values, thus we develop a novel, generalized method to look for any (n, k) block-coding structure. Second, we account for DNA frame shift mutations, which are also usually not an issue in telecommunications applications. Third, the symbolic framework of the Galois field allows the four different bases to be solely symbolic, as they are in nature. So, while our method is founded in error-correction code theory, we tailor it to our biological application. Modeling the Replication Channel

Communication channel models can be paralleled to DNA processes. In one doctrine, the channel is assumed to be the amino acid translation from nucleotide triplets [16]. In May et al., the channel is the actual replication process, and the DNA is the medium in which genetic information is transmitted from generation to generation [25]. The latter is good for mutation modeling since transcription and copying of DNA is a noisy process. From the first section, when the activity of the proofreading mechanism is blocked, replication error rates increase. This leads us to the hypothesis that there is a sequence coding structure to protect against replication noise. In Figure 1, we assume that the DNA is the sequenced genomic data available in GenBank [26] and that our goal is to examine the dashed-line-encompassed area and uncover the encoder scheme; in other words, we wish to infer structure from JANUARY/FEBRUARY 2006

the noisy output to retrieve the original genetic inforNoise mation. Also, if our assumption is correct and Replication DNA is encoded in a linGenetic Translation to DNA Channel early redundant fashion, DNA Information Amino Acids Encoder our analysis will uncover it. In this system, we know nothing about the encoder Decoder or the original information; therefore, system identification and deconvolution Fig. 1. Our noisy channel model of genome replication with underlying coding assumption. methods cannot be used. We will assume that the encoder is linear and try to characterize it given such output. Framing Offset

Subspace Partitioning for (N, K) Codes

In our investigation [13], our primary goal is to identify and characterize any linear constraints that might appear in regions of a sequence. Lacking the benefit or prior knowledge regarding the location, duration, or dimensionality of subspace partitioning in the sequence, we propose a method that generates a complete orthogonal basis set oriented to a local region of data. The basis set is used to decompose the sequence (equivalent to a coordinate transformation). The consistent presence of nulls in the transformed sequence indicates both the presence and the dimension of linear subspace partitioning in the data. The first assumption is a fixed codeword length n. The N*nlength DNA sequence is grouped into a matrix, V = [νν 1 ν 2 . . . ν N ] where ν i is the ith column vector of length n. The alignment of the frames relative to the starting point will be referred to as the framing offset. A choice of a particular framing offset will be referred to as the frameset. Given the frame length n, there are n unique framesets. See Figure 2 for an illustration of all frameset groupings. We apply the Gram-Schmidt algorithm using finite field operations to the sequence of vectors to yield a complete set of orthogonal basis vectors, {e1 , e2 , . . . en }. Once an orthogonal basis is formed from the first j frames of data, the ν i ’s for i > j are decomposed into components of each of the basis vectors. This is simply a coordinate transformation and can be described by:



t i = G νi

where

 e1  e2   G=  ...  . en

Provided that the data has been framed correctly when applying the Gram-Schmidt algorithm, a linear coding redundancy can be detected by noting consistent null coordinates over a region in the transformed sequence of length-n vectors, {t1 , t2 , . . . , tN− j}. This null detection would indicate whether a subspace of the actual n-dimensional space exists.

Subspace Partitioning Algorithm Outline

1) Obtain the orthonormal basis {e1 , e2 , . . . en } by GramSchmidt orthogonalization of j number of νi frames where j ≥ n. Form the transform matrix G from this set. 2) Decompose the sequence into its basis components, {t1 , t2 , . . . , tN− j} across all possible framing offsets. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

0 1 2

GTAGTCGAATGTCATTGCTGAT ... [GTA][GTC][GAA][TGT][CAT][TGC]... [TAG][TCG][AAT][GTC][ATT][GCT]... [AGT][CGA][ATG][TCA][TTG][CTG]...

Fig. 2. Illustration of vector framing for n = 3.

3) Note the persistence of nulls in ti ’s. Calculate confidence by comparing against the probability of sequential sets of randomly chosen vectors having the same subspace partitioning. Given the copious volume of data produced by iterating the algorithm over numerous frame shifts and codeword lengths, a visualization method is devised to aid in the search for consistent subspace partitioning. A probabilistically based value increases to indicate confidence in the presence of subspace partitioning. We can then plot the confidence as a function of sequence index i across all possible framing offsets. Results of the Subspace Partitioning Method

The algorithm is capable of detecting and characterizing linear subspace partitioning in any sequence provided that such a structure is manifest in the data. For a given sequence, all such structures can be found provided that the algorithm is run for every possible framing offset and for every possible codeword length. By way of illustration, a test sequence is generated to occupy a five-dimensional subspace of an eight-dimensional vector space. This constitutes an (8, 5) linear block code in GF(4). Running the algorithm on this sequence for n = 8 yields the confidence image shown in Figure 3(a). Interstitial symbols are introduced throughout the sequence to illustrate the robustness of the algorithm to framing offsets. When frame shift mutations occur, the region of subspace consistency simply migrates to the corresponding row in the diagram. The linear SP algorithm is then tested using an E. coli K-12 MG1655 sequence (GenBank [26] accession code NC_000913). The result is shown in Figure 3(b). A consistent linear block code is not observed to be present throughout the whole sequence, but some regions are oriented in the same subspace for several consecutive frames, denoted by the aggregated intensity of the light bars. The SP algorithm requires two conditions from the sequence. Firstly, the algorithm uses nulls in t to indicate subspace partitioning. This requires that the coordinate system G to be properly JANUARY/FEBRUARY 2006

65

oriented. So, the sequence has to have the same coding present throughout; otherwise, the vector basis will be misaligned and may make even a regional coding structure impossible to detect. The algorithm can be generally applied to any sequence for which it is suspected that coding properties are present. It identifies a block code in a symbol sequence independent of framing, provided that the structure is present from the outset. The algorithm could readily be adapted in a classification scheme for data of unknown origin or for cryptographic/cryptanalysis tasks in which the code or encryption scheme is unknown. Redundancy and Tandem Repeat Detection

From structural studies, we know DNA (especially eukaryotic) has repetitive regions. There are various techniques to classify these [27]–[29]. Most tandem repeat algorithms use complex heuristic, combinatorial, or dynamic programming approaches. In [28], a periodicity transform is used to plot several periodic/near-periodic regions versus position in a simple graph. It is one of the most flexible algorithms (by

using different detection thresholds) and efficient representations (periodicities versus nucleotide position), but only base substitution mutations, not frame shift mutations, are taken into account. Now with a nucleotide representation and field-defined arithmetic operations, we can extend the linear algebraic techniques used in the SP method to detect periodicities. To analyze redundancy, we develop a method, the linear dependence (LD) test, to search for localized regions of linear dependence in sequence data. The LD test indicates the mere existence of a subspace, while the subspace partitioning method from the previous section tells us the subspace’s orientation. Biologically speaking, the SP method tests for strict block-coding structure, while the LD test detects a “rough” redundancy, such as an approximate repeat. If we can determine that a subspace exists and is present for a portion of the data, we can use this as a starting point for further examination of its orientation (as explored in [13]). The LD test determines local redundant regions and is a good starting point for further analysis such as the detection of tandem repeats. Linear Dependence Test

0 1

Frame Offset

2 3 4 5 6 7 50

100

150 Frame # (a)

200

250

0

In the LD method [30], an N 2 -length window of the data is reshaped as an N × N matrix. This matrix occupies a maximum of N-dimensions. In the linear dependence test, the rank of each N × N window is computed to find its dimensional occupancy; the rank computation is based on a recursive Gaussian-elimination algorithm [31] modified for GF(4) arithmetic. Then the data is incremented by an N-length frame each iteration, thereby creating a slowly moving N × N window which moves by N nucleotides each time until the entire sequence has been traversed. A weight, I, is incremented, I = I + 1, on each iteration if rank deficiency is found in consecutive window segments. The outline of the LD technique: 1) For analysis frame length N, collect N consecutive vectors to form N × N window. 2) Perform a rank computation of the N × N matrix. 3) Increment by one frame for each iteration.

20 2

40

3

Offset

Frame Offset

1

4 5 50

60 80

100 120 100

Frame # (b)

150

200

Fig. 3. (a) Linear subspace partitioning results for a (8, 5) block-coding test data. It detects a block code despite interstitial frame shift mutations. (b) Linear subspace partitioning results for a subsection of an n = 6 E. coli K-12 MG1655 sequence. Regional block codes are detected, but there is no evidence of a universal code.

66 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

134 500

1,000 Frame #

1,500

Fig. 4. N = 135 LD test for the Yeast Chromosome I sequence, NC_001133. Intensity increases proportionally to the length and level of the rank deficiency of consecutive N × N windows, each starting at a particular frame number. Two regions associated with the FLO9 gene are shown to be highly repetitive with the LD Test.

JANUARY/FEBRUARY 2006

5

5

10

Offset

Offset

10

15

15

20

23

18 20

40 Frame #

60

10

80

20

30

40

50

Frame #

Fig. 5. N = 19 LD test for a human satellite sequence, HSVDJSAT. Intensity increases with length and level of the rank deficiency. At offset 6, an 893-base region exhibits a 19base repeat.

Fig. 6. N = 24 LD test for HSVDJSAT. At offset 12, a 1,200 base region exhibits a 24-base repetition. This is longer than the N = 19 case. Even though this region has high mutation, the algorithm still finds the redundancy.

4) Note consistent rank-deficiency by incrementing I. By itself, this method is a measure of regional linear dependence and finds variation in dimensional occupancy between overlapping windows.

In [27], it is found that the HSVDJSAT sequence, a repetitive satellite region of 1,985 bases in the human genome, has a tandem repeat of 19 bases from 1,195–1,553. The LD test highlights the tandem repeat across all framesets in Figure 5. While the strong repeat is from 1,150–1,728, a longer redundant region starting around base 900 is detected with an offset of 6. Therefore, the LD test can find the longest periodic region by testing all frame shifts. Exhaustively running the algorithm over various N, a strong periodicity of 24 bases is discovered (as seen in Figure 6). At an offset of 12, there is a periodic region of over 1,100 bases, which is longer than the periodicity found in the N = 19 runs. Hauth has recently reported a periodicity of 48 from 1,190–1,553 [32],

Linear Dependence Test Results

In the first section, we discuss the vital role that nucleotide repeats play in chromosome buffering and neurodegenerative disorders. In this section, we show how the LD test is effective in finding tandem repeats, especially those that are highly altered by mutational errors. Using the online Genbank database [26], we select the Yeast Chromosome I sequence (accession code: NC_001133) and a human satellite region (accession code: HSVDJSAT) for our experimental data. We introduce a way to highlight periodic regions across all frame offsets to ease visual inspection of periodicities. In Figures 4–6, the x-axis values correspond to the frame_numbers (0 to sequence_length/N – N), and the y-axis denotes our algorithm running for all N frame offsets needed to test all possible groupings of the data (see Figure 2 for an illustration). If an insertion or deletion occurs and effectively shifts a repetitive portion forward or backward by a few bases, the highlighted segment will still be shown but in another frame offset since all frame shifts are examined. If an N − 1 rank subspace is found, it is denoted in dark gray, and the lower the rank of the subspace (up to N − 4 for the examples), the brighter the intensity; also, the higher the linear dependence persistence indicator I, the brighter the shading intensity. Therefore, the brightness of the graph is a function of two factors: the strength and length of the redundant region. First, the algorithm was run on the Yeast Chromosome I sequence which can be seen in Figure 4. In Figure 4, two notable redundant regions of over 17,000 bases are found to have a periodicity of N = 135. The number of bases producing a highlighted region can be calculated from the graph by number_of_frames × N + (N × N). Even though the regions are only rank deficient by one or two dimensions (not a strong linear dependence), the frames of nucleotides are almost identical to each other, indicating a 135-base tandem repeat. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Fig. 7. Annotation of an N = 48 HSVDJSAT region (bases 1,141 1,976). The annotation scheme used in Figure 7 is used here. An approximate repeat can be seen among insertion and deletion errors. Uppercase letters denote conserved portions, underlined letters denote an insertion from the previous frame, and bold letters denotes a region retained after/around a deletion occurring from the previous frame. Italics denote a region before a deletion, lowercase letters denote substitution errors/sequence differences, and lightgray letters denote portions where multiple base substitutions occur for a particular base.

→

JANUARY/FEBRUARY 2006

67

but with most tandem repeat algorithms, the N = 24 periodicity or multiples is difficult to find. For example, the maximal tandem repetition (mreps) 2.5 algorithm [29] did not yield a 24-base periodicity (or multiples of 24) for this sequence. This may be from the lack of exact repeats present. To illustrate, a portion of the HSVDJSAT region is shown in Figure 7, and no two frames are equivalent because of mutational errors. For current tandem repeat algorithms, this is a problem because they are based on exact frequencies, but our algorithm detects approximate repeats and, therefore, can easily identify near-periodic regions. In Figure 7, the lowercase and light gray nucleotides show regions where the nucleotides may have mutated to other nucleotides (known as substitution errors) in replication. The light gray bases are interesting because they represent substitution of one or more nucleotides, usually dinucleotides, and also occur quite often in this example. The LD algorithm does not search for exact repeats or matching patterns. Instead, the rank-deficiency of the nucleotide window indicates similar structures, or redundancy, between the segments. Despite these errors, which throw other algorithms astray, the LD algorithm easily detected the periodicity and multiples of 24 as seen in Figure 7. Conclusions

The subspace partitioning method is based on the hypothesis that there is an underlying coding structure in DNA used for error recovery in replication, but our preliminary results do not indicate a universal block code. In our method, we assume consistent error correction would occur in both protein-coding and nonprotein-coding regions. On the contrary, mutation rates vary from region to region in the genome, and these areas may need separate treatment. For example, nonproteincoding regions are more susceptible to mutation than proteincoding regions. In the linear dependence test, we develop an algorithm which finds near-periodic DNA regions, common to genetic disorders, in a fast iterative process. In addition, we show that using a finite-field framework enables the use of linear algebra’s massive toolbox. Two sequences are analyzed via the LD algorithm, and expected tandem repeats are found in each. An unexpected approximate repeat of 24 bases is found in the HSVDJSAT sequence. The discovery is due to the algorithm’s ability to detect redundancy amidst an abundance of mutation that other algorithms do not tolerate. The linear dependence test is a simple way to find imperfect periodicities and remains robust in substitution, deletion, and insertion errors. Acknowledgments

This article is based upon work supported under a National Science Foundation Graduate Research Fellowship and an AT&T Research Laboratories Grant. The author would like to thank Jeffrey D. Moore, now at Khimetrics, Inc., for invaluable discussions and his contribution to subspace partitioning method. Gail Rosen received both a B.S. (highest honors) and M.S. in electrical engineering from the Georgia Institute of Technology (Georgia Tech), in 1999 and 2002, respectively. Since 2002, she has been pursuing a Ph.D. in electrical engineering at Georgia Tech’s Center for Signal and Imaging Processing (CSIP). 68 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

She is a recipient of numerous awards, including a National Science Foundation (NSF) Graduate Research Fellowship, an NSF STEP Fellowship, an AT&T Research Laboratories Grant, and a Georgia Tech ECE Outstanding Teaching Award. Her main research interests are reverse-engineering biological systems and analyzing DNA structure, mutations, and repair. Address for Correspondence: Gail Rosen, Center for Signal and Image Processing, Georgia Institute of Technology, Atlanta, GA 30332-0250 USA. E-mail: [email protected]. References [1] Y.K. Huen, “Brief comments on junk DNA: Is it really junk?” Complexity Int., vol. 9, pp. 1–12, 2002. [2] R.R. Sinden, V.N. Potaman, E.A. Oussatcheva, C.E. pearson, Y.L. Lyubchenko, and L.S. Shlyakhtenko, “Triplet repeat DNA structures and human genetic disease: Dynamic mutations from dynamic DNA,” J. Biosci., vol. 27, no. 1, Feb. 2002. [3] N. Stambuk, “On circular coding properties of gene and protein sequences,” Croatia Chemica Acta, vol. 72, no. 4, pp. 999–1008, 1999. [4] E.N. Trifonov, “3-, 10.5-, 200-, and 400-base periodcities in genome sequences,” Physica A, vol. 249, no. 1–4, pp. 511–516, 1998. [5] B. Windle, “Telomerase: Target of immortality” [Online]. Available: http://www.people.vcu.edu/~bwindle/Telomerase/telomerase.html [6] W. Hahn. “Telomerase and cancer: Where and when?” Clinical Cancer Res., vol. 7, no. 10, pp. 2953–2954, Oct. 2001. [7] T.D. Schneider, G.D. Stormo, L. Gold, and A. Ehrenfeucht, “Information content of binding sites on nucleotide sequences,” J. Mol. Biol., vol. 188, no. 3, pp. 415–431, 1986. [8] R.H. Burdon, Genes and the Environment. Philadelphia, PA: Taylor and Francis, 1999. [9] D. Metzgar, J. Bytof, and C. Wills, “Selection against frameshift mutations limits. Microsatellite expansion in coding DNA,” Genome Res., vol. 10, no. 1, pp. 72–80, 2000. [10] D.A. Mac Donaill, “The role of error-coding in shaping the nucleotide alphabet: Nature’s choice of A, U, C, and G,” in Proc. IEEE EMBS Intl. Conf. Special Session Commun. Theory, Coding Theory Molecular Biol., pp. 3850–3853, 2003. [11] D. Anastassiou, “Genomic signal processing,” IEEE Signal Processing Mag., pp 8–20, July 2001. [12] N. Chakravarthy, A. Spanias, L.D. Iasemidis, and K. Tsakalis, “Autoregressive modeling and feature analysis of DNA sequences,” Eurasip Journal on Applied Signal Processing, vol. 1, pp. 13–28, 2004.< [13] G.L. Rosen and J.D. Moore, “Investigation of coding structure in DNA,” IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Apr. 2003. [14] “GeneMark: A family of gene prediction programs” [Online]. Available: http://opal.biology.gatech.edu/GeneMark/ [15] S.B. Wicker, Error Control Systems. Upper Saddle River, NJ: Prentice Hall, 1995. [16] L.L. Gatlin, Information Theory and the Living System. New York: Columbia Univ. Press, 1972. [17] R. Ramon-Roldan, P. Bernaola-Galvan, and J.L. Oliver, “Application of information theory to DNA sequence analysis: A review,” Pattern Recognition, vol. 29, no. 7, pp. 1187–1194, 1996. [18] T.D. Schneider, “Some lessons for molecular biology from information theory,” Entropy Measures, Maximum Entropy Principle and Emerging Applications. New York: Springer-Verlag, 2003, pp. 229–237. [19] G. Battail, “Replication decoding revisitied,” in Proc. IEEE Information Theory Workshop, Apr. 2003. [20] E.E. May, M.A. Vouk, D.L. Bitzer, and D.I. Rosnick, “An error-correcting code framework for genetic sequence analysis,” J. Franklin Instit., vol. 341, no 1–2, pp. 89–109, Jan.–Mar. 2004. [21] E.E. May, M.A. Vouk, D.L. Bitzer, and D.I. Rosnick, “Coding theory based models for protein translation initiation in prokaryotic organisms,” in Proc. Fifth Int. Workshop Information Processing Cells Tissues (IPCAT), 2003. [22] E.E. May, “Towards a biological coding theory discipline,” New Thesis, vol. 1, no. 1, pp. 19–37, 2004. [23] D.C. Schmidt and E.E. May, “Visualizing ECC properties of E. coli K-12 translation initiation sites,” in Proc. 2nd IEEE Workshop Genomic Signal Processing Statistics, 2004. [24] L.S. Liebovitch, Y. Tao, A.T. Todorov, and L. Levine, “Is there an error correcting code in the DNA?” Biophysical J., vol. 71, no. 3, pp. 1539–1544, 1996. [25] E.E. May, “Analysis of coding theory based models for initiating protein translation in prokaryotic organisms,” Ph.D. dissertation, NC State Univ., 2002. [26] GenBank, “National Center for Biotechnology Database” [Online]. Available: http://www. ncbi.nlm.nih.gov [27] A. Hauth, “Identification of tandem repeats simple and complex pattern structures in DNA,” Ph.D. dissertation, Univ. of Madison,WI, 2002. [28] M. Buchner and S. Janjarasjitt. “Detection and visualization of tandem repeats in DNA sequences,” IEEE Trans. Signal Processing, vol. 51, no. 9, Sep. 2003. [29] R. Kolpakov, G. Bana, and G. Kucherov, “mreps Tandem Repeat Finder” [Online]. Available: http://www.loria.fr/mreps/. [30] G.L. Rosen, “Finding near-periodic DNA regions using a finite-field framework,” in Proc. 2nd IEEE Workshop Genomic Signal Processing Stat., May 2004. [31] G.H. Golub and C.F. Van Loan, Matrix Computations, Baltimore, MD: Johns Hopkins Univ. Press, 1996. [32] A. Hauth [Online]. Available: http://www.cs.wisc.edu/gensoft/beyondTR/static/HSVDJSAT.html

JANUARY/FEBRUARY 2006

Error Correction and Detection in DNA © EYEWIRE

BY DIEGO LUIS GONZALEZ, SIMONE GIANNERINI, AND RODOLFO ROSA

n this article, we investigate the possible existence of errordetection/correction mechanisms in the genetic machinery by means of a recently proposed coding strategy [11]. On this basis, we numerically code exons, creating binary parity strings and successively we study their dependence structure by means of rigorous statistical methods (moving block bootstrap, and a new entropy-based method). The results show that parity sequences display complex dependence patterns enforcing the hypothesis of the existence of deterministic error-correction mechanisms grounded on this particular parity coding.

I

Introduction

The rules governing the translation of RNA sequences into proteins were discovered some 40 years ago [1]. The universal biochemical translation table known as the genetic code connects two different chemical worlds: that of the nucleic acids with that of the biologically active proteins. The identification of this table with a code (a man-made system implementing symbolic representation for communication purposes) represents an early perception of the deep connection between biological information and coding theory. Coding theory is a research area intimately related to information and communication systems theory; for this reason, the introduction of tools from these related fields has represented a natural and inevitable step in the study of genetic information flow [2]. Information theory aims at giving a theoretical framework to processes characterized by some kind of information exchange. The main practical problem faced by information theory, and, in particular, by communication systems theory, is that of the transmission of reliable information through unreliable channels. To this aim, error detection and correction using appropriate coding and decoding methods represent a crucial step. All the methods of error-control coding are based on the adding of redundancy to the transmitted information. As the genetic information is redundant (evident, for example, in the case of short repeats inside introns), and since the genetic code is also redundant itself (intrinsic redundancy in the coding of amino acids), the possible existence of error-control mechanisms represents a somehow natural hypothesis [2]–[9] related to the biological task of ensuring a high degree of reliability in the transmission and expression of genetic information. In literature, such a hypothesis has been explored from a IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

constructive point of view by proposing possible coding strategies that may be used in the organization of genetic information [6]–[8] and also from a statistical point of view, that is, by studying the dependence between symbols for revealing the existence of underlying coding mechanisms [5]. In this article we investigate this problem from a point of view that takes into account, to some extent, the two above mentioned approaches. The article is composed of two main parts. In the first part (“Parity Coding, Error Corrections, and DNA” and “A Mathematical Theory for the Genetic Code”), we present a new mathematical theory of the genetic code that leads to a coding strategy of codons and amino acids showing very interesting mathematical properties from the point of view of coding theory. This first part represents a constructivist part of the article in the sense that the natural coding we propose suggests the existence of error-control/correction mechanisms operating on the basis of its intrinsic mathematical structure. In the second part (“Exploring the Structure of Binary Sequences” and “Results: Analysis of DNA Binary Sequences”), we study the statistical properties of real coding sequences with methods tailored for the study of binary sequences. The output of the aforementioned coding is a binary string obtained by sequentially appending 6-b words (binary words of 6 bits length) representing codons. As it will be shown below, these 6-b words possess well-defined parity properties; therefore, a simplified version of the coding can be obtained by replacing the 6-b words with 1-b words containing only the parity information (recall that the parity of the 6-b string can be defined as the parity of the summation of its symbols: an even number of ones leads to an even string, an odd number of ones to an odd string). The possible existence of an error-correction mechanism based on this parity coding is explored on an empirical basis. This hypothesis necessarily implies that the binary symbols exhibit some structure of dependence. The search for such structure of dependence by means of rigorous statistical methods is thus the scope of the second part of this article. The sections are structured as follows: in the second section we discuss the goal of the article and where the work fits into the state of the art on coding theory applied to the study of biological information flow. 0739-5175/04/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

Detecting Structure in Parity Binary Sequences

69

The new theory for the genetic code is based on a nonunivocal representation of whole numbers by means of the so-called nonpower binary bases.

In the third section, we describe in some detail the new mathematical theory of the genetic code [10], [11]; this theory is based on the representation of natural numbers by means of the so-called nonpower binary number representation systems. The theory describes the degeneracy of the genetic code and allows a 6-b binary characterization of codons. Moreover, hidden symmetry properties of the genetic code are highlighted. Also, the role of the parity of the 6-b representation of codons is shown. On this basis, we describe how to numerically code exons (protein coding regions of DNA) and obtain parity strings, i.e., strings formed by attaching a parity symbol to every codon in a given sequence. This parity coding represents the starting point for the statistical analysis performed in the following sections. In the fourth section, we describe two advanced statistical methods for the study of the dependence structure in binary strings, i.e., the moving block bootstrap (MBB) [12] and an entropy-based dependence metric (the normalized Bhattacharya-Hellinger-Matusita distance) [13]. In the fifth section, we show the results of applying the methods described in section four to binary parity strings. These strings are obtained from the coding described in section three to the protein-coding sequences x80497 (phosphorylase kinase, Homo sapiens) and AF017114 (glycogen synthase mRNA, Oryctolagus cuniculus). The possible significance of these statistical results is discussed in the last section. The results suggest that the intrinsic redundancy present in the genetic code, i.e., the existence of synonymous codons, can be used to encode additional information for error control and correction. Should this hypothesis be proven correct, the theoretical description of redundancy in the actual genetic code would represent a key point in elucidating the genetic mechanism acting on this basis. In the conclusion section, we report on implications and suggestions together with future research directions prompted by the results obtained by combining the novel mathematical theory for the genetic code and rigorous statistical methods. Parity Coding, Error Correction, and DNA

We have mentioned the analogies between the genetic machinery and communication processes. Basically, a communication process is characterized by three main subprocesses: the coding of the information to be communicated, the transmission of the information along the communication channel, and the decoding of the information at the receiver. Usually, it is in the communication channel that unwanted errors are introduced. In man-made communication systems, the coding and decoding steps are tailored in such a way that detection and correction of the errors introduced in the communication channel can be achieved. Indeed, this is the main purpose of communication theory, i.e., to transmit reliable 70 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

information through unreliable channels. Different authors have modeled the genetic information flow in the framework of communication theory ([2], [6], [14], [15]). For example, in [16] a detailed view is given including transcription, translation initiation, and translation elongation in the decoding step, while replication is considered as the main process related to the transmission channel. Even if a thorough analysis in terms of communication theory is not the main aim of this article, we make use of some general features that every communication system, including the genetic one, must possess. From this point of view, we need to remark that it is very difficult to identify a coding step in the genetic system. The information is transmitted along different genetic processes (replication, transcription, and translation) as it is determined at its source, the double helix of DNA (following the central dogma of molecular biology, no additional information is produced in these steps); that is, the information arises already in coded format. Ignorance about these ab initio coding rules implies ignorance about the constraints imposed by these rules on the decoding mechanism. Our point of view in this regard is a pragmatic one. We know that genetic information is coded; exons can be decoded following the rules of the genetic code (introns or intergenic regions also may convey biological information, but we do not know a general decoding rule). Moreover, we know that the genetic code is redundant; that is, a given amino acid may be decoded starting from more than one different codon. Thus, we have a decoding table—the genetic code—and a redundancy associated to this table, the main ingredients needed to implement an error-correction mechanism. Hence, the question naturally arises: Can an error-correction mechanism be implemented on this basis? To this regard, we need to distinguish between robustness to errors and error correction. The robustness to errors of the genetic code has long been recognized; a random error produced in a particular codon leads to the same amino acid or to some similar one from the point of view of physico-chemical properties. But this robustness is not due to an error-correction mechanism. It must be remarked that the natural robustness of the genetic code against errors does not impose any constraint in the redundancy distribution. Instead, an error-correction mechanism, implies the organization of the redundancy in a mathematically structured way (usually following the properties of finite groups). Thus, one of the crucial points for the existence of error-correction mechanisms is the existence of a mathematical structure in the coded data or, equivalently for the genetic case, a mathematical structure in the genetic code evidencing the mathematical structure in the data to be decoded. The other crucial point concerns the redundancy: How is redundancy encoded in the genetic information? The existence of an error-correction mechanism automatically implies the JANUARY/FEBRUARY 2006

existence of dependencies between the symbols representing the information. In this sense, our work is based on these two premises: the founding in the genetic code of a strong mathematical organization and the study of the dependence of the data produced by coding real data on the basis of this mathematical organization. In literature, a few different mathematical models of the genetic code, mainly dealing with the description of the first level of degeneracy (the distribution of the number of synonymous codons), have been proposed [17]–[20]. Our approach is radically different because it is the unique model that describes exactly the first level of degeneracy of the genetic code and gives a deep insight into the second level of degeneracy (the association between specific codons and specific amino acids). Moreover, the approach reveals many surprising numeric and symmetry properties of the genetic code, as explained in the next section. To the authors’ knowledge, this is the unique mathematical model based on a nonpower binary representation of natural numbers. A different model, proposed in [19], is based on number representations and on the number of nucleons on amino acids’ side chains and uses the digital system and a modulus equivalence. In literature, the structure of dependence has been studied mainly with the aim of identifying protein-coding regions, that is, discerning them from intronic and intergenic noncoding regions of DNA (see, for example, [21]). The main aim of this article is somehow different from existing approaches since, as previously stated, we want to investigate the existence of error-correction mechanisms suggested by the strong mathematical structure found in the genetic code. However, the results can be also interesting in relation to the study of longrange correlations in genetic data. In fact, as shown in the next section, the 6-b coding (and, consequently, also the parity coding) is not a fixed binary coding, that is, any of the four bases is not represented by a fixed 2-b number. In fact, the four bases of DNA are usually coded by a twodigit binary number [22]. For example, we can assign to thymine/uracil (T/U) the binary string (0,0), to cytosine (C) (0,1), to adenine (A) (1,0), and to guanine (G) (1,1) (see also [23]). This assignation is necessarily arbitrary as there is no reason to assign to T the string (1,0) instead of (0,0) and so on. Some researchers assign such numbers taking into account the chemical properties of the base (such as the purine or pyrimidine character) sometimes reducing the binary dimension of the representation [21]. Also, in this case, the assignation is arbitrary since this only shifts the problem: Why assign 1 to purine and 0 to pyrimidine and not the reverse? or why not use the other possible partition of bases as keto and amino or strong and weak? For the coding of the four bases (T, C, A, G) in a triplet, there is a total of 24 possible fixed 2-b different choices or six different choices if the representation is 1-b (in fact robustness against fixed code choice has been tested for some alternatives regarding this last case [21]). Our approach, on the contrary, provides a natural strategy for the numerical coding of bases because it takes into account the degeneracy properties of the genetic code. Moreover, codons can be characterized by a parity bit in a nontrivial way, i.e., this characterization cannot be obtained with any of the fixed assignations mentioned above; the numerical assignation in the nonpower binary number representation is not fixed but context dependent. Because of the uniqueness of the numerical values of the IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

basis describing the code degeneracy in the nonpower binary representation, we denote it for simplicity as the genetic code like binary representation (GCL binary representation). The existence of correlations associated to this coding has not been investigated before. A comparative study between this and former approaches may give a deeper view about the origin of correlations in the genomic data. In literature, the use of parity coding at the genetic level has been suggested in different contexts [3], [7], [8], [10], [11]. In the approach described in [7], it is shown that a parity coding is actually working at a chemical level for the selection of complementary bases in DNA. Such an approach is relevant in an evolutionary context in order to explain the actual use of complementary bases in modern DNA. Our approach suggests a similar coding acting along the double helix of DNA, where biologically meaningful information is encoded; of course, the same coding can be applied to both the DNA and the messenger RNA (mRNA) by simply swapping T and U. Clearly, if some error-control mechanism is present, the redundant information cannot be stochastically independent; crucial information about the relative dependence of different symbols can be obtained by statistical methods. The GCL coding gives a natural parity assignation for codons; therefore, in the second part of the article, we study the dependence properties of binary parity sequences obtained from sequences of DNA or mRNA. This statistical information represents a necessary step in order to crack any eventual error-control mechanism based on this specific coding and may be useful also in order to gain understanding about the organization of the genetic information along nucleic acid molecules (for example, the existence of long-range correlations). A Mathematical Theory for the Genetic Code

From a mathematical point of view, the new theory for the genetic code is based on a nonunivocal representation of whole numbers by means of the so-called nonpower binary bases. Contrary to the usual number representation systems, that is, systems in which the positional values grow as the powers of some base (for example, the powers of 10 in the usual decimal power representation), nonpower refers to the fact that the positional values of the representation system grow more slowly than the power of some basis (the powers of 2 in the nonpower binary systems). It is shown that there exists a unique nonpower set of positional bases (called a genetic code like nonpower binary representation system) capable of explaining all the logical properties associated with the degeneracy of the genetic code viewed as a generic correspondence or mapping between two sets with a different number of elements (the 64 possible codons formed by all the combinations of four letters, T/U, C, A, G, and the 20 amino acids plus the stop signal). Table 1. Degeneracy distribution for the standard genetic code. Degeneracy

Amino Acids (#)

6

3

4

5

3

2

2

9

1

2

JANUARY/FEBRUARY 2006

71

If some error-control mechanism is present, the redundant information cannot be stochastically independent.

Due to the redundancy of the code, some elements of the codon set are necessarily mapped to the same element of the amino acids set. In Table 1 we show the actual distribution of the codons that codify the same amino acid in the standard genetic code. Such a table represents the first level of degeneracy. Besides the description that refers to this first level of degeneracy, called the degeneracy distribution, it is important to account for the distribution of codons and amino acids inside the degeneracy distribution since each possesses a precise physicochemical and biological identity. The nonpower representation theory of the genetic code also provides a deep insight into this second level of degeneracy, that is, the specific codon and amino acid assignation inside the given degeneracy distribution. The theoretical representation proposed here is also able to disclose the presence of a hidden symmetry of the genetic code, the so-called palindromic symmetry. This symmetry is related to the existence of degeneracy-preserving transformation rules which associate amino acids in pairs (pairs of palindromic amino acids). Moreover, numerical elements in the nonpower representation can be associated with biochemical elements in the genetic code. This association uncovers another hidden property of the genetic code: individual codons are codified in parity, i.e., a parity bit can be assigned to every codon. This last property is very appealing since it can be related to the possible existence of error-correction mechanisms in the genetic machinery. Parity coding is one of the simplest and most widely used strategies for error control and correction in man made digital communication systems. From the point of view of set theory, the genetic code is a correspondence or mapping between two sets of different cardinality: the 64 codons formed by all the possible combinations of the four bases, U, C, A, G, and the 20 amino acids plus the stop signal. The correspondence defines the starting and arriving sets; that is, an arrow points from a given element of the codons set (starting set) to a corresponding element in the amino acids set (arriving set). The direction of the arrows is compatible with the central dogma of molecular biology; that is, the genetic information flow is only from nucleic acids to proteins. The different cardinality of the startTable 2. Degeneracy distribution inside quartets for the Euplotid nuclear variant of the genetic code. Degeneracy

Amino Acids (#)

4

8

3

2

2

12

1

2

72 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

ing and arriving sets implies the redundancy and degeneracy properties of the code. Our aim is to build a mathematical structure that possesses the degeneracy properties of the genetic code from a logical point of view. In other words, we aim at creating a structural isomorphism. First, we define the properties of the genetic code from the point of view of set theory: the code is a surjective, noninjective correspondence (that is, not one-to-many) between two sets of different cardinality. The surjective property assures that no elements in the arriving set are vacant (all amino acids and the stop signal are coded by at least one codon). The noninjective property refers to the fact that some elements in the arriving set are represented by more than one element in the starting set (some amino acids are represented by more than one codon). This property viewed from the point of view of the arriving set is called degeneracy: a given amino acid does not uniquely specify the codon that originated it. From a mathematical point of view, this implies that the correspondence is not invertible. In biological terms, this affirmation is equivalent to saying that, given a coding sequence, we can know the corresponding sequence of amino acids defining a particular protein; but given a particular protein, we do not know the specific sequence of codons that codify it at the mRNA or DNA levels. The noninjective property viewed from the starting set point of view implies the concept of redundancy: different elements of the starting set codify for the same element in the arriving set (different codons codify the same amino acid). The redundant elements are called in this biological case synonymous codons. The “not one-to-many” property means that a specific codon cannot codify for more than one amino acid; this is a true statement for a given variant of the code and excludes possible contex-dependent translation oddities. Theoretically, the properties we have just defined can identify infinite correspondences between sets of different cardinality. We must now define a correspondence that takes into account the actual cardinal numbers of the genetic code. For this purpose, we first need to define degeneracy distribution, that is, a table (see Table 1) where we report all the degeneracy values actually found in the code (left) along with the corresponding number of amino acids that share such a degeneracy (right). For different reasons (for example, the symmetry properties of the code or some oddities in the characteristics of the degeneracy-6 group of amino acids), different authors (see [11] and references therein) have considered the degeneracy distribution inside quartets of the genetic code, that is, inside groups of four codons sharing their first two letters (for example, the quartet UGU, UGC, UGA, UGG). From a mathematical point of view, this is equivalent to enlarging the arriving set of 21 elements to 24 elements as follows: the degeneracy-6 amino acids are split into two elements, the first represented JANUARY/FEBRUARY 2006

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

12 0 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 1 0 0 1 0 1 0 R Arg 4 L Leu 4 4 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 1 0 0 1 0

0

0

10 1 0

11 1 0

1

13

14 0 1 1 1 0 1

0 1 1 0 1 1 0 1 0

1 1 0 0 1 1

1 0 1 0 1 0 0 1

0 1 0 1 1 1

1 0 0

0 0 1 1

1 1 0 1

1 0 A Ala

G Gly V Val

P Pro 4

4 1 0 0 0 1 1

0 1 0 0 1 1 0

1 0

0 1

1 0

0 0 1

0 1 0

1 0 0

1 1 0 1 0

1 0 0 1 0

0 0

0 1 0 9

1

0 0 1 1 I Ile

T Thr S Ser 4 4

C Cys 0 0

0 0

0 0

0 0 1

0 1 1

0 0 1

1 0 0 0 1

0 1 0 0 0

1 0 1

0 0 1 0 8

1 0 0 7

1

1 0 0

0 1 0 0 1

0 0 1 1 0

0 0 1

0

0 0 6

1

0 0 5

1

1

1 1 1

1 0 0 0 0

0 0 0 0 0

1 0 1

0 1 0 0 4

0 0 0 3

1

0 0 1 1 1 1

3

E Glu

D Asp H His

Q Gln 2

2

R Arg 2

N Asn Y Tyr

L Leu 2 2

2

K Lys

S Ser 2 F Phe

Ter 2

2 1

1 0 1

0 0 0 0 0

0 0 0 0 0

1 0 0

1 0 0 0 2

0 0 0 1

0

15

16 1 1 0 0 0 1

0 1 1 1 1 1 1 0 1

0 1 0 1 1 0

1 0 1 0 1 1 1 0

0 1 1 1 1 1

0 0

1

17

18 1 1 0 1 0 1

1 1 0 0 1 1 1 0 0

1 1 0 1 1 0

1 1 0

20

19 1 1 0 1 1 1

1 1 1 0 0 1 0 1 0

0 0 0 1 1 1

1 1 1

21

22 1 1 1 1 0 1

1 1 1 0 1 1 1 0 0

1 1 0 1 1 1

1 1 1

23

# 8 7 4 2 1 1

1 1 1 1 1 1

2 1 1 8 7 4 7 4 2 1 1 8 1 1 4 2 8 7 M Met W Trp 1

Degen. # Pairs of Palindromic 8 7 4 2 1 1 1 1 2 4 8 7 1 2 1 8 7 4 1 1

0 0 0

2 4

0

8 7

0 0

#

0

Table 3. Representation of the first 24 whole numbers (outer columns) in the GCL nonpower representation [1 1 2 4 7 8] (length-6 binary strings, horizontal rows). The degeneracy number (the number of binary strings that represent the same whole number) and the corresponding amino acids are shown in the center of the table. Notice that the table is symmetric (palindromic symmetry) and that the amino acids are associated in pairs (pairs of palindromic amino acids). The color (either light gray or dark gray) indicates the parity of each string (odd and even, respectively).

by four codons and the second by two of them. In this way the 20 amino acids plus the stop signal are represented by 24 different sets. For reasons that will become clear below, this is the representation which we aim at describing here. The degeneracy distribution obtained from the genetic code correspondence between the two sets of 64 and 24 elements is shown in Table 2 for the euplotid nuclear variant of the code, which differs from the standard one only in the assignation of the TGA codon (cysteine instead of stop). Now, we describe the mathematical model that enables us to build a structural isomorphism having the same mathematical structure as the genetic code. The theoretical framework is that of number theory and, in particular, the theory of integer number representation. Usual power positional representation systems are based on an additive process in which the powers of a given base are multiplied for the positional digits and added together in order to obtain a given integer. In the decimal representation system the powers of 10 are used. The digits can range from 0 to n−1, in this case, 0 to 9. For instance, the number 735 can be obtained as 75 = 5 × 100 + 3 × 101 + 7 × 102 . The fact that the digits are limited to the value of n−1, ensures the one-to-one character of the representation: a number is represented by only one combination of digits and vice versa. However, we are interested in redundant representation systems; redundancy can be obtained in two ways, allowing for the digits to go over their range or decreasing to some extent the values of the positional numbers (the ordered powers of the given base for power representation systems). We use this second possibility starting with the power representation system with the lowest integer base, i.e., 2, which defines the binary positional system. Thus in our system we assign to the positional numbers different values that grow more slowly than the powers of two. In particular, by taking the following set of positional values: [1 1 2 4 7 8], we can reproduce exactly the degeneracy distribution of the genetic code presented in Table 2 (for details see [11]). At this point we have found a structural isomorphism between two correspondences: on one side we have codons in the starting set and amino acids in the arriving set, and on the other side, we have six-digit binary strings in the starting set and 24 whole numbers in the arriving one. The scheme describes perfectly the degeneracy properties of the genetic code. Nevertheless, it is well known that the identity of biological elements matters: arbitrary permutations of codons inside a given degeneracy distribution are not equivalent from a biological point of view. Surprisingly though, on the basis of the analysis of the symmetry properties on both sides of the structural isomorphism, we can relate in a natural fashion numerical elements of the no-power representation to biochemical elements of the genetic code. In this way, codons are mapped into 6-b binary strings, just like amino acids are mapped into integer numbers. The details of this mapping have been reported in [11] and are summarized in Table 3. The table shows the representation of the first 24 whole numbers in the GCL nonpower binary system. Each whole number is represented by a set of length-6 binary strings, for example, the number 6 is represented by the strings (001011) and (001100). 0∗8+0∗7+1∗4+0∗2+1∗1+1∗1= In fact, 0 ∗ 8 + 0 ∗ 7 + 1 ∗ 4 + 1 ∗ 2 + 0 ∗ 1 + 0 ∗ 1 = 6 . The amino acid corresponding to a given whole number is shown in the central columns of the table. Observe that the number of binary

JANUARY/FEBRUARY 2006

73

strings representing a given whole number corresponds to the degeneracy of the assigned amino acid describing exactly the degeneracy distribution of the genetic code. Two major consequences arising from this approach are in order: 1) the uncovering of a hidden symmetry inside the genetic code, i.e., the palindromic symmetry, and 2) the natural classification of codons in definite parity classes. In Table 4, we show graphically the palindromic symmetry that maps quartets, preserving the degeneracy distribution; in Table 5, we show the parity distribution of codons. Every codon is assigned a parity bit which corresponds to the parity of the length-6 binary string shown in Table 3. Note that, as remarked above, the parity of the binary string can be computed by summating its symbols: an even number of ones leads to

Table 4. Graphical representation of the palindromic symmetry. All the quartets—defined by the same two first letters (bases) of codons—are associated in pairs by the palindromic transformation. Arrows of the same color indicate a common operation at the triplet level.

T

C

A

G

T C A G

Table 5. Graphical representation of the parity classification of triplets. Light gray boxes indicate odd triplets, dark gray boxes indicate even triplets. The parity of a codon corresponds to the parity of the length-6 binary string that represents it in the GCL nonpower representation (see Table 3).

T

C

A

G

T

C

TTT Phe

TCT Ser

TAT Tyr

A

TGT Cys

G T

TTC Phe

TCC Ser

TAC Tyr

TGC Cys

C

TTA Leu

TCA Ser

TAA Stop

TGA Cys

A

TTG Leu

TCG Ser

TAG Stop

TGG Trp

G

CTT Leu

CCT Pro

CAT His

CGT Arg

T

CTC Leu

CCC Pro

CAT His

CGC Arg

C

CTA Leu

CCA Pro

CAA Gln

CGA Arg

A

CTG Leu

CCG Pro

CAG Gln

CGG Arg

G

ATT Ile

ACT Thr

AAT Asn

AGT Ser

T

ATC Ile

ACC Thr

AAC Asn

AGC Ser

C

ATA Ile

ACA Thr

AAA Lys

AGA Arg

A

ATG Met

ACG Thr

AAG Lys

AGG Arg

G

GTT Val

GCT Ala

GAT Asp

GGT Gly

T

GTC Val

GCC Ala

GAC Asp

GGC Gly

C

GTA Val

GCA Ala

GAA Glu

GGA Gly

A

GTG Val

GCG Ala

GAG Glu

GGG Gly

G

74 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

an even string and an odd number of ones to an odd string. We can observe that the parity bit can be also derived easily on the biochemical side by means of two complementary rules applied to codons: if the codon ends with a purine base (A or G), the parity is determined by such base (that is, an A defines an odd codon, and a G defines an even codon); if instead, the codon ends with a pyrimidine base (U or C), the parity bit is determined by the second letter of the codon (i.e., C or A in the second position give an even codon, and U or G determines an odd codon). As we have remarked above, in this article we focus mainly on the parity coding because of its connection with hypothetical error-control mechanisms. Hence, in the next section we try to fathom this hypothesis by analyzing the dependence structure of parity sequences obtained through the application of the nonpower parity bit assignation as shown in Table 5. Exploring the Structure of Binary Sequences

In the bioinformatics literature, the dependence structure of DNA sequences has been investigated in several studies, even though a rigorous statistical approach is not always followed. For an excellent statistics-oriented review on the topic see [24]. In order to investigate the dependence structure of DNA parity sequences, we exploit statistical methods that are appropriate for the analysis of dependent data. In the next section, we will discuss and motivate the use of such methods in our context. In particular, we will give a reliable estimate of the standard error and related confidence intervals for the proportion p of zeros in the sequences. Moreover, as shown above, we are able to assess quantitatively the presence of possible dependencies in the data. In order to investigate further such dependence, we will introduce a metric based on entropy as a relevant tool for characterizing the structure of DNA sequences. Bootstrap Methods for Dependent Data

In this section, we outline a brief sketch about bootstrap methods. Such techniques introduced by Efron in 1979 [25] and described more fully in [26] are intensive computational procedures based on resampling from the observed data. Let the observed sample xn = (x1 , x2 . . . , xn ) be a realization of random vector Xn = (X1 , X2 . . . , Xn ) having an unknown underlying distribution function F. Let θ be the unknown parameter of interest (for example, the mean, median, correlation coefficient, etc.), θ(Xn ) be an estimator of θ , and θ(xn ) be an estimate based on the observed sample xn . The bootstrap gives a somewhat “automatic” nonparametric method for providing an approximation of the unknown distribution of θ(Xn ), in particular for estimating its standard error, denoted with σ (θ). In the following, we will denote the estimate of σ (θ) with σˆ (θ) or simply σ . Notice that even though F is known, assessing the accuracy of an estimate is often a difficult task, except for rather simple cases. The basic idea of the bootstrap is to resample the original data xn and make inference from the resamples. All this requires the following steps: 1) estimate F by Fˆ , the empirical distribution function, obtained by putting probability mass 1/n on each xn ; 2) generate a bootstrap sample x∗n = (x1∗ , x2∗ . . . , xn∗ ) from Fˆ by making independent random draws with replacement from the data; 3) compute the bootstrap replication θ ∗ = θ ∗ (xn ), that is, the value of the statistics pertaining to the bootstrap sample x∗n ; and 4) repeat the second and third steps B times to obtain B JANUARY/FEBRUARY 2006

bootstrap replications whose distribution approximates the distribution of θ(Xn ). The estimate of the standard error σ (θ) is approximated by the bootstrap standard error σˆ (θ ∗ ), or simply σˆ ∗ , given by: 1/2
B  (θˆb∗ − θ¯b∗ )2 ∗ σˆ = , (1) B−1 b=1 where θ¯ ∗ =

B ˆ∗  θ b

b=1

B

.

that observations more than l apart are nearly statistically independent. In this way q = n − l + 1 “moving blocks” are obtained (Q1 , . . . , Qq), where the ith block Qi with starting point Xi contains l observations: Qi = (Xi, Xi+1 . . . , Xi+l−1 ). From these q blocks Qi(i = 1, . . . , q), we draw at random with replacement h blocks, with h × l = n. The h selected blocks, placed one after the other, form the new sequence Q∗ = (Q∗1 , . . . , Q∗h ). Analogously to the IID-bootstrap, we can form a suitable number of MBB replications Q∗ from

0.028

σ 2 = Var[X¯ n ] =

0.024 σˆ*

If the observations are correlated, that is, they can no longer be considered realizations of mutually independent random variables with the same distribution function F, the bootstrap is not applicable in the form outlined above, since the dependence structure of the data is disregarded. So, the “classical” bootstrap for independent and identically distributed (IID) variables, call it an IID-bootstrap, must be replaced by the moving block bootstrap (MBB), which resamples not individual observations but blocks of observations. The MBB allows one to assign measures of accuracy to statistical estimates for dependent observations in the form of finite time series. This problem is discussed also in [27], here we recall summarily some basic points. Consider the stationary time series Xn = µ, σ02 , γk (X1 , X2 , . . . , Xn ) . Let and ρk k = (1, . . . , n − 1) be the mean, variance, covariance, and autocorrelation function of Xn , respectively. Note that γ0 = σ02 and ρk = γk /γ0 The variance of the estimator X¯ n of µ, is given by

0.022 0.020 0.018 0.016 0

20

40

60 80 100 Block Length

120

140

Fig. 1. Moving block bootstrap estimates σˆ ∗ of the standard error of p as a function of the block length l, computed for the sequence AF017114 (coding region) (n = 736, filled symbols): c1: in frame; c2, c3: out of frame. Results pertaining to IID binomial sequences having the same proportion p as the original data are reported in empty symbols (c1_iid, c2_iid, c3_iid).

n−1  σ02 n−k γk +2 n n2 k=1


 n=1  σ02 n−k 1+2 ρk = n n k=1

c1_iid c2_iid c3_iid

c1 c2 c3

0.026

(2)

Since it will be useful in the following, we recall that in statistical mechanics the variance of Xn is usually written as (see, e.g., [28])   2τ , (3) σ 2 = σ02 1 + δt where τ is the integrated correlation time ∞ τ = ρ(t)dt,

each of which the statistic of interest is computed, and the bootstrap estimate of the standard error σˆ ∗ is derived through (1). The idea of the MBB is due to [30] and was studied theoretically in [12] and [31]. In practice, by varying l, one sees that when l is small the MBB estimate σˆ ∗ is close to σ0 because the scheme does not manage to reproduce the correlation structure at lag > l, which is present in the original data. With increasing l, the data belonging to different blocks become more and more independent of one another until the blocks are actually IID random variables under the MBB scheme, and at the same time, inside each block the correlation is retained. In the presence of a positive (negative) correlation in the series, the plot of σˆ ∗ vs l shows an increase (decrease) of σˆ ∗ [see (2)] up to a region, call it a plateau, in which the variations are less pronounced (see Figure 1 below for the application to DNA series). The reaching of the plateau indicates: 1) a suitable choice for l, 2) the MBB estimate σˆ ∗ of σ , and 3) the “strength” of the correlation, as derived from (3).

0

and δt is the time interval between two successive observations. We will show through the MBB how it is possible to estimate σ 2 directly and derive from it an estimate for τ through (3). For a comparison of various approaches for estimating σ 2 , one can see also [29]. In order to estimate σ 2 , the MBB considers in a chain of n observations all possible contiguous blocks of length l such IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

A Dependence Metric Based on Entropy

In the literature, there are many proposals of dependence measures, each of them motivated by different needs and built to characterize a specific aspect of the process under study. An important class of such measures is based on entropy functionals developed within information theory (see, for example, [32] and the references therein). For JANUARY/FEBRUARY 2006

75

instance, Shannon mutual information has spread widely in the context of nonlinear dynamics [33] as well as time series analysis [34]. However, none of these entropies define a metric since either they do not obey the triangular inequality or they are not commutative operators. Also, there have been recent studies in the statistics community with the aim of describing the properties that an ideal measure of dependence should possess (see, for example, [13] and the references therein). For these reasons, we have adopted the metric entropy measure Sρ (k), a normalized version of the Bhattacharya- HellingerMatusita distance, defined as follows: 1 Sρ (k) = 2


+∞
+∞

1  1  1 Pr{Xt = i, Xt+k = j} 2 i=0 j=0 2  − Pr{Xt = i}Pr{Xt+k = j} .

(5)

Here, the probabilities have been estimated in a nonparametric fashion by means of relative frequencies, and the confidence bands at the 95% level under the null hypothesis of independence have been obtained through Monte Carlo simulation. Results: Analysis of DNA Binary Sequences

f(Xt ,Xt+k ) (x1 , x2 )

−∞ −∞

 2 − fXt (x1 ) fXt+κ (x2 ) dx1 dx2 ,

(4)

where fXt (·) and f(Xt ,Xt +k (·, ·) denote the probability density function of X t and of the vector (X t , X t+k ), respectively. The measure is in precise relation to other entropy functionals, such as Shannon entropy and Kullback-Leibler divergence, and can be interpreted as a nonlinear autocorrelation function. Sρ (k) satisfies many desirable properties: 1) it is a metric and is defined for both continuous and discrete variables; 2) it is normalized and takes the value 0 if Xt and X t+k are independent and takes the value of 1 if there is a measurable exact (nonlinear) relationship between the variables; 3) it reduces to the linear autocorrelation function in the case of Gaussian variables; and, notably, 4) it is invariant with respect to continuous, strictly increasing transformations. Among other things, [13] addresses the issues of nonparametric kernel estimation of Sρ (k) and of its utilization in the context of hypothesis testing of serial dependence. The measure has been proven to have impressive and robust power for characterizing nonlinear processes. In the case of binary series the measure becomes

250 200 Replications

Sρ (k) =

In the following we show the results obtained from the application of the methods described in the previous section to several DNA parity sequences, derived from both codons and anticodons, each of them considered both in-frame and outframe. Here, by anticodon we mean the complementary triplet in the Watson-Crick sense. Since parity is defined by the second and/or the third letter in the codon, the parity sequence associated to the anticodons, which are read in reverse order, carries completely different information. For this reason, we have chosen to analyze the anticodon sequences also. Moreover, as redundant information can be codified along the sequences in unknown ways, it is also interesting to study the out of frame versions of both codon and anticodon sequences. Hence, from each codon we obtain and analyze six sequences. In the following c1 (frame) and c2 (+1), c3 (−1) (out-frame) refer to the codon sequence, whereas a1 (frame) and a2 (+1), a3 (−1)(out-frame) refer to the anticodon sequence. Notice that the length of the sequence n refers to the codon sequence so that it has to be multiplied by three to obtain the length of the base sequence. As remarked above, the studies where DNA sequences are rendered dichotomous rely on somehow arbitrary choices for the mechanism of dichotomization, without an underlying model. In our case, such a procedure is encompassed naturally within the approach presented in the previous sections so that we expect the results to be well informative. In the following, we will always refer to the protein-coding part of the DNA sequences. Also, in this article, we will mainly concentrate on the statistical aspects arising from the analysis of parity sequences. Further investigations, including a comparison with noncoding portions, are in progress and will be reported in a future work.

150

The Moving Block Bootstrap 100

50

0

65

70 Number of 0 (%)

75

Fig. 2. Histograms of 1,000 moving block bootstrap replications of the proportion p for the c1 codon sequence AF017114 (coding region) (continuous line) and for IID binomial sequences (dotted line). The block length has been set to l = 80. 76 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

First, we apply the MBB in order to 1) obtain confidence intervals for the proportion p of zeros in the sequence and 2) investigate the dependence in the sequence. Recall that, for independent data, i.e., realizations of IID binomial variables, the standard error of the estimator of p is given by  p(1 ˆ − p)/n, ˆ where pˆ denotes an estimate of p. In this instance, both the IID-bootstrap and the MBB give the same results. However, if there is some form of dependence, the MBB is able to reveal it and, at the same time, estimate the “true” standard error σ . Figure 1 (filled symbols) shows the behavior of the MBB estimates σˆ ∗ of the standard errors of the estimator of p as a function of the block length l for the parity sequence AF017114 [35] (coding region), codon in frame JANUARY/FEBRUARY 2006

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

the IID binomial hypothesis ([C.I. 95]0 ) and, correctly, through the MBB [C.I. 95]*. It is important to note that even though in this instance the confidence intervals under the IID assumption do not differ markedly from the MBB intervals, the latter approach is the correct one for assessing the accuracy in the presence of dependent data without making distributional assumptions. For instance, assume we wish to test the hypothesis that the proportion p is the same for c1 and c2 at the 99% significance level. On the basis of an IID confidence interval, one would erroneously reject such a hypothesis. On the contrary, the hypothesis is not rejected if the MBB is employed.

0.024 0.022

ˆ* σ

0.020

a1 a2 a3

0.018

a1_iid a2_iid a3_iid

0.016 0.014 0.012

0

20

40

60 80 100 Block Length

120

140

Fig. 3. Moving block bootstrap estimates σˆ ∗ of the standard error of p as a function of the block length l, computed for the anticodon sequence AF017114 (coding region) (n = 736, filled symbols): a1: in frame; a2, a3: out of frame. Results pertaining to IID binomial sequences having the same proportion p as the original data are reported in empty symbols (a1_iid, a2_iid, a3_iid).

300 250

Replications

(c1) and out of frame (c2 (+1) , c3 (−1)). We also report the results obtained by applying the MBB on binomial IID sequences having the same proportions p of zeros as the observed data (empty symbols). The bootstrap replications B are 1,000 in all cases. If the data were independent there would be no statistical difference in the standard errors estimates between observed and IID sequences. The values of pˆ are 0.702,0.603,0.448 for c1, c2, and c3, respectively. The values of σˆ 0 are 0.017 (c1) and 0.018 (c2, c3). At first sight, there is a clear difference between the results from the observed and the IID sequences, revealing the presence of a kind of dependence in the data. For the IID sequences, in fact, σˆ ∗ always remains close to σˆ 0 while significant increases of σˆ ∗ are displayed for the observed sequences. Let us follow the trend referring, as an example, to c1. At the beginning σˆ ∗ is very close to σˆ 0 as expected. With increasing l, σˆ ∗ grows. After l ≈70, σˆ ∗ reaches a plateau. On the plateau, the actual dependence structure of data is captured, and the value found for σˆ ∗ may be retained as an estimate for the standard error σ . Here, it results σˆ ∗ = 0.023 By replacing σˆ 0 and σˆ ∗ in (3), it follows that the integrated correlation time is τˆ = 0.41. Similar trends hold also for c2 and c3, for which τˆ values are 0.25 and 0.39, respectively. It has been proved [12], [31] that σˆ ∗ is a consistent estimator of σ if l grows to infinity with n, provided that l/n → 0. In practice, as shown in [29], the MBB enables one to assign accuracy even though the number of blocks is rather small, say, n/ l ≈ 10, so that the last points of Figure 1, corresponding to 4-6 blocks, are not reliable. As remarked above, bootstrap methods can assess more than standard errors. For instance, we report in Figure 2 the bootstrap distributions of the estimator of p of the proportion of zeros with B = 1, 000 for the IID-sequence (dotted line) and the observed sequence (continuous line) taken at l = 80 for the c1 sequence. As expected from the central limit theorem, both distributions are Gaussian with the same mean, but the difference in the variance is clearly visible. Such a difference can be assessed easily through a test on the variances that results significant. The trends of Figures 1 and 3 show clearly that the difference between IID and MBB is due to the autocorrelation of the sequence [the second term between square brackets in (2)]. Notice that, in general, one can build confidence intervals from bootstrap distributions without having to make normal theory assumptions (see [26] for a complete discussion on this point). We have tested several DNA sequences. In most cases, the trends of σˆ ∗ vs l are qualitatively similar to those reported in Figure 1, that is, a rise of σˆ ∗ as l increases. However, some sequences reveal no dependence; that is, σˆ ∗ remain always close to σˆ 0 , while other sequences display a decrease of σˆ ∗ as l increases, as shown in Figure 3 for the anticodon a1 of the AF017114 sequence (coding region). The decrease of σˆ ∗ up to the plateau around l ≈ 80 − 100 indicates that in this instance the correlation is negative. In analogy with Figure 2, we report in Figure 4 the MBB distribution of the estimators of p (l = 90). In this instance, the MBB distribution has a variability that is smaller than that of IID case, so the MBB confidence interval will be more accurate. In Table 6 we summarize the results obtained by the MBB for the sequence AF017114 (coding region). In the columns are reported the sequence name, the estimate p, ˆ σˆ 0 , σˆ ∗ and 95% confidence interval for p, obtained under

200 150 100 50 0 40

45 50 Number of 0 (%)

Fig. 4. Histograms of 1,000 moving block bootstrap replications of the proportion p for the a1 codon sequence AF017114 (coding region) (continuous line) and for IID binomial sequences (dotted line). The block length has been set to l = 80.

JANUARY/FEBRUARY 2006

77

and the distribution of the distances between them seem to conform to a kind of regularity that may be gene specific, as also σˆ ∗ pˆ σˆ 0 Seq. AF017114 [C.I. 95]* [C.I. 95]0 shown in a recent study on human c1 (0) 0.702 0.017 0.023 [0.670, 0.736] [0.654, 0.744] chromosomes [37]. The long c2 (+1) 0.602 0.018 0.022 [0.566, 0.637] [0.557, 0.643] memory content seems to be less pronounced in out-frame c3 (−1) 0.448 0.018 0.024 [0.409, 0.483] [0.401, 0.492] sequences, as shown in Figure 6. a1 (0) 0.470 0.018 0.012 [0.435, 0.505] [0.446, 0.494] Notice, however, that the peaks a2 (+1) 0.739 0.016 0.021 [0.706, 0.770] [0.697, 0.781] show a significant dependent structure also in this instance. a3 (−1) 0.458 0.018 0.022 [0.423, 0.491] [0.417, 0.503] In Figures 7 and 8, we show the results of the computation of Sρ (k) upon the anticodon sequence The Entropy-Based Dependence Metric X80497 in frame and out frame, respectively. In this The plot of Figure 5 shows the entropy-based dependence instance, the situation seems somehow reverted if compared metric Sρ (k) versus the lag k = 1, . . . , 300 computed on with the results for the codon. In fact, the long-range depenthe coding sequence of the gene X80497 [36], n = 1, 236 dence here is more evident for out-frame than for in-frame codons, in frame. We recall that the measure can be intersequences. Notice also the large peak at lag 1 for the first of preted as a nonlinear autocorrelation function; that is, if the two out-frame sequences [Figure 8 )]. In any case, the Sρ (k) exceeds the confidence band at lag k, then there is a anticodon parity series also shows a significant dependence significant correlation between symbols that are distant k structure at several lags that cannot be attributed to statistical steps in the sequence. As in the previous section, by signififluctuations. cant we mean consistently different from IID processes. The findings reported above show clearly that the Here and in the following, in order to obtain good estimates entropy-based metric has been able to disclose the exisfor Sρ (k), we chose a number of lags which is approximatetence of a nontrivial dependence structure in DNA parity ly one quarter of the length of the sequence as is well sequences. Further investigations along this line will known in time series analysis. Two remarkable aspects include testing for nonlinearity by exploiting, for instance, emerge clearly from the inspection of the figure. First, the surrogate data methods, a class of Monte Carlo tests aimed appearance of a kind of long-range dependence, starting at building distribution-free hypothesis testing for nonlinfrom about lag 100. Second, the presence of several peaks ear time series (for a review on the topic see [38]). that extend well over the confidence band, indicating the Another important topic is to assess how the correlation possible presence of periodicities. The strength of the peaks structure of DNA sequences depends on the repetition of certain patterns throughout the sequence. Also in this case, it is possible to employ a suitable modification of surrogate data methods in order to build several statistical hypotheses in a straightforward manner. A similar task is pursued in [37], although the authors do not build statistical tests; rather, they seem to make comparisons on the basis of a single sequence rather than building a randomization distribution. 0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012

Sp(k)

Table 6. Summary statistics and confidence intervals ([C.I. 95]) for the sequence AF017114 obtained under the IID binomial hypothesis (0 ) and through the MBB (*).

Conclusions

0

50

100

150 Lag k

200

250

300

Fig. 5. Sρ (k), k = 1, . . . , 300 for the codon sequence X80497 in frame c1, n = 1, 236. The confidence band at the 95% level (dashed line) was obtained through Monte Carlo generation of 3,000 Bernoulli IID sequences.

78 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

In this article, we have employed a novel mathematical theory for the genetic code in order to test the hypothesis that some error-control mechanism based on parity coding may be active inside the genetic machinery. We have used this particular model mainly because ➤ it describes completely the degeneracy distribution of the genetic code ➤ it uncovers strong numeric and symmetry properties (TC degeneracy in the third letter, complement to 1 palindromy, coding of the third letter, etc.) ➤ it gives a natural coding method for parity characterization of codons ➤ there are not alternative nonpower representations describing the code degeneracy (the set of nonpower basis, [1 1 2 4 7 8], is unique). Returning to error correction, it is known that different biochemical error-control systems are actually working at the level of amino acid translation in the ribosome, for JANUARY/FEBRUARY 2006

0.0015 0.0010 50

100

150 Lag k

200

250

300

0.0000

0.0005

Sp(k)

Sp(k)

0 e+00 1 e−04 2 e−04 3 e−04 4 e−04 5 e−04 6 e−04 7 e−0.

0

0

50

100

150

200

250

300

Lag k

(a)

(b)

Fig. 6. Sρ (k), k = 1, . . . , 300 for out-frame sequences of the gene X80497 c2 (a) and c3 (b), n = 1, 235. The confidence band at the 95% level (dashed line) was obtained through Monte Carlo generation of 3,000 Bernoulli IID sequences.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

0 e+00 2 e−04 4 e−04 6 e−04 8 e−04 1 e−03

to study in the future is how one or a few sequences are selected as the good ones in the ocean of equally valid possibilities. Is this choice related to some organizational principles in the genetic information such as error control and correction? For different reasons, our approach seems to point to an affirmative answer to this question.

Sp(k)

example, stopping a frame-shift reading [39]. However, many aspects of error control are poorly understood and the very low error rates associated with different genetic processes are difficult to be described theoretically outside a mathematical framework. Error discrimination and correction can be performed only if some mathematical template is available (of course, in the case of genetic processes, this template may represent some privileged state in terms of chemical energy exchange). Moreover, it is somehow accepted that the genetic code itself has gained its actual form thanks to its self-correcting capabilities [40]. However, this error-minimizing ability of the genetic code is usually studied within a probabilistic approach since random mutations either do not modify or minimally modify a synthesized protein. In this context, the decoding of an exon is kept immune to random mutations due to the particular structure of the genetic code itself (see [41] for some controversial related issues). For a given protein, mutations can be viewed as little deviations from a coding template. Robustness of the protein synthesis is related to the fact that mutations usually modify a codon either into a synonymous codon (without noticeable changes in the protein) or into a codon codifying for a chemically similar amino acid (introducing minimal changes in the protein structure). However, the most striking fact is the existence of a given template, that is, the reason why nature has preferred a particular sequence of codons in order to codify a particular protein. A relatively short protein 100 amino acids long can be represented in 3100 (the average degeneracy per codon is approximately 3 because there are 61 codons representing 20 amino acids) or roughly 1047 different manners due to the freedom of choice between synonymous codons. In fact, an important theoretical question we have tried to address here and will continue

0

50

100

150 Lag k

200

250

300

Fig. 7. Sρ (k), k = 1, . . . , 300 for the anticodon sequence X80497 in frame a1, n = 1, 236. The confidence band at the 95% level (dashed line) was obtained through Monte Carlo generation of 3,000 Bernoulli IID sequences.

JANUARY/FEBRUARY 2006

79

Sp(k)

GC-biased comparison between organisms or regions of the same genome that differ in the GC content. The entropy-based metric Sρ (k) is tailored to explore the dependence structure of a sequence and can be seen as a nonlinear autocorrelation function. Since its relation with several existing entropy-based measures and due to its good properties, Sρ (k) has revealed a powerful and informative tool in this context. The computation of Sρ (k) upon several parity sequences has highlighted the existence of a long-range dependence together with high peaks that might be associated to gene-specific periodicities. Even though a detailed phenomenological discussion is out of the scope of this article, it is clear that the results obtained through the two methods are coherent and complement each other. Hence, our approach appears to have a great potential in different applied fields related to genomics and bioinformatics. These different findings prompt us to investigate the issue further under different hypotheses. A matter we intend to pursue is to assess whether the dependence we have observed is of a nonlinear nature. This hypothesis can be tested directly by means of nonparametric tests for nonlinearity based on surrogate data. Since the entropy-based measure Sρ (k) was shown to have considerable power against nonlinear alternatives, it is possible to employ it as a test statistic and build the Monte Carlo distribution of Sρ (k) under the null hypothesis that the series we have observed is a realization of a correlated linear process. Our guess is that a nonlinear dynamical system may represent a very efficient decoding system for the management of nonlinearly correlated information. Moreover, the observed dynamic complexity of such kind of systems (including chaotic behavior and also self-correcting capabilities) can

0

50

100

150 Lag k

200

(a)

250

300

0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012

0.0000 0.0005 0.0010 0.0015 0.0020 0.0025 0.0030

Sp(k)

• The genetic code exhibits a strong mathematical structure that is difficult to put in relation with biological advantages other than error correction. It must be remarked that the probability of random generation of a similar but less restrictive mathematical ordering has been calculated to be 3.09−32 , that is, practically zero [11]. • Interestingly, this mathematical structure implies that codons are parity coded (parity coding represents the simplest and most widely used system for error checking and correction in man-made digital data communication systems). • The statistical analysis performed in this work clearly shows that parity symbols exhibit strong and complicated dependence patterns (a necessary condition for the existence of mechanistic constraints). From the point of view of the statistical analysis of the sequences, we have introduced two methods for a rigorous study of the serial dependence. In fact, the existence of errorcorrection mechanisms implies the presence of a correlation in the sequences. Both the approaches show the existence of a significant dependence and prompt us to pursue further investigation on the topic. As we have remarked above, the MBB is a tool to assign the accuracy to estimates in presence of dependent data. As a byproduct, it provides also a measure of the correlation of the sequence through (3). In particular, we have focused on the proportion p of even codons as defined through the theoretical approach presented above. Thus, the study of the variance associated to p gives substantive information about the dependence of the data in this coding framework. In addition, this analysis can be interesting for a comparative study. In fact, as can be desumed from Table 5, the proportion p does not depend explicitly on the GC content of the sequences, potentially allowing for a non-

0

50

100

150

200

250

300

Lag k (b)

Fig. 8. Sρ (k), k = 1, . . . , 300 for out-frame sequences of the gene X80497 a2 (a) and a3 (b), n = 1, 235. The confidence band at the 95% level (dashed line) was obtained through Monte Carlo generation of 3,000 Bernoulli IID sequences.

80 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

offer an interesting possibility for determining the complex and elusive rules underlying the biological encoding of genetic information. Diego Luis Gonzalez was born in Buenos Aires, Argentina, in 1951. He received his degree in physics from the University of La Plata in Argentina in 1981 and a Ph.D. in theoretical physics from the same university in 1987. His Ph.D. focused in the study of synchronization and chaos in nonlinear oscillators. He worked from 1988 for the National Research Council of Italy at LAMEL Institute in the field of microelectronics and microsystems. Since 1999 he has collaborated with the Acoustical Lab of the St. George School Foundation and the National Research Council in Venice, Italy. His main research interests are the theory of nonlinear dynamics and chaos and their application to the modeling of complex dynamic systems, with particular emphasis on biological systems. Simone Giannerini was born in 1970 in Castiglione dei Pepoli (Bologna, Italy). He received his degree in statistics in 1998 and a Ph.D. in statistics in 2002 at Bologna University. He also obtained an M.Sc. in statistics at the London School of Economics in 2001. Since 2005, he has been a researcher at the statistics department at Bologna University. His research interests include chaos theory, nonlinear time series analysis, stochastic processes, and epidemiology. Rodolfo Rosa was born in Bologna, Italy, in 1944. He received his degree in physics in 1968 and in philosophy in 1977. From 1969–1992 he has been a researcher at the National Research Council-LAMEL institute in Bologna. Since 1992 he has been a professor at the Faculty of Statistics, Bologna University, where he teaches courses on statistics for experimental research, chaos and complexity, and stochastic processes. His research interests include philosophy of science, Monte Carlo methods applied to atomic interactions in solids, statistical mechanics and, more recently, advanced statistical methods and chaos theory. Address for Correspondence: Diego Luis Gonzalez, Laboratorio di acustica musicale e architettonica, CNRFondazione Scuola di S. Giorgio, Isola di San Giorgio Maggiore, Venezia, I-30124, Italy. E-mail: diego.gonzalez@ cini.vecnr.it. References [1] B. Hayes, “The invention of the genetic code,” Comput. Sci., vol. 86, pp. 8–14, no. 14, 1998. [2] H. Yockey, Information Theory and Molecular Biology. New York: Cambridge University Press, 1992. [3] D.R. Fordsike, “Are introns in-series error detecting sequences?” J. Theoret. Biol., vol. 93, no. 4, pp. 861–866, 1981.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

[4] J. Rzeszowska-Wolny, “Is genetic code error-correcting?” J. Theoret. Biol., vol. 104, no. 4, pp. 701–702, 1983. [5] L. Liebovitch, Y. Tao, A. Todorov, and L. Levine, “Is there an error correcting code in the base sequence in DNA?” Biophys. J., vol. 71, no. 3, pp. 1539–1544, 1996. [6] G. Battail, “Is biological evolution relevant to information theory and coding?” in Proc. ISCTA’01, Ambleside, 2001, pp. 343–351. [7] D. MacDónaill, “A parity code interpretation of nucleotide alphabet composition,” Chem. Commun., vol. 18, pp. 2062–2063, 2002. [8] E. May, “Analysis of coding theory based models for initiating protein translation in prokaryotic organisms,” Ph.D. dissertation, NC State Univ., Raleigh, NC, 2002. [9] G. Rosen and J. Moore, “Investigation of coding structure in DNA,” in Proc. ICASPP, 2003. [10] D. Gonzalez and M. Zanna, “Una nuova descrizione matematica del codice genetico,” Systema Naturae, Annali di Biologia Teorica, vol. 5, pp. 219–236, 2003. [11] D. Gonzalez, “Can the genetic code be mathematically described?,” Med. Sci. Monitor, vol. 10, no. 4, pp. 11–17, 2004. [12] H. Künsch, “The jackknife and the bootstrap for general stationary observations,” Annals Stat., vol. 17, no. 3, pp. 1217–1241, 1989. [13] C.W. Granger, E. Maasoumi, and J. Racine, “A dependence metric for possibly nonlinear processes,” J. Time Series Anal., vol. 25, no. 5, pp. 649–669, 2004. [14] L.L. Gatlin, Information Theory and the Living System. New York: Columbia Univ. Press, 1972. [15] E.E. May, M.A. Vouk, D.L. Bitzer and D.I. Rosnick, “A coding theory framework for genetic sequence analysis,” J. Franklin Instit., vol. 341, no. 1–2, pp. 89–109, 2004. [16] E.E. May, “Towards a biological coding theory discipline,” New Thesis, vol. 1, no. 1, pp. 19–38, 2004. [17] J.E.M. Hornos and Y.M.M. Hornos, “Algebraic model for the evolution of the genetic code,” Phys. Rev. Lett., vol. 71, no. 26–27, pp. 4401–4404, 1993. [18] V.A. Karasev and V.E. Stefanov, “Topological nature of the genetic code,” J. Theor. Biol., vol. 209, no. 3, pp. 303–317, 2001. [19] V.I. Shcherbak, “Arithmetic inside the universal genetic code,” BioSyst., vol. 70, no. 3, pp. 187–209, 2003. [20] A. Patel, “Quantum algorithms and the Genetic Code,” Pramana- J. Physics, vol. 56, no. 2–3, pp. 367–381, 2001. [21] A. Arneodo, Y. D’Aubenton-Carafa, E. Bacry, P.V. Graves, J.F. Muzi and C. Thermes, “Wavelet based fractal analysis of DNA sequences,” Physica D, vol. 96, no. 1, pp. 291–320, 1996. [22] M.A. Jimenez-Montaño, C.R. de la Mora-Basañez, and T. Pöschel, “The hypercube structure of the genetic code and non-conservative amino acid substitutions in vivo and in vitro,” BioSyst., vol. 39, no. 2, pp 117–125, 1996. [23] N. Stambuk, P. Konjevoda, and N. Gotovak, “Binary Coding, mRNA Information and Protein Structure,” J. Comput. Information Technol., vol. 12, no. 2, pp. 73–81, 2004. [24] R. Durbin, S. Eddy, A. Krogh, and G. Mitchinson, Eds., Biological Sequence Analysis. Cambridge, UK: Cambridge Univ. Press, 1998. [25] B. Efron, “Bootstrap methods: Another look at the jackknife,” Annals Stat., vol. 7, no.1, pp. 1–26, 1979. [26] B. Efron and R.J. Tibshirani, An Introduction to the Bootstrap. New York: Chapman & Hall, 1993. [27] B. Ripley, Stochastic Simulation. New York: Wiley, 1987. [28] K. Binder, “Introduction,” in The Monte Carlo Method in Condensed Matter Physics, K. Binder, Ed. Berlin: Springer-Verlag, 1982, pp. 1–22. [29] S. Mignani and R. Rosa, “Markov chain Monte Carlo in statistical mechanics: The problem of accuracy,” Technometrics, vol. 43, no. 3, pp. 347–355, 2001. [30] S. Gottlieb, P. Mackenzie, H. Thacker, and D. Weingarten, “Hadronic coupling constants in lattice gauge theory,” Nuclear Physics B, vol. 263, no. 3–4, pp. 704–730, 1986. [31] R. Liu and K. Singh, “Moving blocks jackknife and bootstrap capture weak dependence,” in Exploring the Limits of Bootstrap, R. LePage and L. Billard, Eds. New York: Wiley, 1992, pp. 225–248. [32] J. Crutchfield and D. Feldman, “Regularities unseen, randomness observed: Levels of entropy convergence,” Chaos, vol. 13, no. 1, pp. 25–54, 2003. [33] H. Abarbanel, Analysis of Observed Chaotic Data. New York: Springer Verlag, 1996. [34] H. Joe, “Relative entropy measures of multivariate dependence,” J. Amer. Stat. Assoc. vol. 84, no. 405, pp. 157–164, 1989. [35] Oryctolagus cuniculus glycogen synthase mRNA [Online]. Available: http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=2384761. [36] PHKLA gene; phosphorylase kinase, Homo Sapiens [Online]. Available: http://www. ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=663009 [37] D. Holste, I. Grosse, S. Beirer, P. Schieg, and H. Herzel, “Repeats and correlations in human DNA sequences,” Phys. Rev. E, vol. 67, 061913, 2003. [38] T. Schreiber and A. Schmitz, “Surrogate time series,” Physica D, vol. 142, no. 3–4, pp. 346–382, 2000. [39] V. Marquez, D. Wilson, and K.H. Nierhaus, “RNA-protein machines,” Biochem. Soc. Trans., vol. 30, no. 2, pp. 133–140, 2002. [40] D. Ardell, “On error minimization in a sequential origin of the standard genetic code,” J. Mol. Evol., vol. 47, no. 1, pp. 1–13, 1998. [41] S. Freeland and L. Hurst, “Load minimization of the genetic code: History does not explain the pattern,” in Proc. R. Soc. Lond. B, 1998, vol. 265, pp. 2111–2119.

JANUARY/FEBRUARY 2006

81

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

Finding Large Domains of Similarly Expressed Genes

© EYEWIRE

A Novel Method Using the MDL Principle and the Recursive Segmentation Procedure

BY DANIEL NICORICI, OLLI YLI-HARJA, AND JAAKKO ASTOLA

he advent of microarray technology enables us to measure simultaneously levels of thousands of genes for entire genomes in a single experiment—producing daunting amounts of data and genetic information. After the Human Genome Project ended in 2003 with the successful completion of the human genetic sequence [1], attention is turning to functional genomics. With gene expression data available for different organisms and their genomes already sequenced, a major goal is to understand the regulation of genes at the gene level and at the chromosomal level. Thus, an essential task is to define the role of the regulation mechanism and to understand how the regulation of a set of adjacent genes functions at the chromosomal level. In order to make biological sense of the sequenced genomes and the gene expression data that are available, all of the data must be organized in a manner that allows 1) the discovery of patterns that may arise, and 2) the establishment of relations between the gene expressions and the regulation mechanisms, e.g., the transcriptional regulation. Recent studies [2]–[5] confirm that the genomes are divided into large domains that are composed of adjacent genes on chromosomes with similar expression profiles. There is evidence from budding yeast that some similarly expressed genes are found in adjacent pairs or triplets on chromosomes [3]. Larger domains are found to exist in the human genome [2], [4] due to the strong clustering of highly expressed genes in nearly all tissues. Also in their study, Spellman et al. [14] have found by analyzing the Drosophila genome and high-density oligonucleotide microarrays that its genes are clustered into coregulated groups of adjacent genes on chromosomes. The mechanism underlying the large domains is not yet known, but the observed similarities in the expression of adjacent genes are consistent with regulation at the level of chromatin structure [5]. The method used by Spellman et al. [5] does not provide a very good criterion for evaluating the quality of segmentation into large domains. We describe a new method for finding large domains of similarly expressed genes using the minimum description length (MDL) principle and a recursive segmentation procedure. For the recursive segmentation, we introduce a new stopping criterion based also on the MDL principle.

T

82 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Based on the MDL principle, we give a rigorous definition of the quality of the segmentation of genomic profiles into large domains. Intuitively, a large domain can be considered a group of adjacent genes on a chromosome, where the expression profiles of the genes are similar. This can be described in a succinct way by using the MDL principle, which has been introduced by Rissanen [6], [7]. The MDL principle has been used in statistics, machine learning, data mining [8], and genomic signal processing [9]–[11]. According to the MDL principle [6], the model is selected based on its fitting performance, but it also penalizes a very high complexity of the model. Genomes can be divided into large domains that are important in controlling the expression of groups of adjacent genes [5]. The recursive segmentation can be used for finding their borders. The recursive segmentation methods have been applied to DNA segmentation into homogeneous domains; for finding the borders between coding and noncoding regions in DNA; for detecting the existence of the isochores, CpG islands, and replication origin and terminus; for detecting complex patterns such as telomers; and for evaluating the genomic complexity [12]–[14]. The criterion for continuing the recursive segmentation process can be based on 1) statistical significance [12], 2) the Bayesian information criterion (BIC) [12]–[14], or 3) the MDL principle [11], [15]. Our approach uses only the general properties of the large domains and, in this way, prior training on data sets is not necessary. The training data sets that contain the positions of large domains are not available. Also throughout this study, we define the genome data as the data containing measurements of gene levels versus experimental conditions, and the gene profiles are ordered according to their position on the chromosomes. MDL Principle and Coding of Genome Data

Let X be a genome data represented as a n × m matrix where the row i represents the activity of the gene i (gene profile i) over different experimental conditions, and the column j represents the set of measurements for the experimental condition j. The genes in the genome data X are ordered according to their positions along the chromosomes and its entries xi, j take value in the set {0, 1, . . . , q − 1} due to quantization of the genome data to q levels. 0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

Our new segmentation method allows us to find large domains of similarly expressed genes without any a priori data for training.

MDL Principle

The MDL principle by Rissanen [6], [16] considers the description length of the data and the model as follows L(M, X) = L(M) + L(X|M),

(1)

where L(M) is the length of the description of the model and L(X|M) is the length of the description of the data, where the data X is described using the model M. According to the MDL principle, the best model that fits the data is the model with the shortest length of the total description L(M, X). Such a reduction indicates that the model M is able to capture the patterns and the dependencies within the data X. The goal is not to write down the encoded data but to compare the code length of the encoded data for a class of models. Thus, the model with the best fitting performance, which gives the shortest overall code length, is selected but in a balanced way; the models with a very high complexity are penalized. The MDL principle has been used in various applications [6], [9], [10]. Coding of Genome Data

The compression of a given genome data Y is done using the model M1 , which takes into consideration the similarity between all the gene profiles from Y. The genome data Y is a n∗ × m∗ matrix that contains n∗ genes across m∗ different experimental conditions, where the entries yi, j take value in the set {0, 1, . . . , q − 1} due to quantization of Y to q levels. The genes are ordered within Y according to their position on the chromosomes. Further, Y is considered to be a submatrix of the matrix X. We apply several transformations to the matrix Y ∗∗ such that (matrix Y) → (matrix Z) → (string wn ). The probability of the observed genome data Y is computed using the ∗∗ string wn , which takes into consideration the similarity between all gene profiles within the genome data Y. We construct a q × m∗ matrix M such that entries mi, j are the counts of the symbol (i − 1) within j of the
q the column ∗ matrix Y. Thus, one has i=1 m i, j = n , where j = 1, . . . , m∗ . The symbols observed at each column j of the matrix Y are reordered by their counts from the matrix M using a permutation vj(·), where j = 1, . . . , m∗ . The permutations are used because their coding requires a relatively short code length. A permutation aligns the histograms of symbols of each Y’s column, such that all histograms are monotonically decreasing, and collapses all histograms into a single one [9]. A permutation vj(·) maps k → vj(k), where k = 1, . . . , q, as follows



0 1 ... vj(0) vj(1) . . .

q−1 vj(q − 1)

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

 .

(2)

The transformed matrix Z is obtained from the matrix Y by using a set of permutations v = (v1 (·), . . . , vm∗ (·)), where zi, j = v(yi, j). Such a transformation is reversible due to the use of permutation [9], i.e., one can recover Y from Z knowing v. ∗∗ The matrix Z is transformed further into the string wn of length ∗∗ ∗∗ ∗ ∗ n , where n = n × m , by concatenating its rows. The ∗∗ entries W of the string wn take value in the set {1, . . . , q − 1}. The entries of the matrices X, Y, and Z take value also in the ∗∗ same set {1, . . . , q − 1}. The transformed string wn is modeled further as a multinomial trial process with parameters P(W = 0)
= θ∗0 , . . . , P(W = q − 1) = θq−1 . The symbol l is observed mj=1 mvj(l), j times in the matrix Z and in the string ∗∗ ∗∗ wn . Also, one can recover Y from wn knowing v. ∗ For instance, one has for q = 2, n = 3, m∗ = 2, and  Y=  Z=

0 0 1 0 1 0



 that M =

 1 3 , v1 = (1 0), v2 = (0 1), 2 0

 1 0 ∗∗ 0 0 , wn = (1 0 0 0 0 0), and n∗∗ = 6. 0 0

To conclude, the probability of the genome data Y is given by ∗∗ ∗∗ P(Y;  θ , v ) = P(wn ( v );  θ(wn ), v (Y))
m∗
m∗ mvj(0), j mvj (q−1), j · ... · θq−1j=1 , = θ0 j=1

(3)

v (Y) = { vi(·) : i = 1, . . . , m∗ } where the set of permutations  n∗∗ determines the string w , and the multinomial parameters of ∗∗ ∗∗ ∗∗ ∗∗ v ) are  θ(wn ) = ( θ0 (wn ), . . . ,  θq−1 (wn )). the string wn ( n∗∗ ∗ The string w contains mi values of i, where ∗∗  ) =∗ m∗i n∗∗ and i = 0, . . . , q − 1. Clearly, one has θi(wn
m∗i = mj=1 mvj(i), j. The overall code length of the encoded n∗ × m∗ matrix Y using the model M1 based on the MDL principle is as follows L(M1 , Y) = − log2 P(Y;  θ , v) + m∗ log2 (q!) (4) + log2 Nθ (n∗∗ , q), where the first part encodes the data Y given the model M1 θ and  v, the second part encodes the optiwith the parameters  v, and the third part encodes the maximum mal permutations  θ and  v. In order to encode effilikelihood (ML) estimates of  ciently, the set of probabilities, Nθ (n∗∗ , q) is used because the θ,  v) is redundant, and one can restrict set of pairs (  θ = ( θ0 , . . . ,  θq−1 ) such that  θ0
 θ0
. . .
 θq−1 [9]. The length of the list containing all possible q-tuples JANUARY/FEBRUARY 2006

83

class labels as a two-part code. The code length computed by Tabus et al. [9] is based on gene expressions of each patient, and it is used in the problem of class discrimination. The major difference between our approach and the approach of Tabus et al. [9] is that we compute the code length of the encoded genome data Y in such way to take into consideration the similarity between the gene profiles (the matrices M and Z are constructed in this way). Furthermore, the computed code length is used in the problem of segmentation into large domains of similarly expressed genes.

1 10 20 Gene Index

30 40 50 60 70

Coding of Genome Data with Large Domain of Similarly Expressed Genes

80 90 100

1

10

20

30 40 50 60 70 80 Experimental Conditions

The encoding of a given genome data X is done also using the model M3 (i, j), which considers the existence of a large domain (i, j) containing similarly expressed genes. A large domain (i, j) starts with the ith gene profile and ends with the jth gene profile within matrix X, and it splits the matrix X into three submatrices X (a) , X (b) , and X (c) , which contain the X’s gene profiles from 1 to i − 1, i to j, and j + 1 to n, respectively. The submatrix X (b) is considered to be the only one that represents the large domain (i, j), and it cannot contain more than the a priori established maximum number of gene profiles. The number of genes contained in a large domain has been determined previously using biological experiments or data [2]–[5]. A large domain with similarly adjacent gene profiles gives a submatrix X (b) that is encoded very effectively using (4) based on the MDL principle. According to the MDL principle, the overall code length of the encoded genome data X using model M3 (i, j) that considers the existence of a large domain (i, j) is

90 100

Fig. 1. Synthetic genome data containing 100 gene profiles across 100 experimental conditions. The first 50 gene profiles are randomly generated, the next 20 gene profiles are identical, and the last 30 gene profiles are randomly generated. The expression of genes have binary values.

L(M1,T )-L(M3(i,j ),T ) [b]

∗∗ (n∗∗ is Nθ (n∗∗ , q) such that n∗∗ 0 , . . . , n q−1 ) 0 + ···+ ∗∗ ∗∗ ∗∗ ∗∗
. . .
n nq−1 = n and n0
n∗∗ M1 [9]. The model 1 q−1 considers that the entire given genome data Y is encoded as a single part, and no large domains of similarly expressed genes exist within Y. Thus encoding the genome data Y with few similarities between genes profiles will be penalized with a larger code length than in the case when there are more similarities between the gene profiles. We note that our approach of computing the code length of encoded data Y, especially of (3) and (4), is a modification of the approach used by Tabus et al. [9] for computing the code length of encoded



L(M3 (i, j), X) = L M1 , X (a) + L M1 , X (b) 
+ L M1 , X (c) + 2 · log2 n,

(5)

1,600 1,400 1,200 1,000 800 600 400 200 0 −200 30 25 20 j−i +1

15

Length of Candidate Large Domain (# of Genes)

10 5

2

1

100 90 70 80 60 50 30 40 i 20 10 Start Position of Candidate Large Domain (Gene Index)

Fig. 2. A 3-D representation of code length L(M1 , T) − L(M3 (i, j ), T)based on the MDL principle, computed for all possible candidate large domains (maximum length of 30) for synthetic genome data of 100 genes from Figure 1. The maximum value for the computed code length is circled on the graph and it corresponds to the large domain (51, 70).

84 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

10,001

10,020

4

10,040

10,040

2

10,080

1

10,000

0

10,120

Gene Index

3

10,060 Gene Index

10,001

5

10,020

10,060 10,080 10,100 10,200

10,140

−1

10,160

−2

10,600

10,180

−3

10,800

−4

10,200

10,200

1

10

20 30 40 50 60 70 Experimental Conditions

80 89

10,400

1

10

20

30

40

50

60

70

80

89

Experimental Conditions

Fig. 3. Expression profiles of 200 adjacent genes on the right arm of the Drosophila chromosome 3 (3R).

where the first three terms L(M1 , X (a) ), L(M1 , X (b) ), and L(M1 , X (c) ) are computed using (4) based also on the MDL principle, and they give the cost in bits of encoding the submatrices X (a) , X (b) , and X (c) , respectively. The last term 2 · log2 n is needed to encode in bits the two positions where the large domain starts and ends within the matrix X. When it is assumed that no large domain exists within the genome the data X, the code length of the encoded matrix X is L(M1 , X), and it is computed using the model M1 and relation (4).

Fig. 4. Expression profiles of 200 adjacent genes (from Figure 3) on the right arm of the Drosophila chromosome 3 (3R) quantized to binary values. For each square, a black color denotes a lower relative expression than a white color for a gene in an experiment.

L(M1 , X) − L(M3 , X) = max (L(M1 , X) − L(M3 (i, j), X)) (i, j)

= L(M1 , X) − min L(M3 (i, j), X). (i, j)

(6)

Clearly, one has L(M3 , X) = min(i, j) L(M3 (i, j), X). If the maximized code length L(M1 , X) − L(M3 , X) is above the In this study we use the recursive segmentation method prothreshold, the genome data is segmented, and if not, the segposed by Bernaola-Galvan et al. [12] and Li [13] for finding mentation is stopped for the respective data. We note the large domains of similarly expressed genes in the given genome data. The recursive segmentation of a given genome data X proceeds as follows. We sweep through the gene profiles of X and compute at every position i and j, where i < j, i = 1, . . . , n − h and 1,000 j = i + 1, . . . , i + h , that divide the matrix X into the upper submatrix X (a) , 800 the middle submatrix X (b) , which repre600 sents the large domain (i, j), and the (c) X , the code lengths lower submatrix 400 of the whole matrix, the upper, the mid200 dle, and the lower submatrices. According to Spellman et al. [5], we 0 choose the maximum length of a large −200 domain to be h = 30 genes. The posi30 10,2 25 tions i and j are accepted as cutting 1 0 20 10, 0,180 0 1 1 points, representing the large domain 0 6 , 0 1 15 1 40 10 0,1 j−i+1 10 (i, j), when the code length, 10, 10,08 ,100 20 0 1 0 0 6 5 (Length of Candidate L(M1 , X) − L(M3 (i, j), X), computed 2 10,010,020,040 0 i 01 Large Domain) using (4) and (5), reaches its maximum. (Start Position of Candidate [# of Genes] Further, we recursively apply the Large Domain) segmentation to the upper submatrix [Gene Index] X (a) and to the lower submatrix X (b)
− L(M3 (i, j ), X)
based on the MDL until maximized code length Fig. 5. 3-D representation of code length L(M1 , X) L(M1 , X) − L(M3 , X) is above a principle, computed for all possible candidate large domains (maximum length of certain threshold. In this approach, 30) for data of 200 adjacent genes, from the right arm of Drosophila chromosome 3 the threshold is based on the MDL (3R), shown in Figure 4. The maximum value for the computed code length is circled principle, where on the graph, and it corresponds to the large domain (83, 104). L(M1,T)-L(M3(i,j),T) [b]

Recursive Segmentation into Large Domains

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

85

The model with the best fitting performance, which gives the shortest overall code length, is selected but in a balanced way.

maximum value of the computed code length, L(M1 , T) − L(M3 , T), finds exactly the start position and the length of the large domain with 20 identical gene profiles in the synthetic data T. Stopping Criterion for Recursive Segmentation

The stopping criterion, in the case when relation (6) is used, can be considered from the point of view of hypothesis testing and the model selection framework. For the hypothesis testing framework, the probability that the value of L(M1 , X) − L(M3 , X) can be obtained by chance is

10,001

10,001

10,020

10,020

10,040

10,040

10,060

10,060

10,080

10,080 Gene Index

Gene Index

similarity of this method with the approach when Jensen– Shannon divergence is used [13], [14]. Figures 1–2 shown synthetic data T of 100 gene profiles and the three-dimensional (3-D) representation of the code length L(M1 , T) − L(M3 (i, j), T). The synthetic data T consist of 100 gene profiles that take binary values, where the first 50 gene profiles of data T are randomly generated, the next 20 gene profiles are identical, and the last 30 gene profiles are randomly generated. The maximum value for the computed code length is circled on Figure 2, and it corresponds to the large domain (51, 70). Figure 2 shows that the

10,100

10,100

10,120

10,120

10,140

10,140

10,160

10,160

10,180

10,180

10,200

1

10

20

30

40

50

60

70

80

89

10,200

Experimental Conditions

Large Domains

(a)

(b)

Fig. 6. Large domains of similarly expressed genes—gray or black rectangles in (b)—found using recursive segmentation applied to data of 200 adjacent genes from the right arm of Drosophila chromosome 3 (3R), shown in Figure 4.

86 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

computed by the null hypothesis that the genome data is homogeneous. The exact form of the null distribution is difficult to find [13]. The stopping criterion, based on Bayesian information criterion (BIC), for segmentation using Jensen–Shannon divergence has been introduced by Li in his study [13]. One can see the subtle relations between (6) and the Jensen–Shannon divergence or the Kullback-Leibler divergence [6]–[8], and also between the BIC and the MDL principle, which give an identical formula for certain cases [8]. In this study, we introduce a new stopping criterion for the recursive segmentation, when using the code length L(M1 , X) − L(M3 , X), based on model selection using the MDL principle. Thus, the stopping criterion tests if a threerandom–submatrices model M3 gives a shorter code length than the one-random–matrix model M1 . If the three-random–submatrix model has a shorter code length (it better fits the data), then the cuts are accepted (the large domain is accepted); otherwise, it is not. The MDL principle assures us of balancing the goodness-of-fit of the model with the complexity of the model in relations (4) and (5), and a very high complexity of the model is penalized. In order to continue the recursive segmentation procedure and to decide if the cuts i and j are significant or not (if the large domain (i, j) is significant), the three-random-submatrices model must fit the data better than the one-random model. This leads to a stopping criterion that is as follows L(M1 , X) − L(M3 , X) > 0,

(7)

where L(M1 , X), L(M3 , X) are computed using (4) and (6), respectively. Thus, the recursive segmentation continues, or the cuts i and j, which represent the large domain (i, j), are accepted as significant as long as criterion (7) is fulfilled.

patterns, which are not otherwise functionally related in any obvious way, appear frequently [5]. The starting point is the gene expression data X, also called genome data, where each entry xi, j indicates the expression level of gene i for experimental condition j. We make the assumption that the transcription machinery of a gene uses the expressed/not expressed or upregulated/ downregulated states [18]. More precisely, we quantize each gene profile independently to binary states [18] by applying the Lloyd algorithm [9]. The quantization to discrete values of genome data can be viewed also as removing the noise from data [9]. In this study, the entries in X are quantized to q = 2 levels, but the newly introduced method for finding large domains can use more than two levels of quantization. For the remainder of the article, we assume that the genome data X is quantized to binary values (how many quantization levels are chosen is outside the scope of this article). The MDL principle can also be used to select an optimum q value as suggested in [9]. In order to illustrate the segmentation procedure, we apply the new segmentation method on a group, chosen arbitrarily from the chromosome 3R of Drosophila, of 200 adjacent
. Figure 3 shows the original expression progenes, noted as X
that are ordered files of the group of 200 gene profiles from X accordingly to their position along the chromosome. Also,
after quanFigure 4 shows the same 200 gene profiles from X tization to binary states using Lloyd algorithm, where a white color indicates a higher relative expression of a gene in an experiment than a black color. In Figure 3, and especially in Figure 4, are visible groups of adjacent genes that have similar expression profiles and the vertical separation between embryos and adults of Drosophila. Figure 5 illustrates the 3-D representation of the code length
) − L(M3 (i, j), X
) , where the other two axes L(M1 , X

Experimental Results 150 135 120 105 90 Counts

We illustrate the finding of large domains of similarly expressed genes based on the MDL principle and recursive segmentation using the Drosophila genome data of Spellman et al. [5], publicly available [17], and human genome data. The microarray genome data of Drosophila contains 13,165 gene expression profiles, covering 89 distinct experimental conditions from 267 Affymetrix GeneChip Drosophila Genome Arrays [5]. The experimental conditions consist of adults and embryos which are visible in Figures 3 and 4 as a vertical line. The data are in log2 ratio format, all replicates are averaged, and the values are time zero corrected [5]. Data preprocessing and experimental conditions are described in detail in [5]. The genes in this dataset are organized according to their positions along the chromosome. Visual inspection of the data, as shown in Figures 3–4, reveals that groups of adjacent genes with similar expression

75 60 45 30 15 0

2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930 Length of Large Domain (# of Genes)

Fig. 7. The results of a recursive segmentation of Drosophila genome into large domains of similarly expressed genes.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

87

our new segmentation method permits us to find successfully large domains of similarly expressed genes without any use of a priori data for training.

represent i (position where the candidate large domain starts) and j+ i − 1 (length of the candidate large domain given as number of genes). The maximum value
) − L(M3 (i, j), X
) is circled on Figure 5 and of L(M1 , X it corresponds to the large domain (83, 104), which is marked in Figure 4 on the right-hand side as a grayfilled rectangle. This large domain is accepted as signifi
) − L(M3 (83, 104), X
) = 987.14 b cant because L(M1 , X
) − L(M3 (83, 104), X
) > 0, where criterion and L(M1 , X (7) is fulfilled. Figure 6 shows the results of the recursive segmenta
tion applied to the same 200 adjacent gene profiles X from the Drosophila chromosome 3R. The new method introduced in this study, based on the MDL principle and recursive segmentation, is able to find successfully
as the large domains of similarly expressed genes in X shown in Figure 6. The new recursive method based on the MDL principle is applied to the genome of Drosophila [5] containing 13,615 genes, and it finds 750 large domains of similarly expressed genes together with their exact positions on the chromosomes. From these 750 large domains, 223 large domains are the domains that contain between 10–30 similar gene profiles. Figure 7 shows a histogram of the sizes of similar gene-profile

50 45 40

Counts

35 30 25 20 15

segments that result when the new recursive segmentation method, based on the MDL principle, is applied to the Drosophila genome data. The human genome data contain 21,810 genes ordered according to their position on the chromosomes versus 50 patients with colorectal tumors. The Affymetrix HG-133A chips have been used for gene measurements. The gene profiles are quantized to binary using the Lloyd algorithm as done for the Drosophila genome data. The new recursive method finds 160 large domains of similarly expressed genes in human genome data. From these 160 large domains, 40 large domains are the large domains that contain between 10–30 similar gene profiles. Figure 8 shows a histogram of the sizes of similar gene-profile segments that result when the new recursive segmentation method is applied to the Human genome data. Even though the biological significance of the detailed results for the Drosophila genome data and the human genome data remains to be later investigated and the underlying mechanism of the large domains is unknown [5], our new segmentation method permits us to find successfully large domains of similarly expressed genes without any use of a priori data for training. The novel method introduced in this study, based on the MDL principle, for finding large domains of similarly expressed genes is different in several aspects from the method introduced in [11] for finding the large domains based on the MDL principle and normalized maximum likelihood (NML) model. The major differences are that in the current method, the quantization is done using the Lloyd algorithm and the similarity between all genes from a large domain are taken into consideration, which is closer to the biological knowledge available. In our previous study [11], only the similarities between the first gene profile and the rest of the gene profiles from a given large domain were taken into consideration.

10

Concluding Remarks

5 0

2 3 4 5 6 7 8 9 1011 121314 15 1617 18 19 20 2122 23 2425 26 2728 29 30 Length of Large Domain (# of Genes)

Fig. 8. The results of a recursive segmentation of the human genome into large domains of similarly expressed genes.

88 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

In this study, we have introduced a new method for finding and defining large domains of adjacent genes on a chromosome with similar expression profiles based on the use of the MDL principle and the recursive segmentation procedure. For the recursive segmentation, we used a newly introduced JANUARY/FEBRUARY 2006

stopping criterion using the MDL principle. Together they offer a novel method to view the large domains of similarly expressed genes in genome data. The description of the genome data and of the large domain is done according to the MDL principle, which selects the model based on its fitting performance and also penalizes a very high complexity of the model. The success of segmentation comes from the observation that the more similar the gene-expression profiles are in a large domain, the shorter the description of the data that represents the large domain. We have applied the new recursive segmentation method to the microarray measurements of the Drosophila genome and human genome in order to demonstrate the ability of the new method to find large domains successfully.

1979 and 1987, he was with the Department of Information Technology, Lappeenranta University of Technology, Finland, holding various teaching positions in mathematics, applied mathematics, and computer science. In 1984, he worked as a visiting scientist in Eindhoven University of Technology, The Netherlands. From 1987–1992 he was an associate professor in applied mathematics at Tampere University, Tampere, Finland. Since 1993, he has been professor of signal processing and director of Tampere International Center for Signal Processing, leading a group of about 60 scientists. He was nominated as an academy professor by the Academy of Finland (2001–2006). His research interests include signal processing, coding theory, spectral techniques, and statistics. He is a Fellow of the IEEE.

Acknowledgments

Address for Correspondence: Daniel Nicorici, Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland. E-mail: [email protected].

The authors thank Prof. Lauri A. Altonen and Diego Arango from the Department of Medical Genetics, Biomedicum Helsinki, Finland, for their valuable support and for providing the human genome data. Special thanks are also addressed to Christophe Roos from MediCel Ltd., Finland, for insightful discussions and references on large domains of similarly expressed genes. Daniel Nicorici received his B.S. and M.S. degrees in electrical engineering from the Technical University of Cluj-Napoca, Romania, in 1999 and 2000, respectively. He received the Ph.D. degree in signal processing from Tampere University of Technology, Finland, in 2005. Since 2001, he has been with the Institute of Signal Processing, Tampere University of Technology, as a researcher. He is currently pursuing his postdoctoral studies at Tampere University of Technology. His research interests include genomic signal processing, bioinformatics, and computational systems biology. Olli Yli-Harja received an M.Sc. in energy technology (1985) and a Ph.D. in computer science and applied mathematics (1989) from Lappeenranta University of Technology, Finland. His professional experience involves research and teaching in signal and image processing, computer science, and computational systems biology. Currently, he is a professor at the Institute of Signal Processing, Tampere University of Technology, Finland, leading a research group in computational systems biology. His research interests involve signal processing methods for systems biology, nonlinear signal processing, computational systems biology, discrete dynamic networks, image analysis, and computational analysis of music. Jaakko Astola received his B.Sc., M.Sc., Licentiate, and Ph.D. degrees in mathematics (specializing in error-correcting codes) from Turku University, Finland, in 1972, 1973, 1975, and 1978, respectively. From 1976–1977 he was with the Research Institute for Mathematical Sciences of Kyoto University, Kyoto, Japan. Between IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

References [1] F.C. Collins, M. Morgan, and A. Patrinos, “The human genome project: Lessons from large-scale biology,’’ Sci., vol. 300, no. 5617, pp. 286–290, 2003. [2] H. Caron et al., “The human transcriptome map: Clustering of highly expressed genes in chromosomal domains,’’ Sci., vol. 291, no. 5507, pp. 1289–1292, 2001. [3] B.A. Cohen, R.D. Mitra, J.D. Hughes, and G.M. Church, “A computational analysis of whole genome expression data reveals chromosomal domains of gene expression,’’ Nature Genetics, vol. 26, no. 2, pp. 183–186, 2000. [4] M.J. Lercher, A.O. Urrutia, and L.D. Hurst, “Clustering of housekeeping genes provides a unified model of gene order in the human genome,” Nature Genetics, vol. 31, no. 2, pp. 180–183, 2002. [5] P.T. Spellman and G.M. Rubin, “Evidence for large domains of similarly expressed genes in Drosophila genome,’’ J. Biol., vol. 1, no. 5, pp. 1–5, 2002. [6] J. Rissanen, “Modeling by shortest data description,’’ Automatica, vol. 14, pp. 465–471, 1978. [7] J. Rissanen, “Strong optimality of the normalized ML codes as universal codes and information in data,’’ IEEE Trans. Information Theory, vol. IT-47, no. 5, pp. 1712–1717, 2001. [8] M.H. Hansen and B. Yu, “Model selection and the principle of minimum description length,’’ J. Amer. Statistical Assoc., vol. 96, no. 454, pp. 746–774, 2001. [9] I. Tabus, J. Rissanen, and J. Astola, “Classification and feature gene selection using the normalized maximum likelihood model for discrete regression,’’ Signal Processing, vol. 83, no. 4, pp. 713–727, 2003. [10] I. Tabus, G. Korodi, and J. Rissanen, “DNA sequence compression using the normalized likelihood model for discrete regression,’’ in Proc. Data Compression Conf., Snowbird, UT, 2003, pp. 253–262. [11] D. Nicorici, O. Yli-Harja, and J. Astola, “An MDL method for finding large domains of similarly expressed genes,” in Proc. Workshop Genomic Signal Processing and Statistics (GENSIPS), Baltimore, Maryland, 2004. [12] P. Bernaola-Galvan, I. Grosse, P. Carpena, J.L. Oliver, R. Roman-Roldan, and H.E. Stanley, “Finding borders between coding and noncoding DNA regions by an entropic segmentation method,’’ Physical Rev. E, vol. 85, no. 6, pp. 1342–1345, 2000. [13] W. Li, P. Bernaola-Galvan, F. Haghighi, and I. Grosse, “Applications of recursive segmentation to the analysis of DNA sequences,’’ Computers and Chemistry, vol. 26, pp. 491–510, 2002. [14] D. Nicorici and J. Astola, “Segmentation of DNA into coding and noncoding regions based on recursive entropic segmentation and stop-codon statistics,’’ J. Applied Signal Processing, vol. 1, no. 1, pp. 81–91, 2004. [15] M. Koivisto, M. Perola, T. Varilo, W. Hennah, J. Ekelund, M. Lukk, L. Peltonen, E. Ukkonen, and H. Mannila, “An MDL method for finding haplotype blocks and for estimating the strength of haplotype block boundaries,” in Proc. Pacific Symposium Biocomputing 2003 (PSB’03), Hawaii, 2003, pp. 502–513. [16] A. Barron, J. Rissanen, and B. Yu, “The minimum description length principle in coding and modeling,’’ IEEE Trans. Inform. Theory, vol. 44, no. 6, pp. 2743–2760, 1998. [17] P.T. Spellman, and G.M. Rubin, Web supplement to: “Evidence for large domains of similarly expressed genes in Drosophila genome,” J. Biol. [Online]. Available: http://www.fruitfly.org/expression/dse/ [18] L.A. Soinov, M.A. Krestyaninova, and A. Brazma, Web supplement for “Towards reconstruction of gene networks from expression data by supervised learning,’’ Genome Biol., vol. 4, no. R4 [Online]. Available: http://genomebiology.com/2003/4/1/R6

JANUARY/FEBRUARY 2006

89

COMMUNICATION THEORY AND MOLECULAR BIOLOGY

Classification of Escherichia coli K-12 Ribosome Binding Sites An Error-Control Coding Model © EYEWIRE

BY ELEBEOBA E. MAY, MLADEN A. VOUK, AND DONALD L. BITZER

dvances in genomic sequencing have provided large amounts of data and have spurred computational tools for recognition and modeling of protein coding regions and accurate identification of exact translation start sites [1]–[10]. For example, probabilistic methods, such as Suzek et al.’s RBSFinder and Yada et al.’s GeneHacker Plus [11] return the location of the initiation codon for prokaryotic genes. Besemer and Borodovsky's GeneMarkS uses iterative hidden Markov models (HMMs) to locate translation start sites with relatively high accuracy [12]. Walker, et al. couple statistical methods with comparative genomics methods to identify start sites. Hannenhalli et al. incorporate several biological factors into their quantitative description of translational start sites, including the binding energy at the ribosome binding site (RBS), distance between RBS and initiator, and the initiator codon. They use a mixed integer linear program to determine parameters for their discriminatory model. GeneLook identifies protein-coding sequences using a two-stage, ab initio process [10]. Classification is based on structural characteristics of the sequence such as properties of the ribosome binding site (RBS), operon structure, and codon and nucleotide frequency. Other computational techniques including support vector machine, machine learning, combinatorial approaches, free energy calculations, Bayesian methods, and information theory have also been used in quantifying and classifying translational start sites [7], [13]–[15], [8], [16]. Though current computational methods have provided tools for locating start sites and increased the overall accuracy of gene locater systems such as GLIMMER and GeneMark, they usually require larger sequence windows for classification of initiation sites. Several initiation site classification tools such as RBSfinder use prior gene classification knowledge to aid in start site identification, hence functioning more like a postprocessor. The ribosome, the protein translation machine, makes initiation decisions based on “real-time” processing of a single messenger RNA leader region. To construct a classification system that can make sufficiently correct real-time classification decisions and use a relatively small classification window that is relatively independent of other environmental factors, we propose an approach based on information theory. Drawing on parallels between genetic information processing

A

90 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

in living organisms and the processing of communications data, we develop an error-control coding-based translation initiation classification system that uses an eleven base classification window. In the sections that follow, we begin with an overview of channel codes and a summary of the translation initiation process. We draw parallels between the two and briefly review a channel code model for translation initiation. We present our block-code Bayesian classifier and discuss the results of applying our system to the translation start site location problem for Escherichia coli K-12. Channel Codes and Translation Initiation Overview of Channel Codes

In data communication, the need for coding theory and its techniques stems from the need for error control mechanisms. In an engineering communication system, a k-symbol block (bits for a binary alphabet) of digitized information is encoded by a (n, k) encoder that combines the input symbols with (n-k) additional symbols based on a deterministic algorithm. In the biological domain a “symbol” can be the designator for an amino acid or nucleic acid base. The algorithm produces an (n, k) code, and the encoder is referred to as the channel encoder or the error-control encoder. The set of all valid n-symbol sequences (each sequence is called a codeword) produced by the (n, k) code make up the codebook [17], [18]. There are Qk codewords for a Q-ary code. (In the context of genetics, the term code usually refers to the mapping of symbols used to identify nucleic acid bases to symbols used to identify amino acids that form proteins. In the information theory domain, a code is the result of algorithmic manipulation of basic symbols used to describe information. The purpose of this type of code is to provide robustness in data communication processes. We believe that the latter principle may in fact be used in the analysis of genetic sequences.) The encoded information is transmitted through a potentially noisy channel where the transmitted bits can be corrupted in a random fashion. At the receiving end, the received message is decoded by a complementary channel decoder [17], [18]. The decoding process involves the removal and possibly correction of errors introduced during 0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

transmission and removal of the n-k excess symbols in order to recover the original k-symbols of transmitted information. The decoding mechanism can only cope with errors that do not exceed the code’s error-correction capability. Figure 1 shows an example of a (3, 1) binary repetition code that uses a majority-logic decoding algorithm. Channel codes can be broadly described as pattern recognition systems [19]. The codewords produced by the code are patterns the system wants to recognize. A “good” code will separate valid patterns in such a way that they can be recognized and will reject all other patterns. This work defines a “good” code based on how well the code recognizes the “patterns” or RNA bases that form the leader region, that is, the RNA bases upstream of (preceding) the location of translation initiation site (e.g., AUG).

stream. Ideally, to determine the decoding model for the ribosome we would simply invert the encoding model that produced the DNA. We have yet to satisfactorily identify the genetic process that parallels the error-control encoding process. Instead we analyze key elements involved in initiating protein translation and constructed a plausible

Error Control

Encoder k-bit

n-bit Information

111-000-000-111

Information

1-0-0-1

Channel

Translation Initiation in Prokaryotes

There are three main steps involved in converting information contained in DNA sequences into functioning polypeptide chains: replication, transcription, and translation [20]. During replication, DNA doubles, forming an identical copy of itself. In transcription, information contained in DNA is converted to its RNA equivalent. The result, for gene-specifying DNA, is a messenger RNA (mRNA). In the final process, translation, the ribosome (a compact macromolecule made up of two subunits; in prokaryotes this is the 30S and 50S subunits) locates a valid start site (initiation process) and converts the mRNA sequence to a sequence of amino acids, which specifies a protein (elongation process). Each three-base mRNA sequence (a codon) corresponds to an amino acid. Initiation, the longest phase in translation, involves two principal steps. First, the 30S ribosomal subunit combines with a stabilizing protein, initiation factor three (IF3). The 30S/IF3 molecule then recognizes the ribosome binding site of the bacterial mRNA. A special hexamer called the Shine-Dalgarno [21] sequence is contained in the ribosome binding site. Once the 30S/IF3 molecule attaches to the mRNA, IF3 is relased, leaving the mRNA/30S complex, which is called the initiation complex. The 30S associates with the mRNA sequence by forming hydrogen bonds between the 16S ribosomal RNA (rRNA) in the 30S subunit and the bases of the mRNA (Figure 2). We now draw a parallel between the information communication processes and the genetic processes [22]. Assume that the unreplicated DNA is the output of a concatenated encoding process, the genetic replication process then represents the error-introducing channel. The genetic decoding process is then: transcription, translation initiation, and translation elongation plus termination [23] ,[24], [22]. We can now view the ribosome's interaction with the mRNA similar to the interaction of a channel decoder with an error-control encoded received data IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

111-001-000-110

Errors! Decoder

Noise+n–bit Information

111-001-000-110 1-0-0-1

~k-bit

1-0-0-1

Information

Fig. 1. An example (n =3, k =1) binary repetition error-control coding system. The encoder combines the one information bit with two additional bits, which are simple repetitions of the information bit. As the encoded bit stream is transmitted through the channel, some bits can become corrupted. The decoder uses simple majority logic to determine the original transmitted message. In majority logic decoding, we estimate our original information bit to be a 0 if the majority of the received bits are zeros and a one otherwise.

Fig. 2. An illustration of the translation initiation process. Genetic information is processed 5
(five prime) to 3
(three prime). The region before (upstream) the initiation codon (designated by AUG/GUG/UUG) is known as the 5
untranslated leader region (UTR). The region following (downstream) the initiation codon is the protein coding portion of the mRNA. The interaction between the 16S rRNA and the leader region of the mRNA is key in successful initiation. Therefore we use the 3
end of the 16S rRNA as a template for forming the codewords for our block coding model.

JANUARY/FEBRUARY 2006

91

encoding and corresponding decoding scheme for describing the initiation process in prokaryotic organisms. The key biological elements considered in forming the coding model are: the 3
(three prime) end of the 16S ribosomal RNA, the common features of bacterial ribosomal binding sites (such as the existence and location of the Shine-Dalgarno sequence), and RNA/DNA base-pairing principles. An encoding method was assumed and the corresponding decoding algorithm was developed using the 16S ribosomal RNA [25]. Block Code Model of Translation Initiation

In the information theory block code model, a genetic encoding is modeled as an (n, k) block code whose output is a systematic, zero parity check code [17], [25]. A systematic zero parity code contains the k information symbols at the beginning of the codeword, followed by n-k parity symbols selected such that the “sum” of the codeword symbols is zero. In the binary space (zeros and ones) the “sum” operator is the exclusiveOR operation, in higher-order field mathematics, special operator tables need to be constructed. Codewords of length n = 5 are constructed using the last 13 bases of the 3
end of 16S ribosomal RNA, which contains the hexamer complementary to the Shine-Dalgarno sequence [20]. We use minimum Hamming distance decoding to test the block code model of translation initiation. (The Hamming distance between two sequences is the number of positions where they differ when aligned. See [25] for a detailed description of the model.) Sequence data from the E. coli K-12 [26] strain MG1655 genome (downloaded from the National Institutes of Health ftp site: www.ncbi.nlm.nih.gov) is used to construct and test the model. Figure 3 shows the resulting mean minimum Hamming distance values by position for the (5, 2) block code model. The horizontal axis is the position of the RNA base relative to the first base of the start (initiation) codon. The vertical axis shows the ensample mean of the minimum Hamming distance values aligned for each of the three sequence groups (translated sequences, hypothetical translated sequences, and nontranslated sequences; categorized based on GenBank annotations). In general, the smaller the value on the vertical axis, the stronger the hydrogen bond formed between the ribosome

and the mRNA. Zero on the horizontal axis corresponds to the alignment of the first base of a codeword with the first base of the initiation codon. As Figure 3 illustrates, there is a significant difference among the translated, hypothetical, and the nontranslated sequence groups. For the translated and hypothetically translated sequence groups, a minimum distance trough occurs in the –15 to –10 regions. These key regions contain the nonrandom domain and the Shine-Dalgarno domain [27]. All the sequence groups in the (5, 2) block code model achieve a global minimum mean distance value in the –5 to 0 region. This is most likely a result of their shared initiation codons. Block-Code-Based Bayesian Classifier

Using the results of the block code model, we designed four Bayesian classification systems. The systems classify individual mRNA sequences as translation initiation sites or noninitiation sites based on the average minimum Hamming distance values in the −15 to −11 alignment window (this includes mRNA bases from position −15 to −7). The −15 to −11 window appears to provide the greatest distinction between the mean minimum Hamming distance values of leader (contains valid initiation site) and nonleader (contains invalid initiation site) sequences in E. coli K-12 (Figure 3). The components of a Bayesian classifier are s, a measurable classification variable; P(s/wi), the conditional probability of measuring a value of s given classification class w; P(wi), the probability of the occurrence of each classification class. The discrimination function for the classifier is P(wi |s) =

where i designates the classification classes and i = (Translated, Nontranslated)

(2)

and P(s) =

N class


P(s | wi)∗ P(wi).

(3)

i=1

1.8

P(wi | s) ≈ P(s | wi)∗ P(wi).

1.7 Mean Dmin

(1)

The constant Nclass is the number of classification classes; Nclass is two for the current work. Since P(s) is the same for all classification classes, we can (for the purpose of classification) simplify the discrimination function, (1), to

1.9

1.6

(4)

Measurable Classification Variable

In the discrimination function, the value of the classification variable s is derived from the sum of the positional Hamming distance values within the −15 to −11 alignment window:

Translated =*– Hypothetical =.– Nontranslated=Solid

1.5 1.4 1.3

−11


s=

1.2 1.1 −30

P(s |wi) ∗ P(wi) , P(s)

Davgp ,

(5)

p =−15

−20

−10

0 Position

10

20

30

Fig. 3. Results of minimum distance block decoding model for the (5, 2) code.

92 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

where Davgp =

N 1
dminp, j . N j=1

(6)

JANUARY/FEBRUARY 2006

The positional Hamming distance value Davgp is the average of the N lowest Hamming distance values dminp, j for the subsequence that begins at position p. In this work, N is set to a value that equals 10% of the total number of codewords in the codebook. Below are the steps for calculating s for a given mRNA subsequence, R−15..−7 (alignment positions are from −15 to −11). 1) Set s to zero and position counter, p, to position −15. 2) For position p, find the Hamming distance between subsequence Rp..(p+n−1) and all codewords in the codebook [25]. 3) Find Davgp using the Hamming distance values calculated for subsequence Rp..(p+n−1) . 4) Increment s: s = s + Davgp . 5) Increment p: p = p + 1. 6) If p > −11 then exit, else goto Step 2. To illustrate, let the (n = 5, k = 2) block code model produce the following set of ten valid codewords (a codebook): AAGGU AAGUG AAAUC GCAGG GCGGA GCGAG CUGUG CUAUC UGAAG UCGGU

Table 1. Hamming distance between example codeword set and CCCCC. Codeword

DHamming

AAGGU

5

AAGUG

5

AAAUC

4

GCAGG

4

GCGGA

4

GCGAG

4

CUGUG

4

CUAUC

3

UGAAG

5

UCGGU

4

s = 0 + Davg−15 = 3.5 . ➤ Increment p:

p = −15 + 1 = −14 . ➤ Continuing as illustrated in the previous steps, we find that

for alignment positions p = −15.. − 11, Davg−15..−11 is

for N = 0.20∗ 10 = 2,

DHamming

Codeword

Davg−15..−11 = (3.5 3.5 3.5 3.5 3.5) ,

(7)

and the value for the classification variable, s, is: and as a simple example, assume the mRNA subsequence (the received parity stream) from position −15 to position −7 is a repetitive sequence of cytosine bases: R−15..−7 = C C C C C C C C C.

(8)

The s value for R−15..−7 is calculated as follows:

➤ Set s = 0 and p = −15 ➤ For p = −15,

R−15..(− 15 + 5 − 1) = R−15..−11 = C C C C C . Table 1 lists the Hamming distance between subsequence R−15..−11 and all codewords in the example codebook. ➤ The N = 2 lowest Hamming distance values from Table 1 are three (corresponding to codeword CUAUC) and four (corresponding to codeword AAAUC). Using (6), Davg−15 is: Davg−15 =

1 (4 + 3) = 3.5 . 2

➤ Increment s:

The distribution for s of the training set of E. coli leader and nonleader sequences are shown in Figure 4 and Figure 5, are formed. In the probability distribution function (PDF), Figure 2, and in the cumulative distribution function (CDF), Figure 3, the horizontal axes are the s values, and the vertical axes are the probability of the s value occurring for the translated (valid leader) and nontranslated (invalid leader) training set models. A Wilcoxon Rank-Sum test applied to the two training sets verified that their corresponding

(5,2) PDF for Nontranslated Sequences Probability

Probability

Defining the Statistical Model: P(w i | s)

0.06

0.06 0.04 0.02 16

17

18

19

20

(9)

This process is used to calculate the s statistic for every mRNA subsequence in the training and test sets. We compiled our data set using GenBank sequences and annotations. All open reading frames (with AUG on the start codon) on the noncomplement strands that were not listed as a gene were categorized as nontranslating genes. All genes on the noncomplement strand were categorized as translating genes. The data set (translating and nontranslating) was divided in half to form the training and testing data sets.

(5,2) PDF for Translated Sequences

0.08

0 15

s = 3.5 + 3.5 + 3.5 + 3.5 + 3.5 = 17.5.

21

22

0.04 0.02 0 15

16

17

18

19

20

21

Sum of Davg Values for Pos=−15 to −11

Sum of Davg Values for Pos=−15 to −11

(a)

(b)

22

Fig. 4. Probability distribution of s values for the (5, 2) block code model.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

93

(5,2) CDF for Nontranslated Sequences 1

0.8

0.8

Probability

Probability

(5,2) CDF for Translated Sequences 1 0.6 0.4 0.2 0 15

16

17

18

19

20

21

22

0.6 0.4 0.2 0 15

Sum of Davg Values for Pos=–15 to –11 (a)

16

17

18

19

20

21

22

Sum of Davg Values for Pos=–15 to –11 (b)

Fig. 5. Cumulative distribution of s values for the (5, 2) block code model.

probability distributions were nonidentical. Both the PDF and CDF are used to model the probability that s occurs, given that we are in class wi . The PDF model is the probability of a single s value occurring in a given classification group, P(S = s | wi); the CDF model is the probability of a range of s values occuring, P(S ≤ s | wi). Incorporating Prior Knowledge, P(w i )

Two approaches for defining P(wi), the probability that class wi occurs, are investigated. One approach defines prior probabilities by assessing the number of valid leader regions and the number of invalid leader regions in the E. coli genome. Valid leader regions are leaders that are associated with an annotated gene, as designated in the E. coli K-12 MG1655 genome [26] (available through GenBank). Invalid leaders are sequences upstream from an open reading frame that is not designated as a valid gene in GenBank. The open reading frames associated with invalid leaders must contain at least 33 codons. We estimate the prions by taking the ratio of valid leaders to the total number of potential leaders and the ratio of nonvalid leaders to the total number of potential leaders:

0.08 0.07

Translated

Probability

0.06 0.05 0.04

Nontranslated

(10) (11)

The second approach for defining P(wi) uses the coding theory framework on which our model is constructed. From a coding theory view, the decoder has no prior knowledge regarding the probability that a received parity sequence (the mRNA leader) being a valid or an invalid leader. Therefore, each class can be viewed as equally probable. This results in the following prior probability values: P(wTranslated ) = 50%, P(wNontranslated ) = 50%.

(12) (13)

Both approaches are used in our classification system. Bayesian Classification Systems

We form four discriminate functions using all combinations of equal and unequal priors. ➤ Classification System 1: Uses PDF and unequal prior probabilities. ➤ Classification System 2: Uses PDF and equal prior probabilities. ➤ Classification System 3: Uses CDF and unequal prior probabilities. ➤ Classification System 4: Uses CDF and equal prior probabilities. Given the discrimination function, the rule for deciding to which class a received parity sequence (the mRNA test sequence) belongs to is: IF P(wTranslated | s) > P(wNontranslated | s)THEN

0.03

Select wi = wTranslated

0.02

ELSEIF P(wNontranslated | s) > P(wTranslated | s)THEN

0.01 0 16

P(wTranslated ) = 9.39%, P(wNontranslated ) = 90.61%.

17 18 19 20 21 Sum of Davg Values for Pos=–15 to –11

22

Select wNontranslated ELSE

Fig. 6. Probability distributions used for the (5, 2) Bayesian classifiers.

94 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Indicate a tie occurred. JANUARY/FEBRUARY 2006

To illustrate, using the statistic calculated in (9), we can classify our example mRNA “received” sequence, R−15..−11 . For R−15..−11 , the classification variable s equals 17.5. The discrimination functions for Classification System 1 and P(wTranslated | s) and P(wNontranslated | s) are calculated as follows: ➤ Discrimination function for translated class: P(wTranslated | s = 17.5) = P(17.5 | wTranslated )∗ P(wTranslated ) (14) = 0.05∗ 0.0939 = 0.0047.

➤ Discrimination function for nontranslated class:

P(wNontranslated | s = 17.5) = P(s | wNontranslated ) ∗ P(wNontranslated ) = 0.015 ∗ 0.9061 = 0.0136. (15) Since the value of the discrimination function for the nontranslated class is greater than that of the translated class, the example sequence is classified as a nonvalid leader region.

Table 2. Size of training set and test set. Number in Training Set Leader Non-eader

Number in Test Set

1,459

1,458

10,520

10,519

Table 3. Results of Bayesian Classifiers for (5, 2) Block Code (values are in %). True Positive

False Positive

False Negative

True Negative

Classifier 1

19.91

1.78

80.09

98.22

Classifier 2

67.90

20.28

32.10

79.72

2.52

73.27

97.48

0.00

0.00

Classifier 3 Classifier 4

26.73 100

100

Table 4. Classification rates (in %) for (5, 2) Bayesian classification systems.

Results

The four classification systems are implemented using the codebook generated from the (5, 2) block code model for E. coli K-12 genomic sequences [25]. Table 2 gives the number of sequences in the training and testing data groups. The training set is used to construct the statistical models for the four classification systems and the classifiers are applied to the sequences in the testing set. Table 3 shows the classification results for the four Bayesian classifiers. The true positive and false positive rates for each classifier in Table 3 are calculated as follows [27]: # of leaders correctly classified (16) total # of leader sequences # of nonleaders in correctly classified False Positive = total # of nonleader sequences (17) True Positive =

True negative and false negative rates are calculated in a similar manner to (16) and (17), respectively. Of the four classification systems, Classifier 2 seems to perform the best, classifying leader and nonleader sequences equally well while maintaining a relatively low rate of incorrect classifications. Table 4 shows the correct versus incorrect classification rates for all four classification systems. The incorrect classification is calculated as follows [29]:

%Incorrect = 1.0 −

TruePos + TrueNeg . TruePos + FalsePos + FalseNeg + TrueNeg (18)

The correct classification value flows naturally from (18). IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Correct Classification

Incorrect Classification

Classifier 1

59.065

40.935

Classifier 2

73.81

26.19

Classifier 3

62.105

37.895

Classifier 4

50

50

Discussion

Classifiers 1 and 3 have correct classification percentages above 50%, as shown in Table 4. This is elevated rates of accuracy are a result of very high specificity or true negative values. Classifiers 1 and 3 are able to effectively detect “errors” (sequences that are not part of the codebook set). Their high error detection rate (ability to accurately classify nonleader sequences) is heavily biased by the use of unequal prior probabilities. Since the prior probability for nontranslated sequences is large, only sequences with very few errors or deviations from the coding model can be detected. From a coding theory perspective, the classifiers detect errors extremely well. From an engineering perspective, a decoding system like Classifiers 1 and 3 wastes resources because it causes the transmitter to resend information multiple times. Multiple retransmission would be necessary since the decoding system fails to recognize slightly errored transmissions. The biological parallel to Classifiers 1 and 3 is a system where only “perfect” sequences are recognized as translation initiation sites. Such a system may not be evolutionally viable. Classifier 4 has the inverse problem. It fails from a coding theory standpoint. Classifier 4 fails to detect any errors, since it classifies all received sequences as valid. The biological system represented by Classifier 4, which indiscriminately initiates translation at all potential initiation sites, would exist below Eigen’s error threshold for viable mutants [24]. Such a system would also be evolutionally inviable. As an error-control decoding system, Classifier 2 outperforms the other classification systems. It detects received sequences with slight variations from the codeword set JANUARY/FEBRUARY 2006

95

67.9% of the time and detects nonsystem sequences at an even higher rate. For a system where a false positive classification (interpreting an invalid sequence as valid) is costly or detrimental to the system, Classifier 2 is not as desirable as Classifiers 1 and 3. Classifier 2 has a false positive rate of 20.28% while Classifiers 1 and 3 have false positive rates of 1.78% and 2.52%, respectively. For some communication systems, it is better to retransmit than to decode the information incorrectly. When compared to the other three classification systems, Classifier 2 represents the most biologically feasible system. It is able to detect a varied set of correct initiation sites, while rejecting sequences with errors beyond the error threshold. The biological equivalent of Classifier 2 has the greatest prospect for evolutionary viability. To improve the classification systems that use unequal prior probabilities, the block code model would have to produce codewords that have a greater minimum Hamming distance separation than the present code. Figure 4 shows the translated and nontranslated PDFs for the current (5, 2) block code. Reducing the region of overlap between the coding-based probability distribution of leader and nonleader sequence sets would increase the sensitivity of the classifier and reduce incorrect classification rates. To accomplish this, a more powerful error-control code must be designed. Such a code would contain codewords with larger Hamming distances between the sequences in the codebook set, thus increasing the minimum distance of the code. The larger the minimum distance of a code, the more errors it can detect and correct. Conclusion

The classification system presented uses an eleven base classification window to identify translation initiation sites. This is a relatively small decision window compared to other classification methods. The 74% correct classification rate of System 2 appears to be comparable to that of GeneMarkS (tested on a set of 195 experimentally validated E. coli genes [12]) after its intermediary Step 2 (67% accuracy following an initial coding region identification step); but GeneMarkS exceeds System 2 after intermediary Step 4 (85% following prediction using GeneMark.hmm). Upon completing all model iterations, the accuracy of GeneMarkS increases to almost 95%. We use genomic data from GenBank to test our classification system, which differs from the dataset used by GeneMarkS, Glimmer (71% correct classification rate), and ORPHEUS (76% correct classification rate). Their classification rates also reflect correct classification of both the 5
and 3
ends of the gene, which corresponds to the transcribed mRNA. A better comparison system for our work is Nishi et al.’s GeneLook system [10]. Using annotated gene sequences, GeneLook was able to accurately identify 76% of selected E. coli genes. Our 74% accurate classification rate is comparable to Nishi et al.'s. Our classification system reflects the genetic initiation process in several aspects. In practice, the small ribosomal subunit does not “analyze” the entire open reading frame before determining whether a three-base nucleic acid sequence is an initiation site. Likewise, the classification systems presented use a relatively small window to detect potential ribosome binding sites. Similar to the biological model, the error-control-based classifiers use the redundancy, or extra information, present in the mRNA leader sequence to locate valid translation initiation sites. 96 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

The results thus far are encouraging. They suggest that it is highly possible to implement an error-control coding-based scoring system that can be combined with Bayesian classification for detecting and possibly designing prokaryotic translation initiation sites. Elucidating how genetic systems incorporate and use redundancy, which is at the core of information-based error correction, and understanding the functional significance of genetic errors from a coding theory perspective will help provide insight into the fundamental rules that govern genetic regulatory systems. Acknowledgments

The authors would like to acknowledge the contributions of Dr. David I. Rosnick to this work and thank Dr. Anne-Marie Stomp for her extensive editorial comments. We also thank Dr. Jeffrey Thorne and Dr. Winser Alexander for their input into this work. Elebeoba May performed this work while at North Carolina State University. This work was supported in part by a National Science Foundation Minority Graduate Research Fellowship (Grant DGE-9616159) and a Ford Foundation Dissertation Fellowship. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. Elebeoba E. May received her Ph.D. in computer engineering from North Carolina State University and joined Sandia National Laboratories’ computational biology department in May 2002. Her research interests include the use and application of information theory, coding theory, and signal processing to the analysis of genetic regulatory mechanisms, the design and development of intelligent biosensors, and large-scale simulation and analysis of biological pathways and systems. She has served as an associate editor and reviewer for IEEE Transactions on Information Technology in Biomedicine, on the IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) 2004 organizing committee, and as chair and organizer of a special session on Communication and Coding Theory in Biology for the IEEE Engineering in Medicine and Biology Society (EMBS) 2003 annual meeting. Organizational memberships include: the IEEE EMBS, the IEEE Signal Processing Society, and the IEEE Information Theory Society. She is a recipient of the 2003 Women of Color Research Sciences and Technology Award for Outstanding Young Scientist or Engineer. Mladen A. Vouk received a Ph.D. from King’s College, University of London, the United Kingdom. He is the interim department head and professor of computer science and the associate vice provost for information technology at North Carolina State University, Raleigh. Vouk has extensive experience in both commercial software production and academic computing. He is the author/coauthor of over 180 publications. His research and development interests include software engineering, scientific computing (including application of engineering methods to JANUARY/FEBRUARY 2006

genetics, bioinformatics, and biophysics) information technology, assisted education, and high-performance networks. He is a member, former chairman, and former secretary of the IFIP Working Group 2.5 on Numerical Software, and a recipient of the IFIP Silver Core award. He is an IEEE Fellow, and a Member of IEEE Reliability, Communications, Computer, and Education Societies, and of the IEEE Technical Committee on Software Engineering. He is a member of ACM, ASQ, and Sigma Xi. He is an associate editor of IEEE Transactions on Reliability, a member of the editorial board for the Journal of Computing and Information Technology, and a member of the editorial board for the Journal of Parallel and Distributed Computing Practices. Donald L. Bitzer received his Ph.D. in electrical engineering from the University of Illinois in 1960. He was a professor of electrical and computer engineering at the University of Illinois from 1960–1989. He retired from the University of Illinois to become a distinguished university research professor in the Computer Science Department at North Carolina State University. Bitzer’s work has involved applying signal processing and coding theory to a variety of areas from radar signals and speech processing to the development of software and hardware required for large computer networks. The large educational computer systems PLATO and NovaNet are results of this research. His research led to the intelligent modems for telephone lines and cable systems as well as the flat plasma display panel now being used for television. More recently, his research has been directed toward using signal processing and coding theory to look for genomic information that controls the translation process in protein production. He has been granted numerous patents in the computer and electronic areas. Dr. Bitzer has been a member of the National Academy of Engineering since 1974. He is a member of the American Society for Engineering Education (since 1974), a fellow in the American Association for Advancement of Science (since 1983), a Fellow of the IEEE (since 1976), a fellow in the Association for Development of Computer Based Instructional Systems (since 1986), and a fellow of the International Engineering Consortium (since 1984). He has received numerous awards. In 1967, he received the Industrial Research 100 Award, and in 1973, he received the prestigious Vladimir K. Zworkin Award of the National Academy of Engineering for “outstanding achievement in the field of electronics applied in the service of mankind.” Other awards include the Chester F. Carlson Award (1981) from the American Society for Engineering Education for “Innovation in Engineering Education,” the Computer Science Man of the Year (1975) from the Data Processing Management Association, and the Education Award (1989) from the American Federation of Information Processing Societies. In 1982 he was named Laureate of the Lincoln Academy by the State of Illinois for contributions made “for the betterment of human endeavor.” In 2002 he received the National Academy of Television Arts and Sciences Emmy Award for Scientific Development and Technical Achievement for his invention and development of plasma displays. The College of Engineering at the University of Illinois awarded him with the Alumni Distinguished Service Award in 2004. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Address for Correspondence: Elebeoba E. May, Sandia National Laboratories, P.O. Box 5800 MS 0310 Albuquerque, NM 87185 USA. Phone: +1 505 844 9933. Fax: +1 505 844 5670. E-mail: [email protected]. References [1] T.D. Schneider, G.D. Stormo, L. Gold, and A. Dhrenfeucht, “Information content of binding sites on nucelotide sequences,” J. Mol. Biol., vol. 188 pp. 415–431, 1986. [2] D. Frishman, A. Mironov, and M. Gelfand, “Starts of bacterial genes: Estimating the reliability of computer predictions,” Gene, vol. 234, no. 2, pp. 257–265, 1999. [3] M. Tompa, “An exact method for finding short motifs in sequences, with application to the ribosome binding site problem,” in Proc. ISMB, 1999. [4] S.S. Hannenhalli, W.S. Hayes, A.G. Hatzigeorgiou, and J.W. Fickett. “Bacterial start site prediction,” Nucleic Acids Res., vol. 27, no. 17, pp. 3577–3582, 1999. [5] B.E. Suzek, M.D. Ermolaeva, M. Schreiber, and S.L. Salzberg, “A probabilistic method for identifying start codons in bacterial genomes,” Bioinformatics, vol. 17, no. 12, pp. 1123–1130, 2001. [6] M. Walker, V. Pavlovic, and S. Kasif, “A comparative genomic method for computational identification of prokaryotic translation initiation sites,” Nucleic Acids Res., vol. 30, no. 14, pp. 3181–3191, 2002. [7] A. Zien, G. Ratsch, S. Mika, B. Scholkopf, T. Lengauer, and K.R. Muller, “Engineering support vector machine kernels that recognize translation initiation sites,” Bioinformatics, vol. 16, no. 9, pp. 799–807, 2000. [8] E. Crowley, “A Bayesian method for finding regulatory segments in DNA,” Biopolymers, vol. 58, pp. 165–174, 2001. [9] H.-Y. Ou, F.-B. Guo, and C.-T. Zhang, “GS-Finder: A program to find bacterial gene start sites with a self-training method,” Int. J. Biochem. Cell Biol., vol. 36, pp. 535–544, 2004. [10] T. Nishi, T. Ikemura, and S. Kanaya, “GeneLook: A novel ab initio gene identification system suitable for automated annotation of prokaryotic sequences,” Gene, vol. 346, pp. 115–125, 2005. [11] T. Yada, Y. Totoki, T. Takagi, and K. Nakai, “A novel bacterial gene-finding system with improved accuracy in locating start codons,” DNA Res., vol. 8, no. 3, pp. 97–106, 2001. [12] J. Besemer, A. Lomsadze, and M. Borodovsky, “GeneMarkS: A self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions,” Nucleic Acids Res., vol. 29, no. 12, pp. 2607–2618, 2001. [13] W.S. Hayes and M. Borodovsky, “How to interpret an anonymous bacterial genome: Machine learning approach to gene identification,” Genome Res., vol. 8, no. 11. [14] A.G. Pedersen and H. Nielsen, “Neural network prediction of translation initiation sites in eukaryotes: Perspectives for EST and genome analysis,” in Proc. Int. Conf. Intell. Syst. Mol. Biol., 1997. [15] Y. Osada, R. Saito, and M. Tomita. “Analysis of base-pairing potentials between 16S rRNA and 5
UTR for translation initiation in various prokaryotes,” Bioinformatics, vol. 15 pp. 578–581, 1999. [16] T.D. Schneider, “Measuring molecular information,” J. Theor. Biol., vol. 201 pp. 87–92, 1999. [17] P. Sweeney, Error Control Coding an Introduction. New York: Prentice Hall, 1991. [18] S. Lin and D.J. Costello Jr., Error Control Coding: Fundamentals and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1983. [19] Y. G. Savchenko and A.A. Svistel’nik, “An approach to pattern recognition systems,” Engineer. Cybernetics, vol. 2, pp. 144–146, 1968. [20] B. Lewin, Genes V. New York: Oxford Univ. Press, 1995. [21] J. Shine and L. Dalgarno, “The 3'-terminal sequence of Escherichia coli 16S ribosomal RNA: Complementarity to nonsense triplets and ribosome binding sites,” Proc. Nat. Acadamy Sci., vol. 71, no. 4, .pp. 1342–1346, Apr. 1974,. [22] E.E. May, M.A. Vouk, D.L. Bitzer, and D.I. Rosnick, “An error-correcting code framework for genetic sequence analysis,” J. Franklin Instit., vol. 341, pp. 89–109, 2004. [23] G. Battail. “Does information theory explain biological evolution?,” Europhysics Lett., vol. 40, no. 3 pp. 343–348, Nov. 1997. [24] M. Eigen, “The origin of genetic information: Viruses as models,” Gene, vol. 135, pp. 37–47, 1993. [25] E. May, M. Vouk, D. Bitzer, and D. Rosnick, “Coding theory based models for protein translation initiation in prokaryotic organisms,” BioSyst., vol. 76, pp. 249–260, 2004. [26] F.R. Blattner, Plunkett, G. III, C.A. Bloch, N.T. Perna, V. Burland, M. Riley, J. Collado-Vides, J.D. Glasner, C.K. Rode, G.F. Mayhew, J. Gregor, N.W. Davis, H.A. Kirkpatrick, M.A. Goeden, D.J. Rose, B. Mau, and Y. Shao, “The complete genome sequence of Escherichia coli K-12,” Sci., vol. 277 no. 5331 pp. 1453–1474, 1997. [27] L. Gold and G. Stormo, “Translational initiation,” in Escherichia coli and Salmonella typhimurium, Cellular and Molecular Biology, pp. 1302–1307, 1987. [28] F. Provost and T. Fawcett, “Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions,” in Proc. 3ird Int. Conf. Knowledge Discovery Data Mining, 1997. [29] G.M. Weiss and F. Provost, “The effect of class distribution on classifier learning,” Rutgers Univ., New Brunswick, NJ, Tech. Rep. ML-TR-43, 2001.

JANUARY/FEBRUARY 2006

97

Issues in Ethics moral blindness and the Guidant recall John Fielder

ast March, Joshua Oukrop, a 21year-old college student from Minnesota, was on a bicycling trip in Moab, Utah, with his girlfriend. He complained of fatigue then fell to the ground and died. He suffered from a relatively common genetic disease, hypertrophic cardiomyopathy, which can cause cardiac arrest. A defibrillator can shock the heart back to its normal function, but it must be used within a short time or the person will die. Before 1985, people like Joshua would have to rely on external defibrillators carried by emergency responders. Bicycling in remote areas far from emergency help would mean almost certain death if the heart lost its rhythm and began beating chaotically . In 1985, the U.S. Food and Drug Administration (FDA) approved the first implanted defibrillator, which would sense the heart’s arrhythmia and provide a strong electric shock to restore it to normal. Manufacturers and physicians call this “providing therapy”; patient’s call it “getting zapped.” A full shock has been described as being kicked by a mule or being in bed and having a bowling ball dropped on your chest from the ceiling. But it is a lifesaver: roughly 1,000 people die each day from arrhythmias, the leading medical cause of death. Implanted defibrillators are usually highly reliable, but like any other sophisticated technology, there are unanticipated failure modes that can render the device useless. For patients like Joshua Oukrup, this is a lifethreatening situation. Guidant determined that the problem with its defibrillator was a short circuit that drained the battery, rendering the device unable to provide a life-saving shock to the heart.

L

98 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

After Joshua’s death, four Guidant officials met with his Minneapolis cardiologist, Dr. Barry Maron. What is remarkable about the meeting is that Guidant offered to replace the device in his other 60 patients but did not plan to notify the physicians of the roughly 24,000 other patients who had the same device. Their stated reason was that their statistics on its failure rate did not warrant an advisory and that replacement surgery might pose greater risks. Dr. Maron responded with the prescient remark, “To be perfectly honest, I think this is the biggest mistake you’ll ever make.” Dr. Maron and a colleague, Dr. Robert Hauser, decided to investigate the problem. In the vast database of FDA reports of medical device problems, they found other reports of similar failures. Guidant later revealed that in April 2002, three years before Joshua Oukrup died, it redesigned the defibrillator to fix the flaw that caused the short circuit. Later it was discovered that Guidant had sold flawed devices that were still in inventory after the redesign. It doesn’t take much imagination, although apparently more than Guidant officials could muster, to see that physicians who were not informed of the problem and/or implanted the flawed devices would feel betrayed and angry. Add to this list patients, members of Congress, and the public, and we can see that Dr. Maron’s prediction was right on target. Drs. Hauser and Maron took their concerns to the New York Times, which planned to publish an article on the issue in their 24 May edition. Just hours before publication, Guidant responded with an advisory. In it, Guidant recommended not replacing the device. 0739-5175/06/$20.00©2006IEEE

One of my jobs as a consultant is to tell senior management how their decisions will be seen “on the street.” Those who are deeply invested in the success of their product and interact almost entirely with others who share this mind-set are highly susceptible to developing a bad case of moral blinders. While Guidant officials are repeating their mantra about reliability statistics, there is a strong urge to grab them by the lapels and say “A young man died because your device failed and you didn’t give his doctor the information he needed that could have saved his life.” Follow the Money

Apart from reliability issues, there are strong financial concerns associated with advisories. Dr. Maron and others might have decided to replace the device in some of their patients, and they might have replaced them with a defibrillator made by another manufacturer. Doctors and patients who feel betrayed by the manufacturer have a strong incentive to go to competitors for replacements. To give you an idea of the dimensions of this response, 47 patients at the Minneapolis Heart Institute Foundation with the unfixed Guidant defibrillator were notified about the defect. As of 5 July, nine patients had already had their devices replaced, and five more were scheduled for replacement. Several other models of Guidant defibrillators suffered from the same design flaw. All told, there are 20,600 patients with implanted Guidant defibrillators subject to the kind of short circuit that led to Joshua Oukrup’s death. More serious than an advisory is a recall, particularly a Class I recall, which is issued when “there is a JANUARY/FEBRUARY 2006

reasonable probability that if a particular device is malfunctioning, the malfunctioning device will cause serious adverse health consequences or death.” The FDA classified Guidant’s advisory on the Oukrup defibrillator, and two other models, as a Class I recall.

healthcare professionals, must decide how to respond to them. Guidant apparently thought that the risks were small enough to be acceptable, but it is the individual patient’s right to decide what risks are acceptable for her or him, not the manufacturer.

The Ethics of Warning

Every implant carries risks. The FDA premarket approval process is designed to minimize the risks through a lengthy process of testing, quality control, and clinical trials. Patients are informed of these risks and must give their consent to accept them before receiving an implant. The ethical basis of this practice is respect for patient autonomy: the idea that patients are in charge of their lives that and they have the right to decide what risks to take. Physicians and other healthcare professionals have the corresponding responsibility to adequately inform patients of the alternative treatments, their risks and benefits as well as the consequences of choosing nontreatment. This may result in choices that others may think are not in the patient’s best interests, but ethically it is up to the patient to decide what those best interests are. While many risks can be determined by premarket testing, only a relatively small number of persons participate in a clinical trial of the device for reasons of safety and economy. In contrast, putting the device on the market means that a very large number of people receive the device, so additional risks may be discovered. Furthermore, clinical trials typically involve better practitioners, more resources, and more controls—in short, better medicine. Marketing an approved device is, therefore, similar to a second clinical trial, particularly in the sense that it can reveal unknown risks. It is obvious that if new, significant risks are discovered, that patients and the appropriate healthcare professionals must be warned. Patients have given their informed consent to the set of risks determined in clinical trials and testing; new unforeseen risks mean that patients, in consultation with their IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Doctors and patients who feel betrayed by the manufacturer have a strong incentive to go to competitors for replacements. Risk is the product of the probability of harm and the severity of harm (R = P × S). Even though the probability of failure is low, if the severity is high (death), there is an obligation to warn. Guidant’s defense has emphasized the high reliability of its device (the low probability of harm), while physicians and patients were understandably focused on the severity of harm (death). Conclusions

Guidant clearly had an obligation to issue a warning about the problem with its defibrillator. It was shamed into issuing an advisory just before its lack of disclosure was revealed in a newspaper. Equally telling is the company’s willingness to replace the defibrillators in Dr. Maron’s patients, while keeping silent about the thousands of other patients with the same device. It was a shortsighted form of damage control, not concern for the patients and physicians who trusted them. Finally, the decision to sell the unfixed defibrillators still in inventory indicates either a breathtaking lack of understanding of how others will view it or a narrow focus on the bottom line. Think of it

this way: Would a patient or doctor knowingly choose a defibrillator with a known lethal flaw over one that had been redesigned to eliminate it? I suspect that Guidant gambled that the low probability of harm would not result in a device failure that resulted in significant injury. It was still highly reliable, even with the unexpected flaw. This is an approach that looks at the entire population of patients and the statistical risks of device failure. That population is not significantly worse off with the unfixed defibrillators, so there is no reason not to sell them. What this leaves out is the perspective of the doctors who rely on Guidant and other manufacturers to tell them what they need to know in order to care for their patients and the perspective of the patients who have the right to decide what is or is not an acceptable risk. Most mistakes of this kind are not made by evil, uncaring people. They usually result from moral blindness, not being able to see the perspectives of others and how their interests will be affected. Reading the transcript of an interview with Fred McCoy, president of Guidant’s cardiac rhythm management division, shows clear symptoms of this malady. If he has any sense of why doctors and patients are angry, it does not come out in the interview. What does come out is prudence: people want more information, so we will give it to them. Imagine how different the impression would be if he began like this: “We apologize to the doctors who implanted our defibrillators and to the patients that received them. We should have known that the information we withheld was important to them. We promise to do a better job of communicating in the future.” Author’s Note: The information for this column came from numerous newspaper reports, mainly in the New York Times and an article, “The Controversy over Guidant’s Implantable Defibrillators,” by Robert Steinbrook in the New England Journal of Medicine (vol. 353, no. 3, July 21, 2005, pp. 221–224). JANUARY/FEBRUARY 2006

99

Patents Maurice M. Klee

what a difference a day made

n 12 July 2005, the 12 active judges of the Court of Appeals for the Federal Circuit (CAFC) handed down a landmark decision on claim interpretation in the case of Phillips v. AWH. For the first time, the court set down in detail an agreed-upon, comprehensive process for interpreting patent claims. In particular, the court clarified a set of conflicting cases on the role of dictionaries in the claim interpretation process. Perhaps the two litigants most dramatically affected by the Phillips decision were Ron Nystrom and the Trex Company, maker of the widely sold Trex decking product. Nystrom, the owner of a two-truck, two-employee lumberyard, invented an improved board for decks. The board had a curved top surface that automatically shed water, but was still flat enough so that furniture would not rock and the board felt comfortable when walked on. He obtained a patent on his new board and he sued Trex for infringement in 2001. As is standard, the district court conducted a Markman hearing to determine the meaning of the words used in Nystrom’s claims. The key word at issue was “board.” Trex said that the word should be limited to boards made of wood because Nystrom’s patent only discussed wood boards and made no mention of plastic-based materials of the type Trex used for its decking. Trex also pointed to the definition of the word “board” in Webster’s Third International Dictionary: “a piece of sawed lumber of little thickness but considerable surface area.” Nystrom also turned to the dictionary—but a different one. The American Heritage Dictionary of the English Language had a definition like Webster’s but also had an alternate def-

O

100 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

inition that read: “a flat piece of wood or similarly rigid material adapted for a special use.” Plainly, Trex’s boards were made of a “rigid material” and, thus, Nystrom argued that under this definition the word “board” covered Trex’s products.

The court clarified a set of conflicting cases on the role of dictionaries in the claim interpretation process.

The district court sided with Trex. It looked to the specification of Nystrom’s patent and its prosecution history before the Patent Office and became convinced that the patent was only directed to wooden boards. Nystrom appealed to the CAFC and, in 2004, the CAFC reversed the district court. In essence, the CAFC adopted Nystrom’s argument that under the definition in the American Heritage Dictionary, Trex’s product was a “board.” It thus sent the case back to the district court for a full trial on Nystrom’s patent infringement charge. Then came the Phillips case. Immediately after Phillips came down, Trex asked the CAFC to reconsider its 2004 decision. Trex argued that Phillips had rejected the process used in the 2004 decision, i.e., the process where dictionary definitions could be used to expand the scope of a claim, and had substituted the requirement that the claim language itself, plus the specification

and prosecution history, had to be the starting points and primary materials used in interpreting the words of a claim. According to Trex, the district court’s original Markman hearing had done just that and, thus, the original decision should be affirmed, rather than reversed as in 2004. Although rehearings of decided questions are the exception and not the rule, the CAFC agreed to consider Trex’s request. After reviewing the text of the Nystrom patent and its prosecution history in light of Phillips, the court withdrew its 2004 decision and substituted a new decision in which Trex, rather than Nystrom, won. American Heritage’s alternate definition, which had played such a key role in the first decision, was now dismissed as being “relatively obscure.” So 12 July 2005 was a heck of day for Nystrom. Although he did not know it at the time, within a few months he was going to go from being the little guy with the big guy backed into a corner to being out of court because his patent only covered wooden boards and Trex’s were made of plastic. What a difference a day made. Maurice M. Klee practices patent, trademark, and copyright law in Fairfield, Connecticut. He received a B.S. degree in physics from the University of Illinois, a Ph.D. in biomedical engineering from Case Western Reserve University, and a J.D. from George Washington University. He is a member of Phi Beta Kappa and Order of the Coif. He is a former assistant professor in the College of Engineering at Michigan State University and a former staff fellow at the National Institutes of Health. Copies of the full text of the Trex and Phillips decisions can be obtained from the CAFC’s Web site at http://www.fedcir.gov. JANUARY/FEBRUARY 2006

Retrospectroscope who was the first genetic engineer? L.A. Geddes

T

hese days, we hear a lot about genetics and its role in identifying criminals and coaxing bacteria to produce drugs that previously were obtained from plant and animal tissues. However, long before the laws pertaining to reproduction (genetics) were known, plant and animal breeding was widely performed, largely on a hit-or-miss basis. It is probably true that an art becomes a science when predictive laws are discovered. The law that predicts the characteristics of the offspring was not discovered by a biologist, biochemist, or physician. It was discovered by a clergyman, Gregor Mendel (1822–1884), who used no instruments, only his power of observation and patience. Mendel observed that the seeds of some garden peas produced tall plants while the seeds of others produced plants with less-developed characteristics. The modern term for such a distinguishing characteristic is phenotype. Dominant is the term used to describe the prominent feature, and recessive is that used to describe the attenuated feature. Mendel found that by breeding two peas, one with dominant characteristics and the other with recessive characteristics, one half of the offspring contained dominant and recessive characteristics and the other half

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

consisted of those with only dominant and only recessive characteristics. Putting this result into a mathematical equation yields

The law that predicts the characteristics of the offspring was discovered by a clergyman.

DR + DR → 2DR + DD + RR , where D stands for dominant and R represents recessive. Mendel was born in 1822 in Heinzendorf, Moravia. He conducted his experiments in the monastery garden at Alt-Brünn in Moravia, where he entered the Augustinian order as a novice in 1843. Mendel attended the University of Vienna for two years and then returned to Brünn, where he taught in the town school for 14 years. It was during this time that he conducted his experiments. Mendel became Abbott of the monastery in 1868.

0739-5175/06/$20.00©2006IEEE

Mendel founded the Brünn Society of Natural Science (Naturforscher Verein in Brünn). His famous paper, “Versuche über Planzenhybriden,” was published in the transactions of the Verein for 1866, where it remained buried and unnoticed for 35 years until Hugo de Vries made the same discovery in 1900. Mendel’s law, which has had a tremendous influence on medical and biological thinking, postulates that in the generation of hybrids one half of the progeny will breed true to the parental characteristics and the other half will be equally divided between offspring possessing only the dominant or the recessive characteristics. Mendel lived at a time when the first engineering schools were being created and the only degree granted was in civil engineering. Biological/biomedical engineering did not begin its development until after the end of World War II in 1945. However, if Mendel had lived at a time when bioengineering existed, he would undoubtedly be the first genetic engineer. References [1] L.A. Geddes, “A bibliography of biological telemetry,” Amer. J. Med. Electron., vol. 1, no. 5, pp. 294–298, Oct.–Dec. 1962. [2] L.A. Geddes, “Baylor medical college-preparing for the space age,” IEEE Eng. Med. Biol. Mag., pp. 50–63, Sep. 1991. [3] H.E. Hoff and L.A. Geddes, “The beginnings of graphic recording,” ISIS, vol. 53, part 3, no. 173, pp. 286–324, 1962.

JANUARY/FEBRUARY 2006 101

Engineering in Genomics

Stina Gestrelius and Mikael Oerum

cluster formation as a tool for development in Medicon Valley

ince the inception of Silicon Valley in California, hundreds of high-tech clusters have been created in various places of the world, and their numbers continue to grow as the cluster formation is increasingly adopted as an important economic development tool. Clusters are generally organized in order to stimulate the growth and competitiveness of a region in a particular technological area. In particular, knowledge-based clusters involve industry, public authorities, and research organizations, and leaders from each use such cluster formations to create new partnerships and collaborative structures. There is no particular blueprint for clusters. Clusters occur in many shapes and forms, and a specific cluster development is the product of a dynamic and interactive process among the key stakeholders in the specific region [1]. The most common objectives found in clusters are ➤ fostering networks among people ➤ establishing networks among firms and public research-based organizations ➤ promoting innovation and new technologies ➤ creating a brand for the region ➤ providing business assistance ➤ promoting the expansion of existing firms ➤ facilitating greater innovation ➤ attracting new firms and talent to the region ➤ promoting exports from the cluster. Any region seeking to succeed in high-tech fields must have stakeholders that are able to collaborate and innovate across sectors. Abundant lessons from existing clusters suggest that a collaborative approach will accelerate results. This approach is achieved by combining the scientific,

S

102 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

entrepreneurial, financial, and infrastructural elements into a globally viewed competitive approach that is focused on specific goals. A Cross-Border Bioregion in Southern Scandinavia

Medicon Valley (http://www.mediconvalley.com) is one of the oldest and most mature bioregions outside of the United States and is a key center for the medico/human life science research in Scandinavia. Geographically, the region covers the greater Copenhagen area (the capital of Denmark) and the southern tip of Sweden, an area comprising approximately 3 million inhabitants (22% of the total population in Denmark and Sweden). As an international hub in northern Europe, Copenhagen Airport has 15 million international passengers annually. It’s a one-hour drive from one end of Medicon Valley to the other, and 14 universities are located there. More than

10,000 private and public researchers in the biomedical and clinical areas are conducting worldclass research (see Figure 1). Prominent research areas are diabetes, inflammation, neurology, and cancer. There are 140,000 university students, of which 15–20% choose to study within the biomedical field. Medicon Valley also contains 26 hospitals (11 are university hospitals); seven science parks; several pharmaceutical companies including Novo Nordisk, AstraZeneca, Lundbeck, Leo Pharma, and Ferring; and more than 250 biotech and medtech companies with approximately 41,000 employees (40% of all employees are in these industries in Sweden and Denmark). Inspired by Silicon Valley and the Oresund strait between Denmark and Sweden, Medicon Valley got its name in the beginning of the 1990s and was established officially in 1996 as a university project supported by the European Commission.

Fig. 1. A bird’s-eye view on the two nation’s biocluster Medicon Valley.

0739-5175/06/$20.00©2006IEEE

JANUARY/FEBRUARY 2006

Medicon Valley Academy: A Triple-Helix Bio-organization

In 1997, the Swedish/Danish cross-border network organization Medicon Valley Academy (http://www.mva.org) was founded by the universities in the region to integrate the universities, hospitals and pharma, medtech, and biotech companies around Oresund. Since the Oresund strait is the border between the two Nordic countries, the integration into Medicon Valley is therefore also between two nationalities with similar, but definitely not identical, languages or systems. In 2000, Medicon Valley Academy became a true triple-helix bioorganization when industry companies joined the organization as members (Figure 2). Since then, the Medicon Valley Academy members have been biotech and medtech companies, major pharma companies, all the relevant university faculties, hospitals, service providers, and public organizations in the Oresund area. Accordingly, Medicon Valley Academy is born with the task to promote research, innovation, and commercialization across the board and to enhance network formation and knowledge transfer among all the players of Medicon Valley. Medicon Valley Academy is coordinating two cross-border research programs, a Ph.D. program, and a postdoctoral program; these are jointly funded (either regionally or nationally) by both Sweden and Denmark. The Medicon Valley Ph.D. program is a good example of a successful technology transfer under the auspices of Medicon Valley Academy. Each of the Ph.D. projects is the collaborative project of a university or hospital and a company in the region. The student works with tutors and other project team members and conducts research at both locations, thereby promoting knowledge transfer between the public and private sectors. Medicon Valley Academy organizes a large number of focused networking events, seminars, conferences, and various meetings between all or a select group of the triple-helix players in the IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

region. The annual Scandinavian BioTech Forum Science Conference is the most important regional meeting place for the dissemination of scientific results. The BioTech Forum, held in Copenhagen, had 10,000 visitors in 2004 and is the largest of its kind in Scandinavia. Lord Sainsbury of Turville, the United Kingdom Minister for Science and Innovation, gave the opening address naming Medicon Valley as the most attractive bioregion in Europe. A Two Nations Innovation System Rules

The national innovation systems in Denmark and Sweden are quite different from each other. The public and private research funding in Sweden amounts to more than 4% of GDP, the highest percentage worldwide. The majority, almost 75%, is performed in-house by the big companies like AstraZeneca and Ericsson. Denmark, with research spending just below 3% of GDP, tradi-

tionally has a large number of research institutes for performing applied research in cooperation with industry. In 2003, the European Commission published the Biotechnology Innovation Scoreboard (BIS), which compares the performance of industry in each of the European Union member states, Norway, Switzerland, the United States, Canada, and Japan. The analysis showed that Denmark and Sweden were two of the leading countries as seen in nine out of the 12 indicators used by the European innovation evaluation system. Sweden leads in terms of biotechnology publications and the number of dedicated biotechnology companies. It also has the best public understanding of biotechnology. Denmark is the topperforming country in terms of drug approvals and U.S. patents. As of four years ago, the Danish universities share intellectual property (IP) rights with university inventors

Universities

MEDICON VALLEY Hospitals

Knowledge ON Knowledge

ACADEMY

Enterprises

Fig. 2. The triple helix in Medicon Valley joins together the universities, hospitals, and enterprises in Medicon Valley Academy.

JANUARY/FEBRUARY 2006 103

Engineering in Genomics (continued)

EXAMPLES OF TECHNOLOGY TRANSFER AND INNOVATION ORGANIZATIONS IN MEDICON VALLEY CAT/Symbion Innovation: www.catsymbioninnovation.dk CONNECT Denmark: www.connectdenmark.com CONNECT Skåne: www.connectskane.se DTU Innovation: www.dtu-innovation.dk Forskarpatent i Syd: www.forskarpatent.com H:S Innovation: www.hs-innovation.dk LUAB (Lund University): www.lu.se/ind-liaison/luab Lumitec: www.lumitec.se Technical University of Denmark/Patent office: www.adm.dtu.dk/erhverv/ patent Teknologisk Innovation: www.tekinno.dk Teknopol: www.teknopol.se TeknoSeed: www.teknoseed.se University of Copenhagen/Tech transfer office: www.tt.ku.dk

and are obliged to commercialize as much as possible. In Sweden, the IP rights still belong to university employees, who are free to decide when to publish and when to attempt commercialization. High Critical Mass in Biomedical and Clinical Research Areas

A 2004 comparison study demonstrated that Medicon Valley on an international scale has a very high publication rate within basic biomedical research, experimental clinical research, and clinical research. The annual research output in

terms of publications from Medicon Valley (Denmark and Sweden), Cambridge, Oxford, and Edinburgh (United Kingdom), Stanford University, California (United States), and Helsinki (Finland) are shown in Figure 3. Excellent public and private research areas in Medicon Valley include diabetes, inflammation, neuroscience, and cancer. Furthermore, the region is strong within disease control and epidemiology, with the Danish National Serum Institute and World Health Organization’s regional center for Europe located in Copenhagen.

Papers per Year in SCI/SSCI

3,500 MV

3,000

CB

2,500

SU HF

2,000

OX 1,500

EH

1,000 500 0

1997 1998

1999 2000

2001 2002

Fig. 3. Annual output of publications 1997–2002 within basic biomedical research, experimental research, and clinical research from Medicon Valley, Cambridge, Stanford, Helsinki, Oxford, and Edinburgh. (Source: City University of London.)

104 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Other strongholds include methodologies such as nanobiotechnology, proteomics, stem cell research, bioinformatics, and clinical trials. The stem cell research is favored by a fairly liberal policy in Sweden and has attracted international scientists as well as overseas research funding. Four years after the bridge between Denmark and Sweden was inaugurated, the universities have established common courses, allowing students to select from across the border. Certain education disciplines are only found on one side and must then benefit all in the region. An important example is the Danish University of Pharmaceutical Sciences, as there is no Swedish equivalent near the pharmaceutical companies in the south of Sweden. Hosting Continuous Growth During the Last Decade

Since the mid-1990s, Medicon Valley has experienced a continuous increase in the number of biotech companies (Figure 4). The growth rate showed a steady increase until 2000 and, surprisingly, experienced a new peak in 2004, stressing the position of Medicon Valley as an important international biohub as well as indicating a more favorable international economic climate during the past few years. JANUARY/FEBRUARY 2006

New companies are started on both sides of Oresund, some based on university research and others as spinoffs from big pharma and mature biotech companies. In response to increasing global competition, both the Swedish and the Danish governments recently have launched aggressive plans in order to further stimulate and increase research, public and private innovation, and commercialization within information technology and biotechnology. The proclaimed goal of the Danish government is to position Denmark as one of the top innovative and competitive societies in the world by 2010. The initiatives to support this goal include a significant increase in public research funding as well as funding of public-private-interface research collaborations. For industry, a reduction of the corporate tax is on the political agenda, and a special tax relief for young innovative companies is being considered also. Last but not least, the political focus is on strengthening the early-stage funding of high-tech companies and introducing a stock exchange for unlisted companies similar to the United Kingdom’s Alternative Investment Market (AIM) model. A new Swedish strategy for biotechnology was published in May 2005, and the first action is a tax relief for research costs in small-and medium-sized companies, including nonprofitable spinouts, to be implemented from 2007. Since Medicon Valley comprises a significant part of the two countries’ high-tech industries, these political initiatives are considered to have substantial impact on the future of the region. Medicon Valley Goals Towards 2010

For Medicon Valley Academy, 2005 marks the entry point to the second five-year planning period, which aims to support the further development of the more mature region. Already, strategic evaluation and benchmarking

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

20 18 16 14 12 10 8 6 4 2 0 1997

1998 1999 2000

2001

2002

2003

2004

Fig. 4. Numbers of new biotech and medtech companies incorporated in Medicon Valley in the years 1997–2005.

to global best-in-class universities, companies, and regions has become an integral part of the new mission, showing that Medicon Valley seeks to be among the five most attractive bioregions worldwide, recognized for ➤ an excellent scientific environment and pool of talent ➤ outstanding access to knowledge sharing and technology transfer between universities, hospitals, and industry ➤ innovative and entrepreneurial environment and competitive enterprises ➤ international perspective and global networks. “From Bioscience to New Jobs in Medicon Valley,” a strategic report issued by Medicon Valley Academy in October 2004, analyzes the efficiency of technology transfer from the universities of the region; it is benchmarked against Karolinska Institutet (Stockholm, Sweden), Oxford University (United Kingdom), and Stanford University, California (United States). Although research investments and invention disclosures were found to be at the same level or higher in Medicon Valley, the impact on the commercialization rate had room for improvement. The report includes proposals to the many stakeholders

(the two political systems, regional authorities, and universities) to address both upstream and downstream weaknesses in the innovation chain. For Medicon Valley, the realization of these recommendations is an important step on the road to becoming one of the top globally attractive bioregions. Furthermore, the challenges from the ever-increasing speed of the globalization process have moved Medicon Valley Academy to reach out to other regions in an attempt to establish interregional collaborations. In October 2004, a networking project spanning several years was announced between the United Kingdom and Medicon Valley by Lord Sainsbury. This project, the UK-Medicon Valley Challenge Program, is another strategic component in Medicon Valley’s development of its position in Europe. [1] Ö. Sölvell, G. Lindqvist, and C. Ketels, The Cluster Initiative Greenbook. Sweden: Ivory Tower AB, 2003.

Address for Correspondence

Stina Gestrelius, Medicon Valley Academy, Arne Jacobsens Allé 15, 2. Orestad City, DK-2300 Copenhagen S, Denmark. Phone: +45 3532 4150. Email: [email protected].

JANUARY/FEBRUARY 2006 105

Cellular/Tissue Engineering

J. Farhadi, C. Jaquiery, M. Haug, G. Pierer, HF. Zeilhofer, and I. Martin

bone and cartilage tissue engineering for facial reconstructive surgery

n facial reconstructive surgery, new techniques based on the principles of tissue engineering have moved over the last decade from the bench closer to the bedside, where they are being combined with the principles of plastic surgery. In particular, mechanically competent cartilage grafts and osteoinductive constructs vascularized by flaps are envisioned to replace autologous or alloplastic materials, with the goal to reduce donor site morbidity and to increase graft durability. Here, we provide an overview of the state of the art in cartilage and bone tissue engineering and highlight how advances in this field might replace and improve current treatments in facial reconstructive surgery. Trauma, cancer, or congenital abnormalities often lead to cartilaginous and bony defects in the head and neck region. These tissue losses can be replaced either by nonbiological materials (alloplastic implants) or by tissue transfer from a healthy site (autografts). Alloplastic implants (stainless steel, Dacron, or polyacrylates) are readily available and do not lead to donor site morbidity, but they are not long lasting and are associated with a high rate of complications, such as infection, chronic irritation, and sometimes even carcinogenicity. Autologous tissue reconstruction is the prevalent and most versatile option in facial reconstructive surgery but is limited by the availability of donor tissue, morbidity at the donor site, and time-consuming surgery. The field of tissue engineering aims at generating fully biocompatible grafts in sufficient amounts, meeting the requirements of the repair site and avoiding the considerable donor site morbidity associated with traditional reconstructive techniques.

I

Tissue Engineering of Cartilage

The goal of cartilage tissue engineering in facial reconstructive surgery is to 106 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

generate a graft that can be implanted at different sites of the head and neck by applying the same surgical techniques as in reconstruction using autologous grafts. Engineering of a cartilage graft would start from obtaining a small biopsy from the nasal septum, ear, or rib cartilage. This procedure can be performed under local anaesthetic in a minimally invasive fashion and will not lead to donor site morbidity, as does the harvest of large grafts for reconstructive purposes. After enzymatic digestion of the specimen, the cells would be expanded in vitro and then induced to grow on bioactive degradable scaffolds that provide the structural and biochemical cues to guide their differentiation and generate a three-dimensional (3-D) tissue. Such a construct would then be transplanted into the defect, where further cell differentiation and tissue integration is expected to occur. Cell Sources

External ear [1] and nasal [2], [3] chondrocytes have been used with various degrees of success to engineer in vitro and/or in vivo 3-D cartilaginous tissues. Taking both cell yields and proliferation rates into account, we recently reported that a biopsy of human ear, nasal, or rib cartilage, weighing a few milligrams, would yield tens of millions of cells over a two- to three-week period [4]. This number of cells, based on reported seeding densities of nonarticular chondrocytes into various 3-D scaffolds, would be sufficient for the generation of autologous grafts of clinically relevant size (i.e., greater than 1 cm2 in size). But the key point is the chondrogenic capacity of these cells, because chondrocytes dedifferentiate to a fibroblastic stage during monolayer expansion. Although in principle, redifferentiation can be achieved upon transfer into a 3-D culture environment, the potential of human expanded chondrocytes to redifferentiate and generate a functional matrix is limited and decreases with 0739-5175/06/$20.00©2006IEEE

donor age. To overcome these limitations, specific regulatory molecules (e.g., growth factors, hormones, metabolites) have been employed as medium supplements during the different culture phases. Results indicate that expansion of chondrocytes in the presence of growth factors not only increases the cell proliferation rate, but also maintains the ability of the cells to redifferentiate upon transfer into a 3-D environment [5] and to subsequently respond to differentiating agents [6]. At present, however, we could not find in the literature a comparative animal or clinical study involving the use of chondrocytes expanded under conditions favoring cell proliferation and maintenance of chondrogenic ability. An alternative to the use of differentiated chondrocytes is the use of cells with chondrogenic differentiation capacity, like mesenchymal progenitor cells (MPCs). MPCs can be isolated, for instance, from bone marrow aspirates and have the potential to differentiate into various mesenchymal tissue lineages [7]. Despite the reports that MPCs can generate cartilaginous tissues [8], [9], the molecules expressed indicate possible instability of the cartilage phenotype, associated with remodeling of the engineered cartilage into a mineralized tissue. Moreover, no report has been published so far regarding the preclinical or clinical use of MPC in facial cartilage reconstruction. 3-D Scaffolds

Another critical element in engineering cartilage is a suitable scaffold that displays biological and physical properties matching both the needs of differentiating chondrocytes in vitro and of regenerating cartilage in vivo. The scaffold must provide sufficient mechanical strength and stiffness to substitute initially for wound contraction forces and, later, for the remodeling of the tissue. Furthermore, it should enhance cell attachment and provide enough space to JANUARY/FEBRUARY 2006

allow the exchange of nutrients and waste products and the deposition of extracellular matrix. In addition, the mechanical characteristics of the scaffold should be such that at the time of implantation the cell-scaffold construct can sustain the surgical manipulation and the insertion of sutures. Different research groups have used a wide variety of scaffolds in an attempt to generate cartilaginous tissues in vitro [10]. The form and composition of these scaffolds range from nonwoven meshes and foams of alpha-hydroxypolyesters, polyglactin, or hyaluronan alkyl esters to photo-crosslinked hydrogels and sponges based on different types of collagen and glycosaminoglycans. Composites consisting of a 3-D porous scaffold filled with cells embedded in a fibrin or alginate gel have also been explored. But many of these scaffolds are still in the experimental evaluation, and several issues still have to be addressed, related to the interactions between cells and specific substrates, the influence of the pore size distribution on cell behavior, and the effect of scaffold geometry (i.e., in the form of a foam, mesh, or gel) on the induction/ maintenance of the chondrocytic phenotype [11]. Upscaling of the Constructs

One of the major challenges in cartilage tissue engineering is the generation of uniform tissues of clinically relevant size (i.e., a few square centimeters in area and 3–4 mm in thickness). An upscaling of the constructs could be reached by the use of bioreactors, where cell seeding and culture may be facilitated by the application of mechanical and/or hydrodynamic forces [12]. Bioreactors would also provide a controlled in vitro environment over specific biochemical and physical signals, which have the potential to regulate chondrogenesis and improve the structure and function of the resulting cartilage tissues [13]–[15]. Despite the great efforts currently dedicated to the development and use of bioreactors for the engineering of functional cartilage tissue, it is still rather unclear which specific physical stimulation regime is required to induce a specific effect on cultured chondrocytes. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Tissue Engineering of Bone

The goal of bone tissue engineering in facial reconstructive surgery is to generate an osteoinductive graft, namely, a construct that upon implantation in the area to be reconstructed is capable of initiating the formation of bone tissue. The engineering of an osteoinductive graft of predefined size and shape can be achieved by loading a 3-D osteoconductive scaffold with either osteogenic cells or bone morphogenetic proteins. According to the former approach, osteogenic cells are obtained from biopsies of diverse possible tissues (e.g., bone marrow, periosteum) and are typically expanded in culture. The latter approach appears more simple, since it does not require ex vivo cell processing but opens the biological question of how the overdose of one single molecule could recapitulate the complex set of molecular events physiologically involved in the safe and stable formation of bone tissue. Cell Sources

It has been demonstrated that the regeneration of critically sized bone defects in a sheep model can be achieved by combining osteogenic cells and a ceramic scaffold, whereas the ceramic scaffold alone leads to less uniform and less efficient ossification [16]. This and other recent studies support the necessity of delivering viable osteogenic cells within a ceramic scaffold in order to achieve a stable and load-bearing osseous formation and integration. MPCs isolated from the bone marrow are currently the most widely used cell source for the experimental induction of bone tissue formation, which has also been tested in few clinical cases [17]. However, considering the conditions required for bone marrow harvest and that the amount of MPCs that can be isolated is limited and highly variable [18], attempts have been made to isolate MPCs from alternative tissues. Dragoo et al. isolated human MPCs from fat tissue and from bone marrow aspirates and compared the osteogenic potential of both cell sources when transfected with adenovirus containing BMP-2 [19]. Fat-tissue-derived transfected

MPCs showed faster osteogenic differentiation as compared with MPCs extracted from bone marrow, although more recent communications indicate that MPCs from fat tissue are a reliable osteogenic cell source only if associated with the delivery of osteoinductive genes/proteins. The periosteum from the jaws can easily be harvested under local anesthesia and in an outpatient environment. Schantz and associates demonstrated in vitro osteogenic differentiation of periosteum-derived osteoprogenitor cells and ectopic in vivo bone formation using a nude mouse model [20]. Recently, Schimming and Schmelzeisen reported the clinical use of periosteal cells in combination with a polymer fleece in the context of the maxillary sinus elevation procedure [21]. In a series of 27 patients, 18 showed bone formation six months after operation. However, it remains unclear whether the detected bone was formed by periosteal cells or by the cells surrounding the defect. 3-D Osteoconductive Scaffolds

Support of bone regeneration by osteoconductive materials is a procedure that has been used in surgery for decades to restore parts of the facial skeleton. Due to excellent vascularization of the head and neck, incorporation of these materials in general is efficient, and the potential risk of infection is low, as compared to other sites of the body. Osteoconductive materials are biomaterials that support adhesion, proliferation, and differentiation of osteogenic cells from surrounding tissues, ultimately leading to bone tissue formation. After an ideal time frame of a few months, the scaffold should be replaced by newly formed bone, undergoing subsequent integration and remodeling. In addition to animal or human bone-derived scaffolds, two main groups of synthetically manufactured osteoconductive materials can be identified: the ceramics and the synthetic polymers. The criteria for the selection of osteoconductive materials should be based on the ability to support differentiation of osteogenic cells, on the rate of resorption, and on JANUARY/FEBRUARY 2006 107

Cellular/Tissue Engineering (continued)

the possibility of applying rapid prototyping techniques in order to fully control the architecture and the outer design of the scaffold. Ceramics are well known to support the osteogenic phenotype of osteoblasts [22] and can prime the differentiation of MPCs toward the formation of bone tissue [23]. Even if it seems possible to design a standardized hydroxyapatite ceramic scaffold with the help of rapid prototyping techniques [24], the architecture of a given scaffold (i.e., the size and the interconnectivity of the pores), as well as the mechanical properties, is generally better controlled using synthetic polymers [25]. The ability of synthetic polymers to induce osteogenic cell differentiation is, on the other hand, generally lower than that of ceramics, unless growth factors are incorporated and released in a controlled fashion. Growth Factors

Urist first popularized the concept of a bone-generating protein in 1965 when he made the discovery of bone morphogenetic proteins (BMP) [26]. The BMP family includes the most commonly used molecules for musculoskeletal tissue regeneration. In principle, the following three different concepts for the use of growth factors are envisioned in bone tissue engineering. First, a specific growth factor can be applied during culture of osteogenic cells to enhance proliferation and/or differentiation; these cells, combined with an osteoconductive scaffold, can then be implanted as an osteoinductive graft. Second, the desired growth factor may be injected directly at the site together with an osteoconductive material, aiming at recruitment and differentiation of MPCs localized in the neighboring original bone or muscle tissue. Third, a specific growth factor can be incorporated within a polymer scaffold, which, by degradation, will release the factor with defined kinetics.

vascularized bone grafts. This favorable situation would also allow the use of large engineered grafts for the reconstruction of jaws, minimizing the potential risk of failing integration. In case of insufficient vascularization of the recipient bed, the formation of new blood vessels bringing nutrients to the engineered graft could be promoted by 1) the delivery of angiogenic factors, 2) the generation of artificial microvascular networks, or 3) the prefabrication of flaps. The use of angiogenic factors is gaining increasing attention by the scientific community but requires definition and control of the appropriate timing and dose of the specific factors. Indeed, it has been recently demonstrated that a longterm continuous delivery of vascular endothelial growth factor (VEGF) by transfected myoblasts can lead to normal vascularization, but only if levels are maintained below a microenvironmental threshold [27]. The generation of microvascular networks by the insertion of a vascular pedicel into the engineered tissue, buried subcutaneously, is an interesting, innovative strategy that is currently being explored in a variety of models [28]. The prefabrication of a flap in combination with engineered osteoinductive grafts has been recently described for the reconstruction of almost an entire lower jaw using bovine bone-derived ceramic, BMP-2, and MPCs harvested from the bone marrow [29]. After designing the jaw with the help of 3-D imaging, these components were implanted subcutaneously in the back of the patient, and six weeks later, the graft was transferred microsurgically along with the latissimus dorsi muscle. At first glance, it seems appealing to transfer the engineered graft together with an excellent vascularized muscle. However, taking into account that the harvesting procedure of the latissimus dorsi muscle may cause considerable donor site morbidity, the advantages of the previously described procedure are questionable.

Vascularization and Integration

Due to excellent vascularization of the head and neck area, even large segmental defects of the jaws can be reconstructed by the use of free non108 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Advances for Clinical Application and Future Horizons

In this article, we have reviewed some of the techniques that are being developed

to manipulate human chondrocytes and MPCs to generate cartilaginous and osteoinductive tissues. Considering that engineered cartilage and bone tissues in reconstructive surgery of the head and neck would have to restore form and function, the main challenges in the future will be related to improving methods to define the shape and stage of development of the engineered tissues. Moreover, since the clinical use of engineered tissues in facial reconstructive surgery is, so far, anecdotal, it will also be critical to identify which surgical procedures will first benefit from the advances in tissue engineering. Cartilage grafts for nasal reconstructive surgery probably will be the first application in the clinics, as these grafts have to be fairly small and, after implantation, would be embedded in a well vascularized bed. Furthermore, the nose is not subjected to high mechanical stresses directly after implantation of a graft; therefore, the graft does not need to be fully structurally stable. Similar considerations are valid for reconstruction of the eyelids. Instead, the clinical use of engineered ear cartilage grafts is expected to be more complex, as in most clinical situations there is the need to reconstruct a soft-tissue defect next to the cartilage defect. Furthermore, ear cartilage has a more complex anatomical shape, and the engineered ear cartilage may need to be created by computer-aided design (CAD) templates. An important issue for the clinical use of engineered cartilage in facial and reconstructive surgery will be to identify the structural and functional properties of the tissue engineered grafts, which need to be matched for the efficacy and safety of the implantation. The reconstruction of bone defects in the head and neck region by engineered grafts is already close to clinical applications. One of the main problems in bone tissue engineering is to induce rapid vascularization when a certain size of the constructs is reached. As the engineering of a vascular tissue is not yet achievable, the combination of tissue engineering techniques with flap JANUARY/FEBRUARY 2006

surgery could bridge this gap and lead to the clinical application of engineered bone in facial reconstruction. Furthermore, imaging techniques combined with computational modeling and fabrication of scaffolds through rapid prototyping techniques are likely to play an important role, as the facial bones have complex 3-D structures [25]. One major challenge for the routine clinical use of engineered tissues is related to the manufacturing process, which at present is costly, impractical, and not sufficiently standardized. In this context, we envision that 3-D tissues could be engineered within closed bioreactor units with advanced control systems that would facilitate streamlining and automation of the numerous labor-intensive steps. Starting from a patient’s tissue biopsy, a bioreactor system could isolate, expand, seed on a scaffold, and differentiate specific cell types, thereby performing the different processing phases within a single closed and automated system [12]. Such a bioreactor would enable competent hospitals and clinics to carry out autologous tissue engineering for their own patients, eliminating logistical issues of transferring specimens between locations. This would also eliminate the need for large and expensive GMP (good manufacturing practice) tissue engineering facilities and minimize operator handling, with the final result of reducing the cost of tissue engineered products for the health system and for the community. Altogether, when efficiently designed for low-cost operation, novel bioreactor systems could facilitate spreading novel and powerful cell-based tissue engineering approaches, which would otherwise remain confined within the context of academic studies or restricted to elite social classes or systems. Address for Correspondence: Ivan Martin, Institute for Surgical Research and Hospital Management, University Hospital Basel, Hebelstrasse 20, ZLF, Room 405, 4031 Basel, Switzerland. Phone: +41 61 265 2384. Fax: +41 61 265 3990. E-mail: [email protected].

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

References [1] G.J. van Osch, S.W. van der Veen, and H.L. Verwoerd-Verhoef, “In vitro redifferentiation of culture-expanded rabbit and human auricular chondrocytes for cartilage reconstruction,” Plast. Reconstr. Surg., vol. 107, no. 2, pp. 433–440, Feb. 2001. [2] N. Rotter, L.J. Bonassar, G. Tobias, M. Lebl, A.K. Roy, and C.A. Vacanti, “Age dependence of cellular properties of human septal cartilage: Implications for tissue engineering,” Arch. Otolaryngol. Head Neck Surg., vol. 127, no. 10, pp. 1248–1252, Oct. 2001. [3] W. Kafienah, M. Jakob, O. Demarteau, A. Frazer, M.D. Barker, I. Martin, and A.P. Hollander, “Threedimensional tissue engineering of hyaline cartilage: Comparison of adult nasal and articular chondrocytes,” Tissue Eng., vol. 8, no. 5, pp. 817–826, Oct. 2002. [4] A.G. Tay, J. Farhadi, R. Suetterlin, G. Pierer, M. Heberer, and I. Martin, “Cell yield, proliferation, and postexpansion differentiation capacity of human ear, nasal, and rib chondrocytes,” Tissue Eng., vol. 10, no. 5–6, pp. 762–770, May 2004. [5] M. Jakob, O. Demarteau, D. Schafer, B. Hintermann, W. Dick, M. Heberer, and I. Martin, “Specific growth factors during the expansion and redifferentiation of adult human articular chondrocytes enhance chondrogenesis and cartilaginous tissue formation in vitro,” J. Cell Biochem., vol. 81, no. 2, pp. 368–377, Mar. 2001. [6] I. Martin, R. Suetterlin, W. Baschong, M. Heberer, G. Vunjak-Novakovic, and L.E. Freed, “Enhanced cartilage tissue engineering by sequential exposure of chondrocytes to FGF-2 during 2-D expansion and BMP-2 during 3-D cultivation,” J. Cell Biochem., vol. 83, no. 1, pp. 121–128, June 2001. [7] D.J. Prockop, “Marrow stromal cells as stem cells for nonhematopoietic tissues,” Science, vol. 276, no. 5309, pp. 71–74, Apr. 1997. [8] B. Johnstone, T.M. Hering, A.I. Caplan, V.M. Goldberg, and J.U. Yoo, “In vitro chondrogenesis of bone marrow-derived mesenchymal progenitor cells,” Exp. Cell. Res., vol. 238, no. 1, pp. 265–272, Jan. 1998. [9] I. Martin, V.P. Shastri, R.F. Padera, J. Yang, A.J. Mackay, R. Langer, G. Vunjak-Novakovic, and L.E. Freed, “Selective differentiation of mammalian bone marrow stromal cells cultured on three-dimensional polymer foams,” J. Biomed. Mater. Res., vol. 55, no. 2, pp. 229–235, May 2001. [10] T.B. Woodfield, J.M. Bezemer, J.S. Pieper, C.A. van Blitterswijk, and J. Riesle, “Scaffolds for tissue engineering of cartilage,” Crit. Rev. Eukaryot. Gene Expr., vol. 12, no. 3, pp. 209–236, 2002. [11] S. Miot, T. Woodfield, A.U. Daniels, R. Suetterlin, I. Peterschmitt, M. Heberer, C.A. van Blitterswijk, J. Riesle, and I. Martin, “Effects of scaffold composition and architecture on human nasal chondrocyte redifferentiation and cartilaginous matrix deposition,” Biomaterials, vol. 26, no. 15, pp. 2479–2489, May 2005. [12] I. Martin, D. Wendt, and M. Heberer, “The role of bioreactors in tissue engineering,” Trends Biotechnol., vol. 22, no. 2, pp. 80–86, Feb. 2004. [13] I. Martin, B. Obradovic, S. Treppo, A.J. Grodzinsky, R. Langer, L.E. Freed, and G. VunjakNovakovic, “Modulation of the mechanical properties of tissue engineered cartilage,” Biorheology, vol. 37, no. 1–2, pp. 141–147, 2000. [14] D. Wendt, A. Marsano, M. Jakob, M. Heberer and I. Martin, “Oscillating perfusion of cell suspensions through three-dimensional scaffolds enhances cell seeding efficiency and uniformity,” Biotechnol. Bioeng., vol. 84, no. 2, pp. 205–214, Oct. 2003. [15] O. Demarteau, D. Wendt, A. Braccini, M. Jakob, D. Schafer, M. Heberer, and I. Martin, “Dynamic compression of cartilage constructs engineered from expanded human articular chondro-

cytes,” Biochem. Biophys. Res. Commun., vol. 310, no. 2, pp. 580–588, Oct. 2003. [16] E. Kon, A. Muraglia, A. Corsi, P. Bianco, M. Marcacci, I. Martin, A. Boyde, I. Ruspantini, P. Chistolini, M. Rocca, R. Giardino, R. Cancedda, and R. Quarto, “Autologous bone marrow stromal cells loaded onto porous hydroxyapatite ceramic accelerate bone repair in critical-size defects of sheep long bones,” J. Biomed. Mater. Res., vol. 49, no. 3, pp. 328–337, Mar. 2000. [17] R. Quarto, M. Mastrogiacomo, R. Cancedda, S.M. Kutepov, V. Mukhachev, A. Lavroukov, E. Kon, and M. Marcacci, “Repair of large bone defects with the use of autologous bone marrow stromal cells,” N. Eng. J. Med., vol. 344, no. 5, pp. 385–386, Feb. 2001. [18] D.G. Phinney, G. Kopen, W. Righter, S. Webster, N. Tremain, and D.J. Prockop, “Donor variation in the growth properties and osteogenic potential of human marrow stromal cells,” J. Cell Biochem., vol. 75, no. 3, pp. 424–436, Dec. 1999. [19] J.L. Dragoo, B. Samimi, M. Zhu, S.L. Hame, B.J. Thomas, J.R. Lieberman, M.H. Hedrick, and P. Benhaim, “Tissue-engineered cartilage and bone using stem cells from human infrapatellar fat pads,” J. Bone Joint Surg. Br., vol. 85, no. 5, pp. 740–747, July 2003. [20] J.T. Schantz, D.W. Hutmacher, H. Chim, K.W. Ng, T.C. Lim, and S.H. Teoh, “Induction of ectopic bone formation by using human periosteal cells in combination with a novel scaffold technology,” Cell. Transplant., vol. 11, no. 2, pp. 125–138, 2002. [21] R. Schimming and R. Schmelzeisen, “Tissue-engineered bone for maxillary sinus augmentation,” J. Oral Maxillofac. Surg., vol. 62, no. 6, pp. 724–729, June 2004. [22] C. Wang, Y. Duan, B. Markovic, J. Barbara, C.R. Howlett, X. Zhang, and H. Zreiqat, “Phenotypic expression of bone-related genes in osteoblasts grown on calcium phosphate ceramics with different phase compositions,” Biomaterials, vol. 25, no. 13, pp. 2507–2514, June 2004. [23] I. Martin, A. Muraglia, G. Campanile, R. Cancedda, and R. Quarto, “Fibroblast growth factor-2 supports ex vivo expansion and maintenance of osteogenic precursors from human bone marrow,” Endocrinology, vol. 138, no. 10, pp. 4456–4462, Oct. 1997. [24] C.E. Wilson, J.D. de Bruijn, C.A. van Blitterswijk, A.J. Verbout, and W.J. Dhert, “Design and fabrication of standardized hydroxyapatite scaffolds with a defined macro-architecture by rapid prototyping for bone-tissue-engineering research,” J. Biomed. Mater. Res. A, vol. 68, no. 1, pp. 123–132, Jan. 2004. [25] W.Y. Yeong, C.K. Chua, K.F. Leong, and M. Chandrasekaran, “Rapid prototyping in tissue engineering: challenges and potential,” Trends Biotechnol., vol. 22, no. 12, pp. 643–652, Dec. 2004. [26] M.R. Urist, “Bone: Formation by autoinduction,” Science, vol. 150, no. 698, pp. 893–899, Nov. 1965. [27] C.R. Ozawa, A. Banfi, N.L. Glazer, G. Thurston, M.L. Springer, P.E. Kraft, D.M. McDonald, and H.M. Blau, “Microenvironmental VEGF concentration, not total dose, determines a threshold between normal and aberrant angiogenesis,” J. Clin. Invest., vol. 113, no. 4, pp. 516–527, Feb. 2004. [28] K.J. Cronin, A. Messina, K.R. Knight, J.J. Cooper-White, G.W. Stevens, A.J. Penington, and W.A. Morrison, “New murine model of spontaneous autologous tissue engineering, combining an arteriovenous pedicel with matrix materials,” Plast. Reconstr. Surg., vol. 113, no. 1, pp. 260–269, Jan. 2004. [29] P.H. Warnke, I.N. Springer, J. Wiltfang, Y. Acil, H. Eufinger, M. Wehmoller, P.A. Russo, H. Bolte, E. Sherry, E. Behrens, and H. Terheyden, “Growth and transplantation of a custom vascularised bone graft in a man,” Lancet, vol. 364, no. 9436, pp. 766–770, Aug. 2004.

JANUARY/FEBRUARY 2006 109

Conference Calendar

10–13 JANUARY 2006

15–17 MAY 2006

NanoSingapore 2006: IEEE Conference on Emerging Technology-Nanoelectronics Singapore Contact: Miss Janice Tan, Conference Secretariat Phone: +65 6466 5775 ext. 222 Fax: +65 6467 7667 Web: http://www.ieeecet.org E-mail: [email protected]

European Study Group of Cardiovascular Oscillations Jena, Germany Contact: Andreas Voss Phone: +49 3641 938958 Fax: +49 3641 938952 Web: http://www.esgco2006.de E-mail: [email protected]

20–22 FEBRUARY 2006

The 1st IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics Pisa, Italy Contact: Paolo Dario Web: http://www-arts.sssup.it/biorob E-mail: [email protected] 6–9 APRIL 2006

IEEE International Symposium on Biomedical Imaging Crystal Gateway Marriott, Arlington, Virginia Contact: Jelena Kovacevic Web: http://www.biomedicalimaging.org/ E-mail: [email protected] 27 APRIL 2006

6th Asian Pacific Conference on Medical and Biological Engineering Tsukuba, Ibaraki, Japan Contact: Prof. Katsuhiko Tsujioka Phone: +81 (0)86 462 1111 Fax: +81 (0)86 464 1020 Web: http://shuttle.kawasakim.ac.jp/apcmbe2005/ E-mail: [email protected]

110 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

21–25 JUNE 2006

2006 Summer ASME Bioengineering Conference Amelia Island, Florida Contact: B. Barry Lieber Phone: +1 305 284 2330 Fax: +1 305 284 6494 Web: http://divisions.asme. org-/bed/events/summer06.html E-mail: [email protected] 27 AUGUST – 1 SEPTEMBER 2006

World Congress on Medical Physics and Biomedical Engineering 2006 Seoul, Korea Web: http://www.wc2006-seoul.org/ 30 AUGUST – 2 SEPTEMBER 2006

28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society New York, New York Contact: EMB Executive Office Phone: +1 732 981 3451 Fax: +1 732 465 6435 E-mail: [email protected] 20–22 SEPTEMBER 2006

6th IFAC Symposium on Modelling and Control in Biomedical Systems Reims, France Contact: Prof. Janan Zaytoon Phone: +33 3 26 91 32 26

Fax: +33 3 26 91 31 06 Web: http://www.univ-reims.fr/mcbms06 E-mail: [email protected] 28 SEPTEMBER – 2 OCTOBER 2006

3rd Aegean “Biologie Perspective” Santorini Conference Santorini Island, Greece Contact: Brigitte Hiegel, Conference Secretariat Phone: +33 3 83 68 21 71 Fax: +33 3 83 32 13 22 Web: http://biol.prospectiveconf.u-nancy.fr E-mail: [email protected] 2–5 MAY 2007

3rd IEEE EMBS Special Topic Conference on Neural Engineering Kohala Coast, Hawaii Contact: Prof. Metin Akay Phone: +1 603 646 2230 Fax: +1 603 646 3856 E-mail: [email protected] 22–27 AUGUST 2007

29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Lyon, France Contact: EMBS Executive Office Phone: +1 732 981 3451 Fax: +1 732 465 6435 E-mail: [email protected] 13–18 SEPTEMBER 2009

World Congress on Medical Physics and Biomedical Engineering 2009 Munich, Germany Contact: Prof. Dr. Olaf Dössel Phone: +49 (0)721 608-2650 Fax: +49 (0)721 608-2789 Web: http://www.wc2009.de E-mail: [email protected]

JANUARY/FEBRUARY 2006

CLASSIFIED EMPLOYMENT To conform to the Age Discrimination in Employment Act, and to discourage age discrimination, IEEE may reject any advertisement containing any of these phrases, or similar ones: “recent college grad,” “1-4 years maximum experience,” “up to 5 years experience,” or “10 years maximum experience.” IEEE reserves the right to append to any advertisement, without specific notices to the advertiser, “Experience ranges are suggested minimum requirements, not maximums.” IEEE assumes that, since advertisers have been notified of this policy in advance, they agree that any experience requirements, whether stated as ranges or otherwise, will be construed by the reader as minimum requirements only. While IEEE does not ban the use of the term “entry level” its use is discouraged since, to some, it connotes an age rather than experience designation.

To place an ad in the IEEE Engineering in Medicine and Biology Magazine Employment Opportunities section, call:

Susan E. Schneiderman Business Development Manager +1 732 562 3946

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

VISIT US AT

www.medtronic.com

CHALLENGES CHALLENGES THAT THAT GET GET YOUR YOUR PROBLEM-SOLVING PROBLEM-SOLVING MUSCLES MUSCLES FLEXING. FLEXING. TECHNOLOGY TECHNOLOGY THAT THAT MAKES MAKES YOUR YOUR SPINE SPINE TINGLE. TINGLE. PR ROJECTS THAT PROJECTS THAT MAKE MAKE YOUR YOUR PULSE PULSE RACE. RACE.

Medtronic. Careers with a passion for life. At Medtronic, we push the boundaries of technology everyday. Every 6 seconds, somewhere in the world someone’s life is improved by a Medtronic product or therapy. Our success has created exceptional opportunities for TOP TALENT at all levels in: • Engineering (six sigma certification a plus) Quality, Mechanical, Manufacturing, R&D, SW, Electrical, IC Design, Biomedical • Finance and Audit • Information Technology • Regulatory Affairs, Quality Assurance, Clinical Studies, & Biostatistics • Sales & Marketing Learn more about Medtronic and our exceptional Career Opportunities at www.medtronic.com. TO APPLY: Review openings and select "Add this Job to Cart" to route your resume directly to the hiring department. Medtronic is an equal opportunity employer committed to cultural diversity in the workforce.

Join Medtronic today…and experience your career with a Passion for Life.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006 111

University of Connecticut School of Engineering, Biomedical Engineering Program Assistant Professor in Residence Position University

of

Connecticut

is

seeking

an

exceptional candidate for an Assistant Professor in Residence in Biomedical Engineering in the School of Engineering. This is a Teaching focused faculty position with an emphasis in teaching undergraduate biomechanics and senior design, and

opportunities

in

teaching

graduate

biomechanics. The candidate must have an outstanding record of accomplishment in teaching. Preference will be given to candidates with a Ph.D. and B.S. degrees in biomedical engineering with a biomechanics emphasis. Consideration will be given to candidates with Ph.D. and B.S. degrees in mechanical engineering, provided a strong and broad background in biomedical engineering is demonstrated. Nomination letters and applications that include a letter of interest describing teaching experience, research activities and career goals, a current curriculum vita, and the names of at least three references, should be sent to: Dr. Ian Greenshields Chair, Biomedical Engineering Search Committee, Office of the Dean, 261 Glenbrook Road Unit 2237

HARVARD MEDICAL SCHOOL Department of Continuing Education and

BETH ISRAEL DEACONESS MEDICAL CENTER Department of Medicine present

University of Connecticut Storrs CT 06269-2237 Phone: (860) 486 5091 Email: [email protected] Salary will be commensurate with the record of the applicant. This is a full time, non-tenure track position for an initial term of three-years. Applicant screening will begin immediately and will continue until the position is filled. The University of Connecticut is an Equal Opportunity and Affirmative Action Employer, and encourages applications from minorities, women and people with disabilities. The Biomedical Engineering program offers B.S., M.S. and Ph.D. degrees and has over 50 faculty members. Detailed information about the Biomedical Engineering program is available at http://www.engr.uconn.edu/bme.

HEART RATE VARIABILITY - 2006 TECHNIQUES, APPLICATIONS AND FUTURE DIRECTIONS April 20 - 22, 2006 The Fairmont Copley Plaza • Boston, MA Under the Direction of: Ary L. Goldberger, MD, George B. Moody and Chung-Kang Peng, PhD This 2.5-day intensive course will provide a state-of-the-art overview of the technical considerations, pertinent computational tools, and clinical and research applications of heart rate variability (HRV) analysis. Particular emphasis will be placed on the utility and pitfalls of these techniques. The course will provide a rigorous review of time and frequency domain, and complexity based (nonlinear) methodologies for analyzing heart rate, arterial blood pressure, and respiratory variability. The course faculty includes international leaders in the field. Physicians/Other Professionals: $550 (USD) Residents and Fellows in Training: $300 (USD) For more information, call Harvard Medical School - Department of Continuing Education at 617-384-8600 (M-F; 10-4 EST)

www.cme.hms.harvard.edu/courses/heartrate

112 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

JANUARY/FEBRUARY 2006

The IEEE Engineering in Medicine and Biology Society (IEEE EMBS) members come from all disciplines of biomedical engineering, from all over the world, and from all types of environments. Whether you’re a biomedical engineer, physician or surgeon, clinical engineer, radiologist, inventor, researcher, educator or student, IEEE EMBS brings you the tools you need to shape your future in an ever-evolving field.

Analytical Diagnostic Techniques Bioelectric Signals Bioinformatics Biological Sciences Biomagnetic Signals Bio-models, Controls and Simulation Biomedical Communication Biomedical Measurement Cells to Organs Engineering Computer-assisted Surgery Devices and Instrumentation Infrared Imaging Genetics Hospital Engineering Medical Imaging Microwaves NanoBioscience Neuroengineering Optical and Laser Radiation Radiation and Radioactivity Sensory Processing Sensory Motor Systems and Rehabilitation Signal and Image Processing Therapeutic Surgical Techniques Thermology

Here’s what we can do for you… The unique resources of IEEE EMBS membership help you develop your skills and build your professional future. Connect with a community of innovators and access career tools and the latest technical information and research... resources designed to help you succeed! IEEE EMBS delivers access to quality information on technical and professional issues in Biomedical Engineering, including the most highly cited journals in the field. IEEE Engineering in Medicine and Biology Magazine IEEE Transactions on Medical Imaging The #1 journal in biomedical engineering!* IEEE Transactions on Neural Systems and Rehabilitation Engineering IEEE Transactions on Biomedical Engineering IEEE Transactions on Information Technology in Biomedicine IEEE Transactions on NanoBioscience ALSO AVAILABLE TO IEEE EMBS MEMBERS: conference and workshop member discounts, and international, regional and local meetings of colleagues.

Join today – find out what we can do for you!

www.embs.org *Source: ISI Journal Citation Report 2001

Annual Index 2005 Annual Index IEEE Engineering in Medicine and Biology Vol. 24 This index covers all technical items — papers, correspondence, reviews, etc. — that appeared in this periodical during 2005, and items from previous years that were commented upon or corrected in 2005. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author’s name. The primary entry includes the coauthors’ names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author’s name, the publication abbreviation, month, and year, and inclusive pages. Subject cross-references are included to assist in finding items of interest. Note that the item title is found only under the primary entry in the Author Index. AUTHOR INDEX A Ahuja, A., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Akazawa, K., see Okuno, R., EMB-M Jul-Aug 05 48-56 Allen, R.H., see Kim, E.J., EMB-M Nov-Dec 05 34-39 Asakura, R., see Uebaba, K., EMB-M Mar-Apr 05 69-78 Atsumi, K., and S. Kamohara. Bridging conventional medicine and complementary and alternative medicine; EMB-M Mar-Apr 05 30-34 Ayres, F.J., and R.M. Rangayvan. Characterization of architectural distortion in mammograms; EMB-M Jan-Feb 05 59-67 Azakawa, K., see Garcia, G.A., EMB-M Jul-Aug 05 63-72 Azevedo, L., see Ceres, R., EMB-M Nov-Dec 05 55-63 B Barnea, O., see Shusterman, V., EMB-M Mar-Apr 05 52-57 Bartoo, G. FDA’s annual report on medical devices [Regulatory Issues]; EMB-M Jan-Feb 05 99-100 Baura, G. When money wasn’t king [Point of View]; EMB-M Mar-Apr 05 15-16 Baura, G. Lights out? [Point of View]; EMB-M Nov-Dec 05 12, 14 Baura, G.D. What about grad school? [Point of View]; EMB-M Jul-Aug 05 16-17 Berger, T.W., A. Ahuja, S.H. Courellis, S.A. Deadwyler, G. Erinjippurath, G.A. Gerhardt, G. Gholmieh, J.J. Granacki, R. Hampson, Min Chi Hsaio, J. Lacoss, V.Z. Marmarelis, P. Nasiatka, V. Srinivasan, Dong Song, A.R. Tanguay, and J. Wills. Restoring lost cognitive function; EMB-M Sep-Oct 05 30-44 Bernas, M., M. White, B. Kriederman, P. Summers, and C. Witte. Massage therapy in the treatment of lymphedema; EMB-M Mar-Apr 05 58-68 Bianchi, A.M., see Casarotto, S., EMB-M Jan-Feb 05 68-77 Binseng Wang Clinical Engineering (David, Y. et al., eds.; 2003) [book review]; EMB-M May-Jun 05 18-19 Blazewicz, J., P.L. Hammer, and P. Lukasiak. Predicting secondary structures of proteins; EMB-M May-Jun 05 88-94 Blom, E.M., see Breedveld, P., EMB-M Nov-Dec 05 40-47 Bonato, P., see Hortobagyi, T., EMB-M Jan-Feb 05 20-21 Breedveld, P., J.S. Scheltes, E.M. Blom, and J.E.I. Verheij. A new, easily miniaturized steerable endoscope; EMB-M Nov-Dec 05 40-47

114 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Brinton, R.D., see Cocozza, J.D., EMB-M Sep-Oct 05 82-91 Bursac, N., see Pedrotty, D.M., EMB-M May-Jun 05 125-127 C Calderon, L., see Ceres, R., EMB-M Nov-Dec 05 55-63 Casarotto, S., A.M. Bianchi, S. Cerutti, and G.A. Chiarenza. Dynamic time warping in the analysis of event-related potentials; EMB-M Jan-Feb 05 68-77 Catanese, M., see Parrish, T.B., EMB-M Mar-Apr 05 35-40 Cauwenberghs, G., see Murari, K., EMB-M Nov-Dec 05 23-29 Ceres, R., J.L. Pons, L. Calderon, A.R. Jimenez, and L. Azevedo. A robotic vehicle for disabled children; EMB-M Nov-Dec 05 55-63 Cerutti, S., see Casarotto, S., EMB-M Jan-Feb 05 68-77 Chen, J.Y., and A.Y. Sivachenko. Data mining in protein interactomics; EMB-M May-Jun 05 95-102 Cheung, T.H.T., see Hamady, M., EMB-M May-Jun 05 34-40 Cheung, T.H.T., see Hamady, M., EMB-M May-Jun 05 58-66 Chiarenza, G.A., see Casarotto, S., EMB-M Jan-Feb 05 68-77 Chia-Yu Yen, S.M. Helmike, K.J. Cios, M.B. Perryman, and M.W. Duncan. Quantitative analysis of proteomics using data mining; EMB-M May-Jun 05 67-72 Chihara, K., see Masuda, Y., EMB-M Jul-Aug 05 43-47 Chi Hsaio Min, see Berger, T.W., EMB-M Sep-Oct 05 30-44 Cios, K.J., see Hamady, M., EMB-M May-Jun 05 34-40 Cios, K.J., see Chia-Yu Yen, EMB-M May-Jun 05 67-72 Coatrieux, J.L. Computer vision and graphics: frontiers, interfaces, crossovers, and overlaps in science; EMB-M Jan-Feb 05 16-19 Coatrieux, J.L. A look at.. shape and function from motion in medical imaging: Part I; EMB-M May-Jun 05 29-31 Cocozza, J.D., and R.D. Brinton. Turning students into science stars (science and engineering education); EMB-M Sep-Oct 05 82-91 Courellis, S.H., see Berger, T.W., EMB-M Sep-Oct 05 30-44 D Darling, A.L., and Wei Sun. Free-form fabrication and micro-CT characterization of poly-ε -caprolactone tissue scaffolds; EMB-M JanFeb 05 78-83 Davoodi, R., see Loeb, G.E., EMB-M Sep-Oct 05 45-51 Deadwyler, S.A., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Decesaris, I., see Muller, H.-P., EMB-M May-Jun 05 109-116 Demelis, M., see Muller, H.-P., EMB-M May-Jun 05 109-116 Demir, S.S. Talking to the autism community [BME Company Profiles]; EMB-M Jan-Feb 05 14-15, 19 Demir, S.S. Entelos: Predictive model systems for disease [BME Company Profiles]; EMB-M May-Jun 05 20-23 Demiris, G. PACS and Imaging Informatics; Basic Principles and Applications (Huang, H.K., ed.; 2004) [book review]; EMB-M JulAug 05 92 Dohi, T., see Kawai, T., EMB-M Jul-Aug 05 57-62 Dong Song, see Berger, T.W., EMB-M Sep-Oct 05 30-44 Down, J., see Gayler, K., EMB-M Mar-Apr 05 79-84 Drozd, M., P. Husar, A. Nowakowski, and G. Henning. Detecting evoked potentials with SVD- and ICA-based statistical models; EMB-M Jan-Feb 05 51-58 Duncan, M.W., see Chia-Yu Yen, EMB-M May-Jun 05 67-72

JANUARY/FEBRUARY 2006

E

Feng-Hao Xu, see Uebaba, K., EMB-M Mar-Apr 05 69-78 Feng Zhonggang, see Zhonggang Feng, EMB-M Jul-Aug 05 73-79 Fielder, J.H. The Vioxx debacle; EMB-M Mar-Apr 05 106-109 Fielder, J.H. Pharmacists refuse to fill emergency contraception prescriptions; EMB-M Jul-Aug 05 88-91 Foster, K.R., and L.G. Kun. In memoriam: Swamy N. Laxminarayan; EMB-M Nov-Dec 05 74 Fujie, M.G., see Kawai, T., EMB-M Jul-Aug 05 57-62 Fujimoto, T., see Masuda, Y., EMB-M Jul-Aug 05 43-47

Hayashi, K., see Ueda, Y., EMB-M Mar-Apr 05 47-51 Heger, S., F. Portheine, J.A.K. Ohnsorge, E. Schkommodau, and K. Radermacher. User-interactive registration of bone with A-mode ultrasound; EMB-M Mar-Apr 05 85-95 Helmike, S.M., see Chia-Yu Yen, EMB-M May-Jun 05 67-72 Henning, G., see Drozd, M., EMB-M Jan-Feb 05 51-58 Higashi, Y., see Masuda, Y., EMB-M Jul-Aug 05 43-47 Hisajima, T., see Uebaba, K., EMB-M Mar-Apr 05 69-78 Hongo, K., see Kawai, T., EMB-M Jul-Aug 05 57-62 Hortobagyi, T., and P. Bonato. Transcranial magnetic stimulation; EMB-M Jan-Feb 05 20-21 Hortobagyi, T. Cross education and the human central nervous system; EMB-M Jan-Feb 05 22-28 Hovmoller, S., see Ison, R.E., EMB-M May-Jun 05 41-49 Hsaio Min Chi, see Berger, T.W., EMB-M Sep-Oct 05 30-44 Humayun, M.S., see Weiland, J.D., EMB-M Sep-Oct 05 12-13 Humayun, M.S., see Weiland, J.D., EMB-M Sep-Oct 05 14-21 Husar, P., see Drozd, M., EMB-M Jan-Feb 05 51-58

G

I

Gao Shumei, see Tanaka, S., EMB-M Jul-Aug 05 32-37 Garcia, G.A., R. Okuno, and K. Azakawa. A decomposition algorithm for surface electrode-array electromyogram; EMB-M Jul-Aug 05 6372 Garg, A., and E. Kokkoli. Characterizing particulate drug-delivery carriers with atomic force microscopy; EMB-M Jan-Feb 05 87-95 Gayler, K., D. Sandall, D. Greening, D. Keays, M. Polidano, B. Livett, J. Down, N. Satkunanathan, and Z. Khalil. Molecular prospecting for drugs from the sea; EMB-M Mar-Apr 05 79-84 Geddes, L.A. Birth of the stethoscope; EMB-M Jan-Feb 05 84-86 Geddes, L.A. Cardiopulmonary resuscitation development; EMB-M Mar-Apr 05 110-113 Geddes, L.A. Retrospectroscope - Science, technology and the researcher; EMB-M May-Jun 05 123-124 Geddes, L.A., see Kemeny, A., EMB-M Jul-Aug 05 80-91 Geller, L., see Karolyi, L., EMB-M Jan-Feb 05 45-50 Gerhardt, G.A., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Gholmieh, G., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Giorgio, T.D. Introduction to Nanotechnology (Poole, C.P. and Ownes, F.J.; 2003) [book review]; EMB-M Mar-Apr 05 123-124 Goldberg, J. Teaching entrepreneurship in senior design courses; EMBM Mar-Apr 05 17-18 Goldberg, J.R. Changing student attitudes, perceptions, and awareness; EMB-M Jul-Aug 05 15-17 Granacki, J., see Wentai Liu, EMB-M Sep-Oct 05 66-74 Granacki, J.J., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Greening, D., see Gayler, K., EMB-M Mar-Apr 05 79-84 Grimes, S.L. The challenge of integrating the healthcare enterprise; EMB-M Mar-Apr 05 122-124 Guangbiao Pu, see Zhongfei Zhang, EMB-M May-Jun 05 117-122 Guoxing Wang, see Wentai Liu, EMB-M Sep-Oct 05 66-74 Gurewitsch, E.D., see Kim, E.J., EMB-M Nov-Dec 05 34-39

Irazoqui, P.P., I. Mody, and J.W. Judy. Recording brain activity wirelessly; EMB-M Nov-Dec 05 48-54 Ison, R.E., S. Hovmoller, and R.H. Kretsinger. Proteins and their shape strings; EMB-M May-Jun 05 41-49 Itou, T., see Uebaba, K., EMB-M Mar-Apr 05 69-78

Egorova, N.A., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Erickson, J., see Tooker, A., EMB-M Nov-Dec 05 30-33 Erinjippurath, G., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Erne, S.N., see Muller, H.-P., EMB-M May-Jun 05 109-116 F

H Hamady, M., T.H.T. Cheung, K. Resing, K.J. Cios, and R. Knight. Key challenges in proteomics and proteoinformatics; EMB-M May-Jun 05 34-40 Hamady, M., T.H.T. Cheung, H. Tufo, and R. Knight. Does protein structure influence trypsin miscleavage?; EMB-M May-Jun 05 58-66 Hammer, P.L., see Blazewicz, J., EMB-M May-Jun 05 88-94 Hampson, R., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Hanli Liu, see Kim, J.G., EMB-M Mar-Apr 05 118-121 Havre, S.L., M. Singhal, D.A. Payne, M.S. Weir Lipton, and B.-J.M. Webb-Robertson. Enabling proteomics discovery through visual analysis; EMB-M May-Jun 05 50-57

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

J Jaron, D., see Kern, S.E., EMB-M Mar-Apr 05 28-29 Jian Yao, see Zhongfei Zhang, EMB-M May-Jun 05 117-122 Jimenez, A.R., see Ceres, R., EMB-M Nov-Dec 05 55-63 Judy, J.W., see Irazoqui, P.P., EMB-M Nov-Dec 05 48-54 K Kamohara, S., see Atsumi, K., EMB-M Mar-Apr 05 30-34 Kan, K., see Kawai, T., EMB-M Jul-Aug 05 57-62 Karolyi, L., S.G. Spitzer, L. Geller, O. Kiss, A. Laszik, P. Sotonyi, and B. Merkely. Isthmus ablation with a novel microwave catheter in dogs; EMB-M Jan-Feb 05 45-50 Kassubek, J., see Muller, H.-P., EMB-M May-Jun 05 109-116 Kawai, T., K. Kan, K. Hongo, K. Nishizawa, F. Tajima, M.G. Fujie, T. Dohi, and K. Takakura. Bipolar coagulation - capable microforceps; EMB-M Jul-Aug 05 57-62 Keays, D., see Gayler, K., EMB-M Mar-Apr 05 79-84 Kemeny, A., and L.A. Geddes. Military antishock trousers; EMB-M JulAug 05 80-91 Kern, S.E., and D. Jaron. Complementary and alternative medicine in the technology age; EMB-M Mar-Apr 05 28-29 Khalil, Z., see Gayler, K., EMB-M Mar-Apr 05 79-84 Kim, E.J., T. Parnduangjai, M.K. McDonald, E.D. Gurewitsch, and R.H. Allen. A biofidelic birthing simulator; EMB-M Nov-Dec 05 34-39 Kim, J.G., Mengna Xia, and Hanli Liu. Extinction coefficients of hemoglobin for near-infrared spectroscopy of tissue; EMB-M Mar-Apr 05 118-121 King, P. Risk Assessment: Basics and Benchmarks (Main, B., Ed.; 2004) [book review]; EMB-M Jan-Feb 05 100 King, P. Ten Questions About Human Error, A New View of Human Factors and System Safety (Dekker, S.W.A.; 2005) [book review]; EMB-M May-Jun 05 18 King, P.H., and TN Vanderbilt Univ., USA. Engineering Design (Eggert, R.; 2005) [book review]; EMB-M Jan-Feb 05 97-98 King, P.J. Sensors Applications, Vol. 3. Sensors in Medicine and Health Care (Oberg, P. et al., Eds; 2004) [book review]; EMB-M Mar-Apr 05 123 Kiss, O., see Karolyi, L., EMB-M Jan-Feb 05 45-50

JANUARY/FEBRUARY 2006 115

Annual Index (continued)

Klee, M.M. What is a reasonable royalty?; EMB-M Jan-Feb 05 96 Klee, M.M. Keeping your stories straight [Patents]; EMB-M May-Jun 05 128 Klee, M.M. Be careful of what you say [Patents]; EMB-M Nov-Dec 05 64 Knight, R., see Hamady, M., EMB-M May-Jun 05 34-40 Knight, R., see Hamady, M., EMB-M May-Jun 05 58-66 Knudson, P.E., see Zhongfei Zhang, EMB-M May-Jun 05 117-122 Koch, K.P., see Stieglitz, T., EMB-M Sep-Oct 05 58-65 Kokkoli, E., see Garg, A., EMB-M Jan-Feb 05 87-95 Konofagou, E.E., and H.M. Langevin. Using ultrasound to understand acupuncture; EMB-M Mar-Apr 05 41-46 Korochkina, O.V., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Kretsinger, R.H., see Ison, R.E., EMB-M May-Jun 05 41-49 Kriederman, B., see Bernas, M., EMB-M Mar-Apr 05 58-68 Krol, A., see Zhongfei Zhang, EMB-M May-Jun 05 117-122 Kun, L.G. Technology, policy, and funding [Government Affairs]; EMB-M May-Jun 05 129 Kun, L.G., see Foster, K.R., EMB-M Nov-Dec 05 74 Kuriowa, K., see Ueda, Y., EMB-M Mar-Apr 05 47-51

Mody, I., see Irazoqui, P.P., EMB-M Nov-Dec 05 48-54 Muller, H.-P., I. Decesaris, M. Demelis, L. Marzetti, A. Pasquarelli, S.N. Erne, A.C. Ludolph, and J. Kassubek. Open magnetic and electric graphic analysis; EMB-M May-Jun 05 109-116 Murari, K., M. Stanacevic, G. Cauwenberghs, and N.V. Thakor. Integrated potentiostat for neurotransmitter sensing; EMB-M NovDec 05 23-29

L

O

Lacoss, J., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Lacoss, J., see Wentai Liu, EMB-M Sep-Oct 05 66-74 Langevin, H.M., see Konofagou, E.E., EMB-M Mar-Apr 05 41-46 Laszik, A., see Karolyi, L., EMB-M Jan-Feb 05 45-50 Lazzi, G. Thermal effects of bioimplants; EMB-M Sep-Oct 05 75-81 Liu Hanli, see Kim, J.G., EMB-M Mar-Apr 05 118-121 Liu Wentai, see Wentai Liu, EMB-M Sep-Oct 05 66-74 Livett, B., see Gayler, K., EMB-M Mar-Apr 05 79-84 Loeb, G.E., and R. Davoodi. The functional reanimation of paralyzed limbs; EMB-M Sep-Oct 05 45-51 Ludolph, A.C., see Muller, H.-P., EMB-M May-Jun 05 109-116 Lukasiak, P., see Blazewicz, J., EMB-M May-Jun 05 88-94 M Madhavan, G. Understanding the Human Machine - A Primer for Bioengineering (Valentinuzzi, M.E.; 2004) [book review]; EMB-M Jan-Feb 05 97 Madhavan, G. Novel Vaccination Strategies (Kaufmann, S.H.E., Ed.; 2004) [book review]; EMB-M Jan-Feb 05 98, 100 Madhavan, G. Biomedical Platforms - Realigning the Normal and the Pathological in Late Twentieth-Century Medicine (Keating, P. and Cambrosio, A.; 2003) [book review]; EMB-M May-Jun 05 19 Mamitsuka, H. Mining new protein-protein interactions; EMB-M MayJun 05 103-108 Marmarelis, V.Z., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Marzetti, L., see Muller, H.-P., EMB-M May-Jun 05 109-116 Masuda, Y., M. Sekimoto, M. Nambu, Y. Higashi, T. Fujimoto, K. Chihara, and Y. Tamura. An unconstrained monitoring system for home rehabilitation; EMB-M Jul-Aug 05 43-47 Matsumoto, T., see Zhonggang Feng, EMB-M Jul-Aug 05 73-79 McDonald, M.K., see Kim, E.J., EMB-M Nov-Dec 05 34-39 McDougall, M.P., and S.M. Wright. Phase compensation in single echo acquisition imaging; EMB-M Nov-Dec 05 17-22 Meneses, C.N., C.A.S. Oliveira, and P.M. Pardalos. Optimization techniques for string selection and comparison problems in genomics; EMB-M May-Jun 05 81-87 Meng, E., see Tooker, A., EMB-M Nov-Dec 05 30-33 Mengna Xia, see Kim, J.G., EMB-M Mar-Apr 05 118-121 Merkely, B., see Karolyi, L., EMB-M Jan-Feb 05 45-50 Min Chi Hsaio, see Berger, T.W., EMB-M Sep-Oct 05 30-44 Mingcui Zhou, see Wentai Liu, EMB-M Sep-Oct 05 66-74

116 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

N Nakajima, K., see Nambu, M., EMB-M Jul-Aug 05 38-42 Nakamura, T., see Zhonggang Feng, EMB-M Jul-Aug 05 73-79 Nambu, M., K. Nakajima, M. Noshiro, and T. Tamura. An algorithm for the automatic detection of health conditions; EMB-M Jul-Aug 05 38-42 Nambu, M., see Masuda, Y., EMB-M Jul-Aug 05 43-47 Nasiatka, P., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Nishizawa, K., see Kawai, T., EMB-M Jul-Aug 05 57-62 Nogawa, M., see Tanaka, S., EMB-M Jul-Aug 05 32-37 Nomura, Y., see Zhonggang Feng, EMB-M Jul-Aug 05 73-79 Noshiro, M., see Nambu, M., EMB-M Jul-Aug 05 38-42 Nowakowski, A., see Drozd, M., EMB-M Jan-Feb 05 51-58

Ogawa, H., see Uebaba, K., EMB-M Mar-Apr 05 69-78 Ohnsorge, J.A.K., see Heger, S., EMB-M Mar-Apr 05 85-95 Okuno, R., M. Yoshida, and K. Akazawa. Compliant grasp in a myoelectric hand prosthesis; EMB-M Jul-Aug 05 48-56 Okuno, R., see Garcia, G.A., EMB-M Jul-Aug 05 63-72 Oliveira, C.A.S., see Meneses, C.N., EMB-M May-Jun 05 81-87 Orum, H. RNA antagonists - a new class of antisense drugs; EMB-M Jul-Aug 05 81-87 P Panescu, D. Cardiac resynchronization therapy; EMB-M Mar-Apr 05 22-26 Panescu, D. An imaging pill for gastrointestinal endoscopy; EMB-M Jul-Aug 05 12-14 Panescu, D. Vagus nerve stimulation for the treatment of depression [Emerging Technologies]; EMB-M Nov-Dec 05 68-72 Pardalos, P.M., see Meneses, C.N., EMB-M May-Jun 05 81-87 Parnduangjai, T., see Kim, E.J., EMB-M Nov-Dec 05 34-39 Parrish, T.B., A. Schaeffer, M. Catanese, and M.J. Rogel. Functional magnetic resonance imaging of real and sham acupuncture; EMB-M Mar-Apr 05 35-40 Pascual-Leone, A., see Valero-Cabre, A., EMB-M Jan-Feb 05 29-35 Pasquarelli, A., see Muller, H.-P., EMB-M May-Jun 05 109-116 Payne, D.A., see Havre, S.L., EMB-M May-Jun 05 50-57 Pedrotty, D.M., and N. Bursac. Cellular/tissue engineering Cardiomyoplasty: The prospect of human stem cells; EMB-M MayJun 05 125-127 Pelyushenko, A.S., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Perryman, M.B., see Chia-Yu Yen, EMB-M May-Jun 05 67-72 Pine, J., see Tooker, A., EMB-M Nov-Dec 05 30-33 Polidano, M., see Gayler, K., EMB-M Mar-Apr 05 79-84 Pons, J.L., see Ceres, R., EMB-M Nov-Dec 05 55-63 Portheine, F., see Heger, S., EMB-M Mar-Apr 05 85-95 Pu Guangbiao, see Zhongfei Zhang, EMB-M May-Jun 05 117-122 R Radermacher, K., see Heger, S., EMB-M Mar-Apr 05 85-95 Rangayvan, R.M., see Ayres, F.J., EMB-M Jan-Feb 05 59-67 Resing, K., see Hamady, M., EMB-M May-Jun 05 34-40 Rodger, D.C., and Yu-Chong Tai. Microelectronic packaging for retinal prostheses; EMB-M Sep-Oct 05 52-57

JANUARY/FEBRUARY 2006

Rogel, M.J., see Parrish, T.B., EMB-M Mar-Apr 05 35-40 Rosenkranz, K., see Rothwell, J.C., EMB-M Jan-Feb 05 40-44 Rothwell, J.C., and K. Rosenkranz. Role of afferent input in motor organization in health and disease; EMB-M Jan-Feb 05 40-44 Ruofei Zhang, see Zhongfei Zhang, EMB-M May-Jun 05 117-122 S Sandall, D., see Gayler, K., EMB-M Mar-Apr 05 79-84 Sanin, A.G., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Sanina, O.A., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Satkunanathan, N., see Gayler, K., EMB-M Mar-Apr 05 79-84 Sawaki, L. Use-dependent plasticity of the human motor cortex in health and disease; EMB-M Jan-Feb 05 36-39 Schaeffer, A., see Parrish, T.B., EMB-M Mar-Apr 05 35-40 Scheltes, J.S., see Breedveld, P., EMB-M Nov-Dec 05 40-47 Schkommodau, E., see Heger, S., EMB-M Mar-Apr 05 85-95 Schoenfeld, R.L. Ernest Amatniek (1915-2005); Extraordinary bioengineer; EMB-M Sep-Oct 05 6 Schuetter, M., see Stieglitz, T., EMB-M Sep-Oct 05 58-65 Sekimoto, M., see Masuda, Y., EMB-M Jul-Aug 05 43-47 Shimabayashi, M., see Uebaba, K., EMB-M Mar-Apr 05 69-78 Shumei Gao, see Tanaka, S., EMB-M Jul-Aug 05 32-37 Shusterman, V., and O. Barnea. Sympathetic nervous system activity in stress and biofeedback relaxation; EMB-M Mar-Apr 05 52-57 Singh, G.B., and H. Singh. Functional proteomics with biolinguistic methods; EMB-M May-Jun 05 73-80 Singh, H., see Singh, G.B., EMB-M May-Jun 05 73-80 Singhal, M., see Havre, S.L., EMB-M May-Jun 05 50-57 Sivachenko, A.Y., see Chen, J.Y., EMB-M May-Jun 05 95-102 Sivaprakasam, M., see Wentai Liu, EMB-M Sep-Oct 05 66-74 Smetanina, S.V., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Song Dong, see Berger, T.W., EMB-M Sep-Oct 05 30-44 Sotonyi, P., see Karolyi, L., EMB-M Jan-Feb 05 45-50 Spitzer, S.G., see Karolyi, L., EMB-M Jan-Feb 05 45-50 Srinivasan, V., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Stanacevic, M., see Murari, K., EMB-M Nov-Dec 05 23-29 Stieglitz, T., M. Schuetter, and K.P. Koch. Implantable biomedical microsystems for neural prostheses; EMB-M Sep-Oct 05 58-65 Sugimachi, M., and K. Sunagawa. Bionic cardiovascular medicine; EMB-M Jul-Aug 05 24-31 Summers, P., see Bernas, M., EMB-M Mar-Apr 05 58-68 Sunagawa, K., see Sugimachi, M., EMB-M Jul-Aug 05 24-31 Sun Wei, see Darling, A.L., EMB-M Jan-Feb 05 78-83

U Uebaba, K., Feng-Hao Xu, M. Tagawa, R. Asakura, T. Itou, T. Tatsue, Y. Taguchi, H. Ogawa, M. Shimabayashi, and T. Hisajima. Using a healing robot for the scientific study of Shirodhara; EMB-M MarApr 05 69-78 Ueda, Y., K. Hayashi, and K. Kuriowa. The application of fMRI to basic experiments in acupuncture; EMB-M Mar-Apr 05 47-51 V Valero-Cabre, A., and A. Pascual-Leone. Impact of TMS on the primary motor cortex and associated spinal systems; EMB-M Jan-Feb 05 29-35 Vanderbilt Univ., TN, USA, see King, P.H., EMB-M Jan-Feb 05 97-98 Verheij, J.E.I., see Breedveld, P., EMB-M Nov-Dec 05 40-47 W Wang Binseng, see Binseng Wang, EMB-M May-Jun 05 18-19 Wang Guoxing, see Wentai Liu, EMB-M Sep-Oct 05 66-74 Webb-Robertson, B.-J.M., see Havre, S.L., EMB-M May-Jun 05 50-57 Weiland, J.D., and M.S. Humayun. Old idea, new technology, the guest editors discuss biomimetics [special section intro.]; EMB-M Sep-Oct 05 12-13 Weiland, J.D., and M.S. Humayun. A biomimetic retinal stimulating array; EMB-M Sep-Oct 05 14-21 Weinstock, R.S., see Zhongfei Zhang, EMB-M May-Jun 05 117-122 Weir Lipton, M.S., see Havre, S.L., EMB-M May-Jun 05 50-57 Wei Sun, see Darling, A.L., EMB-M Jan-Feb 05 78-83 Wentai Liu, M. Sivaprakasam, Guoxing Wang, Mingcui Zhou, J. Granacki, J. Lacoss, and J. Wills. Implantable biomimetic microelectronic systems design; EMB-M Sep-Oct 05 66-74 White, M., see Bernas, M., EMB-M Mar-Apr 05 58-68 Wills, J., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Wills, J., see Wentai Liu, EMB-M Sep-Oct 05 66-74 Wise, K.D. Silicon microsystems for neuroscience and neural prostheses; EMB-M Sep-Oct 05 22-29 Witte, C., see Bernas, M., EMB-M Mar-Apr 05 58-68 Wright, S.M., see McDougall, M.P., EMB-M Nov-Dec 05 17-22 X Xia Mengna, see Kim, J.G., EMB-M Mar-Apr 05 118-121 Xu Feng-Hao, see Uebaba, K., EMB-M Mar-Apr 05 69-78

T

Y

Tagawa, M., see Uebaba, K., EMB-M Mar-Apr 05 69-78 Taguchi, Y., see Uebaba, K., EMB-M Mar-Apr 05 69-78 Tai Yu-Chong, see Rodger, D.C., EMB-M Sep-Oct 05 52-57 Tai Yu-Chong, see Tooker, A., EMB-M Nov-Dec 05 30-33 Tajima, F., see Kawai, T., EMB-M Jul-Aug 05 57-62 Takakura, K., see Kawai, T., EMB-M Jul-Aug 05 57-62 Tamura, T. Biomedical engineering at the forefront in Japan; EMB-M Jul-Aug 05 23 Tamura, T., see Nambu, M., EMB-M Jul-Aug 05 38-42 Tamura, Y., see Masuda, Y., EMB-M Jul-Aug 05 43-47 Tanaka, S., Shumei Gao, M. Nogawa, and K.-I. Yamakoshi. Noninvasive measurement of instantaneous, radial artery blood pressure; EMB-M Jul-Aug 05 32-37 Tanguay, A.R., see Berger, T.W., EMB-M Sep-Oct 05 30-44 Tatsue, T., see Uebaba, K., EMB-M Mar-Apr 05 69-78 Terentiev, I.G., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Thakor, N.V., see Murari, K., EMB-M Nov-Dec 05 23-29 Tooker, A., E. Meng, J. Erickson, Yu-Chong Tai, and J. Pine. Biocompatible parylene neurocages; EMB-M Nov-Dec 05 30-33 Tufo, H., see Hamady, M., EMB-M May-Jun 05 58-66

Yakhno, T.A., V.G. Yakhno, A.G. Sanin, O.A. Sanina, A.S. Pelyushenko, N.A. Egorova, I.G. Terentiev, S.V. Smetanina, O.V. Korochkina, and E.V. Yashukova. The informative-capacity phenomenon of drying drops; EMB-M Mar-Apr 05 96-104 Yakhno, V.G., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Yamakoshi, K.-I., see Tanaka, S., EMB-M Jul-Aug 05 32-37 Yao Jian, see Zhongfei Zhang, EMB-M May-Jun 05 117-122 Yashukova, E.V., see Yakhno, T.A., EMB-M Mar-Apr 05 96-104 Yen Chia-Yu, see Chia-Yu Yen, EMB-M May-Jun 05 67-72 Yoshida, M., see Okuno, R., EMB-M Jul-Aug 05 48-56 Yu-Chong Tai, see Rodger, D.C., EMB-M Sep-Oct 05 52-57 Yu-Chong Tai, see Tooker, A., EMB-M Nov-Dec 05 30-33

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Z Zhang Ruofei, see Zhongfei Zhang, EMB-M May-Jun 05 117-122 Zhang Zhongfei, see Zhongfei Zhang, EMB-M May-Jun 05 117-122 Zhongfei Zhang, Ruofei Zhang, Jian Yao, Guangbiao Pu, P.E. Knudson, R.S. Weinstock, and A. Krol. Medical data on demand with WebMIA; EMB-M May-Jun 05 117-122

JANUARY/FEBRUARY 2006 117

Annual Index (continued)

Zhonggang Feng, T. Matsumoto, Y. Nomura, and T. Nakamura. An electro-tensile bioreactor for 3-D culturing of cardiomyocytes; EMBM Jul-Aug 05 73-79 Zhou Mingcui, see Wentai Liu, EMB-M Sep-Oct 05 66-74 SUBJECT INDEX A Actuators computer vision and graphics. Coatrieux, J.L., EMB-M Jan-Feb 05 16-19 paralyzed limbs, functional reanimation. Loeb, G.E., + , EMB-M Sep-Oct 05 45-51 Amatniek, Ernest obituaries. Schoenfeld, R.L., EMB-M Sep-Oct 05 6 Aquaculture drugs from sea, mol. prospecting. Gayler, K., + , EMB-M Mar-Apr 05 79-84 Arrays biomimetic retinal stimulating array. Weiland, J.D., + , EMB-M SepOct 05 14-21 neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 retinal prostheses, microelectronic packaging. Rodger, D.C., + , EMB-M Sep-Oct 05 52-57 Atomic force microscopy characterizing particulate drug-delivery carriers, AFM. Garg, A., + , EMB-M Jan-Feb 05 87-95 Automation; cf. Robots Awards IEEE Engineering in Medicine and Biology Society presents best Student Paper Award to A. Tooker, E. Meng, J. Erickson, YuChong Tai, and J. Pine. EMB-M Nov-Dec 05 30 IEEE Engineering in Medicine and Biology Society presents best Student Paper Award to E.J. Kim, P. Theprungsirikul, M.K. McDonald, E.D. Gurewitsch, and R.H. Allen. EMB-M Nov-Dec 05 34 IEEE Engineering in Medicine and Biology Society presents best Student Paper Award to K. Murari, M. Stanacevic, G. Cauwenberghs, and N.V. Thakor. EMB-M Nov-Dec 05 23 IEEE Engineering in Medicine and Biology Society presents best Student Paper Award to M.P. McDougall and S.M. Wright. EMBM Nov-Dec 05 17 B Benchmarking book review; Risk Assessment: Basics and Benchmarks (Main, B., Ed.; 2004). King, P., EMB-M Jan-Feb 05 100 Biocybernetics; cf. Brain models Bioelectric phenomena 3D culturing of cardiomyocytes, electro-tensile bioreactor. Zhonggang Feng, + , EMB-M Jul-Aug 05 73-79 biomimetic retinal stimulating array. Weiland, J.D., + , EMB-M SepOct 05 14-21 bipolar coagulation, capable microforceps. Kawai, T., + , EMB-M Jul-Aug 05 57-62 cardiac resynchronization therapy. Panescu, D., EMB-M Mar-Apr 05 22-26 cellular/tissue engng., Cardiomyoplasty. Pedrotty, D.M., + , EMB-M May-Jun 05 125-127 open mag. and elec. graphic anal. Muller, H.-P., + , EMB-M MayJun 05 109-116

118 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

restoring lost cognitive fn. Berger, T.W., + , EMB-M Sep-Oct 05 30-44 TMS, primary motor cortex and assoc. spinal systs., impact. ValeroCabre, A., + , EMB-M Jan-Feb 05 29-35 transcranial mag. stimulation. Hortobagyi, T., + , EMB-M Jan-Feb 05 20-21 Bioelectric phenomena; cf. Bioelectric potentials Bioelectric potentials afferent input, motor organization, health and disease, role. Rothwell, J.C., + , EMB-M Jan-Feb 05 40-44 anal. of event-related potentials, dyn. time warping. Casarotto, S., + , EMB-M Jan-Feb 05 68-77 detecting evoked potentials, SVD- and ICA-based stat. models. Drozd, M., + , EMB-M Jan-Feb 05 51-58 Bioelectric potentials; cf. Electromyography Biohazards book review; Novel Vaccination Strategies (Kaufmann, S.H.E., Ed.; 2004). Madhavan, G., EMB-M Jan-Feb 05 98, 100 Biological cells 3D culturing of cardiomyocytes, electro-tensile bioreactor. Zhonggang Feng, + , EMB-M Jul-Aug 05 73-79 cellular/tissue engng., Cardiomyoplasty. Pedrotty, D.M., + , EMB-M May-Jun 05 125-127 Japan, biomedical engng., forefront. Tamura, T., EMB-M Jul-Aug 05 23 neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 poly-?-caprolactone tissue scaffolds, free-form fab. and micro-CT charactn. Darling, A.L., + , EMB-M Jan-Feb 05 78-83 protein interactomics, data mining. Chen, J.Y., + , EMB-M May-Jun 05 95-102 Biological organs; cf. Brain Biological system modeling detecting evoked potentials, SVD- and ICA-based stat. models. Drozd, M., + , EMB-M Jan-Feb 05 51-58 mining, protein-protein interacts. Mamitsuka, H., EMB-M May-Jun 05 103-108 Biological thermal factors bioimplants, thermal effects. Lazzi, G., EMB-M Sep-Oct 05 75-81 stress and biofeedback relax., sympathetic nervous syst. activity. Shusterman, V., + , EMB-M Mar-Apr 05 52-57 Biological tissues; cf. Skin Biology; cf. Genetics Biomagnetics afferent input, motor organization, health and disease, role. Rothwell, J.C., + , EMB-M Jan-Feb 05 40-44 human motor cortex, health and disease, use-depend. plasticity. Sawaki, L., EMB-M Jan-Feb 05 36-39 TMS, primary motor cortex and assoc. spinal systs., impact. ValeroCabre, A., + , EMB-M Jan-Feb 05 29-35 transcranial mag. stimulation. Hortobagyi, T., + , EMB-M Jan-Feb 05 20-21 Biomagnetism; cf. Magnetoencephalography Biomechanics 3D culturing of cardiomyocytes, electro-tensile bioreactor. Zhonggang Feng, + , EMB-M Jul-Aug 05 73-79 afferent input, motor organization, health and disease, role. Rothwell, J.C., + , EMB-M Jan-Feb 05 40-44 cross education and human CNS. Hortobagyi, T., EMB-M Jan-Feb 05 22-28 myoelectric hand prosthesis, compliant grasp. Okuno, R., + , EMBM Jul-Aug 05 48-56 paralyzed limbs, functional reanimation. Loeb, G.E., + , EMB-M Sep-Oct 05 45-51 transcranial mag. stimulation. Hortobagyi, T., + , EMB-M Jan-Feb 05 20-21 understand acupuncture, ultrasound. Konofagou, E.E., + , EMB-M Mar-Apr 05 41-46

JANUARY/FEBRUARY 2006

Biomedical acoustics stethoscope, birth. Geddes, L.A., EMB-M Jan-Feb 05 84-86 Biomedical communication TalkAustin, medical communication service. Demir, S.S., EMB-M Jan-Feb 05 14-15, 19 Biomedical communication; cf. Biomedical telemetry; PACS Biomedical computing enabling proteomics discovery, visual anal. Havre, S.L., + , EMB-M May-Jun 05 50-57 functional proteomics, biolinguistic methods. Singh, G.B., + , EMBM May-Jun 05 73-80 mining, protein-protein interacts. Mamitsuka, H., EMB-M May-Jun 05 103-108 protein interactomics, data mining. Chen, J.Y., + , EMB-M May-Jun 05 95-102 proteins and their shape strings. Ison, R.E., + , EMB-M May-Jun 05 41-49 proteins, predicting secondary structs. Blazewicz, J., + , EMB-M May-Jun 05 88-94 protein struct. influence trypsin miscleavage. Hamady, M., + , EMBM May-Jun 05 58-66 proteomics and proteoinformatics, key challenges. Hamady, M., + , EMB-M May-Jun 05 34-40 proteomics, data mining, quantitat. anal. Chia-Yu Yen, + , EMB-M May-Jun 05 67-72 retrospectroscope, Science, technol. and researcher. Geddes, L.A., EMB-M May-Jun 05 123-124 string selection and comp. problems, genomics, optim. techs. Meneses, C.N., + , EMB-M May-Jun 05 81-87 Biomedical education book review; Engineering Design (Eggert, R.; 2005). King, P.H., + , EMB-M Jan-Feb 05 97-98 Biomedical engineering biomimetic systems (special section). EMB-M Sep-Oct 05 12-91 biomimetic systems (special section intro.). Weiland, J.D., + , EMBM Sep-Oct 05 12-13 book review; Introduction to Nanotechnology (Poole, C.P. and Ownes, F.J.; 2003). Giorgio, T.D., EMB-M Mar-Apr 05 123124 book review; Understanding the Human Machine - A Primer for Bioengineering (Valentinuzzi, M.E.; 2004). Madhavan, G., EMBM Jan-Feb 05 97 bridging conventional med. and complementary and alternative med. Atsumi, K., + , EMB-M Mar-Apr 05 30-34 computer vision and graphics. Coatrieux, J.L., EMB-M Jan-Feb 05 16-19 integrat. healthcare enterprise, challenge. Grimes, S.L., EMB-M Mar-Apr 05 122-124 Japan, biomedical engng., forefront. Tamura, T., EMB-M Jul-Aug 05 23 retrospectroscope, Science, technol. and researcher. Geddes, L.A., EMB-M May-Jun 05 123-124 technol. age, complementary and alternative med. Kern, S.E., + , EMB-M Mar-Apr 05 28-29 transcranial mag. stimulation. Hortobagyi, T., + , EMB-M Jan-Feb 05 20-21 Biomedical engineering; cf. Biomedical communication; Biomedical education Biomedical engineering education changing student attitudes, perceptions, awareness. Goldberg, J.R., EMB-M Jul-Aug 05 15-17 senior design courses, teaching entrepreneurship. Goldberg, J., EMB-M Mar-Apr 05 17-18 turning students, sci. stars (science and engng. education). Cocozza, J.D., + , EMB-M Sep-Oct 05 82-91 Biomedical equipment bionic cardiovascular med. Sugimachi, M., + , EMB-M Jul-Aug 05 24-31

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

book review; Biomedical Platforms - Realigning the Normal and the Pathological in Late-Twentieth-Century Medicine (Keating, P. and Cambrosio, A.; 2003). Madhavan, G., EMB-M May-Jun 05 19 book review; Clinical Engineering (David, Y. et al., eds.; 2003). Binseng Wang, EMB-M May-Jun 05 18-19 drying drops, informative-capacity phenom. Yakhno, T.A., + , EMBM Mar-Apr 05 96-104 FDA’s annual report on medical devices. Bartoo, G., EMB-M JanFeb 05 99-100 instantaneous, radial artery blood press., noninvasive meas. Tanaka, S., + , EMB-M Jul-Aug 05 32-37 integrat. healthcare enterprise, challenge. Grimes, S.L., EMB-M Mar-Apr 05 122-124 senior design courses, teaching entrepreneurship. Goldberg, J., EMB-M Mar-Apr 05 17-18 stethoscope, birth. Geddes, L.A., EMB-M Jan-Feb 05 84-86 Biomedical equipment; cf. Biomedical telemetry; Endoscopes Biomedical image processing architectural distortion, mammograms, charactn. Ayres, F.J., + , EMB-M Jan-Feb 05 59-67 automatic detect. of health conds., algm. Nambu, M., + , EMB-M Jul-Aug 05 38-42 bone, A-mode ultrasound, user-interactive registration. Heger, S., + , EMB-M Mar-Apr 05 85-95 look at. shape and fn. from motion, med. imaging, Part I. Coatrieux, J.L., EMB-M May-Jun 05 29-31 open mag. and elec. graphic anal. Muller, H.-P., + , EMB-M MayJun 05 109-116 poly-?-caprolactone tissue scaffolds, free-form fab. and micro-CT charactn. Darling, A.L., + , EMB-M Jan-Feb 05 78-83 Biomedical imaging characterizing particulate drug-delivery carriers, AFM. Garg, A., + , EMB-M Jan-Feb 05 87-95 computer vision and graphics. Coatrieux, J.L., EMB-M Jan-Feb 05 16-19 Biomedical imaging; cf. Biomedical MRI; Medical image processing Biomedical materials book review; Sensors Applications, Vol. 3. Sensors in Medicine and Health Care (Oberg, P. et al., Eds.; 2004). King, P.J., EMB-M MarApr 05 123 Biomedical measurement; cf. Biomedical telemetry; Blood pressure measurement Biomedical monitoring home rehabilitation, unconstrained monitoring syst. Masuda, Y., + , EMB-M Jul-Aug 05 43-47 instantaneous, radial artery blood press., noninvasive meas. Tanaka, S., + , EMB-M Jul-Aug 05 32-37 scientific study of Shirodhara, healing robot. Uebaba, K., + , EMBM Mar-Apr 05 69-78 Biomedical MRI phase compensation in single echo acquisition imaging. McDougall, M.P., + , EMB-M Nov-Dec 05 17-22 Biomedical signal processing anal. of event-related potentials, dyn. time warping. Casarotto, S., + , EMB-M Jan-Feb 05 68-77 bionic cardiovascular med. Sugimachi, M., + , EMB-M Jul-Aug 05 24-31 detecting evoked potentials, SVD- and ICA-based stat. models. Drozd, M., + , EMB-M Jan-Feb 05 51-58 instantaneous, radial artery blood press., noninvasive meas. Tanaka, S., + , EMB-M Jul-Aug 05 32-37 myoelectric hand prosthesis, compliant grasp. Okuno, R., + , EMBM Jul-Aug 05 48-56 surface electrode-array EMG, decomp. algm. Garcia, G.A., + , EMB-M Jul-Aug 05 63-72 Biomedical telemetry bioimplants, thermal effects. Lazzi, G., EMB-M Sep-Oct 05 75-81

JANUARY/FEBRUARY 2006 119

Annual Index (continued)

implantable biomimetic microelectronic systs. design. Wentai Liu, + , EMB-M Sep-Oct 05 66-74 retrospectroscope, Science, technol. and researcher. Geddes, L.A., EMB-M May-Jun 05 123-124 Biophysics; cf. Bioelectric phenomena; Biomechanics Blood flow cardiopulmonary resuscitation develop. Geddes, L.A., EMB-M MarApr 05 110-113 mil. antishock trousers. Kemeny, A., + , EMB-M Jul-Aug 05 80-91 real and sham acupuncture, fMRI. Parrish, T.B., + , EMB-M MarApr 05 35-40 Blood pressure measurement bionic cardiovascular med. Sugimachi, M., + , EMB-M Jul-Aug 05 24-31 instantaneous, radial artery blood press., noninvasive meas. Tanaka, S., + , EMB-M Jul-Aug 05 32-37 scientific study of Shirodhara, healing robot. Uebaba, K., + , EMBM Mar-Apr 05 69-78 Bones A-mode ultrasound, user-interactive registration. Heger, S., + , EMB-M Mar-Apr 05 85-95 Book reviews Biomedical Platforms - Realigning the Normal and the Pathological in Late Twentieth-Century Medicine (Keating, P. and Cambrosio, A.; 2003). Madhavan, G., EMB-M May-Jun 05 19 Clinical Engineering (David, Y. et al., eds.; 2003). Binseng Wang, EMB-M May-Jun 05 18-19 Engineering Design (Eggert, R.; 2005). King, P.H., + , EMB-M JanFeb 05 97-98 Introduction to Nanotechnology (Poole, C.P. and Ownes, F.J.; 2003) [book review]. Giorgio, T.D., EMB-M Mar-Apr 05 123-124 Novel Vaccination Strategies (Kaufmann, S.H.E., Ed.; 2004). Madhavan, G., EMB-M Jan-Feb 05 98, 100 PACS and Imaging Informatics; Basic Principles and Applications (Huang, H.K., ed.; 2004). Demiris, G., EMB-M Jul-Aug 05 92 Risk Assessment: Basics and Benchmarks (Main, B., Ed.; 2004). King, P., EMB-M Jan-Feb 05 100 Sensors Applications, Vol. 3. Sensors in Medicine and Health Care (Oberg, P. et al., Eds.; 2004). King, P.J., EMB-M Mar-Apr 05 123 Ten Questions About Human Error, A New View of Human Factors and System Safety (Dekker, S.W.A.; 2005). King, P., EMB-M May-Jun 05 18 Understanding the Human Machine - A Primer for Bioengineering (Valentinuzzi, M.E.; 2004). Madhavan, G., EMB-M Jan-Feb 05 97 Brain afferent input, motor organization, health and disease, role. Rothwell, J.C., + , EMB-M Jan-Feb 05 40-44 anal. of event-related potentials, dyn. time warping. Casarotto, S., + , EMB-M Jan-Feb 05 68-77 bipolar coagulation, capable microforceps. Kawai, T., + , EMB-M Jul-Aug 05 57-62 fMRI, basic expts., acupuncture. Ueda, Y., + , EMB-M Mar-Apr 05 47-51 human motor cortex, health and disease, use-depend. plasticity. Sawaki, L., EMB-M Jan-Feb 05 36-39 mil. antishock trousers. Kemeny, A., + , EMB-M Jul-Aug 05 80-91 neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 real and sham acupuncture, fMRI. Parrish, T.B., + , EMB-M MarApr 05 35-40 restoring lost cognitive fn. Berger, T.W., + , EMB-M Sep-Oct 05 30-44 Brain; cf. Brain models; Electroencephalography; Magnetoencephalography Brain models recording brain activity wirelessly. Irazoqui, P.P., + , EMB-M NovDec 05 48-54

120 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

C Cardiac pacemakers cardiac resynchronization therapy. Panescu, D., EMB-M Mar-Apr 05 22-26 Cardiography 3D culturing of cardiomyocytes, electro-tensile bioreactor. Zhonggang Feng, + , EMB-M Jul-Aug 05 73-79 cardiac resynchronization therapy. Panescu, D., EMB-M Mar-Apr 05 22-26 cardiopulmonary resuscitation develop. Geddes, L.A., EMB-M MarApr 05 110-113 cellular/tissue engng., Cardiomyoplasty. Pedrotty, D.M., + , EMB-M May-Jun 05 125-127 home rehabilitation, unconstrained monitoring syst. Masuda, Y., + , EMB-M Jul-Aug 05 43-47 isthmus ablation, microwave catheter, dogs. Karolyi, L., + , EMB-M Jan-Feb 05 45-50 Cardiology; cf. Cardiovascular system; Electrocardiography Cardiovascular system Vioxx debacle. Fielder, J.H., EMB-M Mar-Apr 05 106-109 CMOS integrated circuits implantable biomimetic microelectronic systs. design. Wentai Liu, + , EMB-M Sep-Oct 05 66-74 Commerce; cf. Investment Compensation phase compensation in single echo acquisition imaging. McDougall, M.P., + , EMB-M Nov-Dec 05 17-22 Competitive intelligence filing patents. Klee, M.M., EMB-M May-Jun 05 128 Computer applications TalkAustin, medical communication service. Demir, S.S., EMB-M Jan-Feb 05 14-15, 19 Computer applications; cf. Handicapped aids Computer graphics vision and graphics. Coatrieux, J.L., EMB-M Jan-Feb 05 16-19 Control equipment; cf. Actuators Control theory; cf. Compensation; Predictive control Converters instantaneous, radial artery blood press., noninvasive meas. Tanaka, S., + , EMB-M Jul-Aug 05 32-37 Copyright filing patents. Klee, M.M., EMB-M May-Jun 05 128 Cybernetics bionic cardiovascular med. Sugimachi, M., + , EMB-M Jul-Aug 05 24-31 paralyzed limbs, functional reanimation. Loeb, G.E., + , EMB-M Sep-Oct 05 45-51 D Data security demand, WebMIA, med. data. Zhongfei Zhang, + , EMB-M MayJun 05 117-122 Data structures protein interactomics, data mining. Chen, J.Y., + , EMB-M May-Jun 05 95-102 Decoding bionic cardiovascular med. Sugimachi, M., + , EMB-M Jul-Aug 05 24-31 Design book review; Engineering Design (Eggert, R.; 2005). King, P.H., + , EMB-M Jan-Feb 05 97-98 Design engineering book review; Engineering Design (Eggert, R.; 2005). King, P.H., + , EMB-M Jan-Feb 05 97-98

JANUARY/FEBRUARY 2006

Detectors paralyzed limbs, functional reanimation. Loeb, G.E., + , EMB-M Sep-Oct 05 45-51 Diseases book review; Novel Vaccination Strategies (Kaufmann, S.H.E., Ed.; 2004). Madhavan, G., EMB-M Jan-Feb 05 98, 100 Entelos, predictive model systems for disease. Demir, S.S., EMB-M May-Jun 05 20-23 DNA drugs from sea, mol. prospecting. Gayler, K., + , EMB-M Mar-Apr 05 79-84 RNA antagonists, class of antisense drugs. Orum, H., EMB-M JulAug 05 81-87 Drugs pharmacists refuse to fill emergency contraception prescrip. Fielder, J.H., EMB-M Jul-Aug 05 88-91 Vioxx debacle. Fielder, J.H., EMB-M Mar-Apr 05 106-109 Dynamics; cf. Vibrations E Echo phase compensation in single echo acquisition imaging. McDougall, M.P., + , EMB-M Nov-Dec 05 17-22 Education; cf. Biomedical education; Engineering education Electric potential; cf. Bioelectric potentials Electrocardiography scientific study of Shirodhara, healing robot. Uebaba, K., + , EMBM Mar-Apr 05 69-78 stress and biofeedback relax., sympathetic nervous syst. activity. Shusterman, V., + , EMB-M Mar-Apr 05 52-57 Electrodes; cf. Microelectrodes Electroencephalography detecting evoked potentials, SVD- and ICA-based stat. models. Drozd, M., + , EMB-M Jan-Feb 05 51-58 scientific study of Shirodhara, healing robot. Uebaba, K., + , EMBM Mar-Apr 05 69-78 Electromyography myoelectric hand prosthesis, compliant grasp. Okuno, R., + , EMBM Jul-Aug 05 48-56 surface electrode-array EMG, decomp. algm. Garcia, G.A., + , EMB-M Jul-Aug 05 63-72 Electron microscopy; cf. Scanning electron microscopy Encapsulation neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 Encoding bionic cardiovascular med. Sugimachi, M., + , EMB-M Jul-Aug 05 24-31 Encoding; cf. Image coding Endoscopes easily miniaturized steerable endoscope. Breedveld, P., + , EMB-M Nov-Dec 05 40-47 Engineering; cf. Biomedical engineering; Design engineering; Engineering education Engineering education attending graduate school. Baura, G.D., EMB-M Jul-Aug 05 16-17 Entropy automatic detect. of health conds., algm. Nambu, M., + , EMB-M Jul-Aug 05 38-42 Error analysis book review; Ten Questions About Human Error, A New View of Human Factors and System Safety (Dekker, S.W.A.; 2005). King, P., EMB-M May-Jun 05 18 Ethical aspects pharmacists refuse to fill emergency contraception prescrip. Fielder, J.H., EMB-M Jul-Aug 05 88-91

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

F Farming; cf. Aquaculture Feedback bone, A-mode ultrasound, user-interactive registration. Heger, S., + , EMB-M Mar-Apr 05 85-95 stress and biofeedback relax., sympathetic nervous syst. activity. Shusterman, V., + , EMB-M Mar-Apr 05 52-57 File organization; cf. Data structures Filters surface electrode-array EMG, decomp. algm. Garcia, G.A., + , EMB-M Jul-Aug 05 63-72 Finance; cf. Investment G Genetics drugs from sea, mol. prospecting. Gayler, K., + , EMB-M Mar-Apr 05 79-84 functional proteomics, biolinguistic methods. Singh, G.B., + , EMBM May-Jun 05 73-80 proteomics and proteoinformatics, key challenges. Hamady, M., + , EMB-M May-Jun 05 34-40 string selection and comp. problems, genomics, optim. techs. Meneses, C.N., + , EMB-M May-Jun 05 81-87 Government policies technology, policy, and funding. Kun, L.G., EMB-M May-Jun 05 129 H Handicapped aids filing patents. Klee, M.M., EMB-M May-Jun 05 128 robotic vehicle for disabled children. Ceres, R., + , EMB-M Nov-Dec 05 55-63 TalkAustin, medical communication service. Demir, S.S., EMB-M Jan-Feb 05 14-15, 19 Health care book review; PACS and Imaging Informatics; Basic Principles and Applications (Huang, H.K., ed.; 2004). Demiris, G., EMB-M JulAug 05 92 book review; Sensors Applications, Vol. 3. Sensors in Medicine and Health Care (Oberg, P. et al., Eds.; 2004). King, P.J., EMB-M Mar-Apr 05 123 Hemodynamics; cf. Blood pressure measurement Human factors book review; Ten Questions About Human Error, A New View of Human Factors and System Safety (Dekker, S.W.A.; 2005). King, P., EMB-M May-Jun 05 18 I Image coding look at. shape and fn. from motion, med. imaging, Part I. Coatrieux, J.L., EMB-M May-Jun 05 29-31 Image processing; cf. Image coding; Image reconstruction; Image registration; Image segmentation; Medical image processing Image reconstruction automatic detect. of health conds., algm. Nambu, M., + , EMB-M Jul-Aug 05 38-42 poly-?-caprolactone tissue scaffolds, free-form fab. and micro-CT charactn. Darling, A.L., + , EMB-M Jan-Feb 05 78-83 Image registration bone, A-mode ultrasound, user-interactive registration. Heger, S., + , EMB-M Mar-Apr 05 85-95 Image segmentation look at. shape and fn. from motion, med. imaging, Part I. Coatrieux, J.L., EMB-M May-Jun 05 29-31

JANUARY/FEBRUARY 2006 121

Annual Index (continued)

Image sensors computer vision and graphics. Coatrieux, J.L., EMB-M Jan-Feb 05 16-19 Image sequence analysis look at. shape and fn. from motion, med. imaging, Part I. Coatrieux, J.L., EMB-M May-Jun 05 29-31 Image texture analysis architectural distortion, mammograms, charactn. Ayres, F.J., + , EMB-M Jan-Feb 05 59-67 Imaging; cf. Biomedical imaging; Magnetic resonance imaging; Microscopy Immunity testing book review; Biomedical Platforms - Realigning the Normal and the Pathological in Late-Twentieth-Century Medicine (Keating, P. and Cambrosio, A.; 2003). Madhavan, G., EMB-M May-Jun 05 19 book review; Novel Vaccination Strategies (Kaufmann, S.H.E., Ed.; 2004). Madhavan, G., EMB-M Jan-Feb 05 98, 100 Industrial property close questions of claim interpretation. Klee, M.M., EMB-M NovDec 05 64 Industrial property; cf. Copyright; Patents Industries; cf. Pharmaceutical industry Information networks; cf. Internet Information retrieval systems book review; PACS and Imaging Informatics; Basic Principles and Applications (Huang, H.K., ed.; 2004). Demiris, G., EMB-M JulAug 05 92 Information science; cf. Information retrieval systems Information technology technology, policy, and funding. Kun, L.G., EMB-M May-Jun 05 129 Information theory; cf. Decoding; Encoding; Entropy Infrared spectroscopy hemoglobin for near-IR spectrosc. of tissue, extinction coeffs. Kim, J.G., + , EMB-M Mar-Apr 05 118-121 Instruments; cf. Endoscopes; Sensors Integrated circuits; cf. Monolithic integrated circuits Internet demand, WebMIA, med. data. Zhongfei Zhang, + , EMB-M MayJun 05 117-122 Interviews Entelos, predictive model systems for disease. Demir, S.S., EMB-M May-Jun 05 20-23 Investment research and development expenditures. Baura, G., EMB-M MarApr 05 15-16 L Laxminarayan, Swamy N. obituaries. Foster, K.R., + , EMB-M Nov-Dec 05 74 Learning (artificial intelligence); cf. Unsupervised learning Learning systems proteins, predicting secondary structs. Blazewicz, J., + , EMB-M May-Jun 05 88-94 recording brain activity wirelessly. Irazoqui, P.P., + , EMB-M NovDec 05 48-54 Lithography; cf. Photolithography M Machine vision look at. shape and fn. from motion, med. imaging, Part I. Coatrieux, J.L., EMB-M May-Jun 05 29-31 vision and graphics. Coatrieux, J.L., EMB-M Jan-Feb 05 16-19 Macromolecules; cf. DNA

122 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Magnetic resonance imaging phase compensation in single echo acquisition imaging. McDougall, M.P., + , EMB-M Nov-Dec 05 17-22 Magnetic resonance imaging; cf. Biomedical MRI Magnetic tape recording retrospectroscope, Science, technol. and researcher. Geddes, L.A., EMB-M May-Jun 05 123-124 Magnetoencephalography open mag. and elec. graphic anal. Muller, H.-P., + , EMB-M MayJun 05 109-116 Management; cf. Risk management Materials; cf. Biomedical materials Matrix decomposition; cf. Singular value decomposition Mechanical factors understand acupuncture, ultrasound. Konofagou, E.E., + , EMB-M Mar-Apr 05 41-46 Mechanics; cf. Biomechanics Mechatronics bipolar coagulation, capable microforceps. Kawai, T., + , EMB-M Jul-Aug 05 57-62 Medical diagnosis drying drops, informative-capacity phenom. Yakhno, T.A., + , EMBM Mar-Apr 05 96-104 proteomics, data mining, quantitat. anal. Chia-Yu Yen, + , EMB-M May-Jun 05 67-72 stethoscope, birth. Geddes, L.A., EMB-M Jan-Feb 05 84-86 stress and biofeedback relax., sympathetic nervous syst. activity. Shusterman, V., + , EMB-M Mar-Apr 05 52-57 Medical diagnostic computing; cf. Medical image processing Medical image processing easily miniaturized steerable endoscope. Breedveld, P., + , EMB-M Nov-Dec 05 40-47 Medical information systems; cf. PACS Medical robots robotic vehicle for disabled children. Ceres, R., + , EMB-M Nov-Dec 05 55-63 Medical services home rehabilitation, unconstrained monitoring syst. Masuda, Y., + , EMB-M Jul-Aug 05 43-47 integrat. healthcare enterprise, challenge. Grimes, S.L., EMB-M Mar-Apr 05 122-124 senior design courses, teaching entrepreneurship. Goldberg, J., EMB-M Mar-Apr 05 17-18 technol. age, complementary and alternative med. Kern, S.E., + , EMB-M Mar-Apr 05 28-29 Medical signal processing; cf. Medical image processing Medical treatment 3D culturing of cardiomyocytes, electro-tensile bioreactor. Zhonggang Feng, + , EMB-M Jul-Aug 05 73-79 bionic cardiovascular med. Sugimachi, M., + , EMB-M Jul-Aug 05 24-31 bridging conventional med. and complementary and alternative med. Atsumi, K., + , EMB-M Mar-Apr 05 30-34 cardiopulmonary resuscitation develop. Geddes, L.A., EMB-M MarApr 05 110-113 cellular/tissue engng., Cardiomyoplasty. Pedrotty, D.M., + , EMB-M May-Jun 05 125-127 fMRI, basic expts., acupuncture. Ueda, Y., + , EMB-M Mar-Apr 05 47-51 mil. antishock trousers. Kemeny, A., + , EMB-M Jul-Aug 05 8091 scientific study of Shirodhara, healing robot. Uebaba, K., + , EMBM Mar-Apr 05 69-78 technol. age, complementary and alternative med. Kern, S.E., + , EMB-M Mar-Apr 05 28-29 TMS, primary motor cortex and assoc. spinal systs., impact. ValeroCabre, A., + , EMB-M Jan-Feb 05 29-35

JANUARY/FEBRUARY 2006

treatment of lymphedema, massage therapy. Bernas, M., + , EMB-M Mar-Apr 05 58-68 Medicine; cf. Diseases; Obstetrics; Pediatrics Microelectrodes biomimetic retinal stimulating array. Weiland, J.D., + , EMB-M SepOct 05 14-21 neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 retinal prostheses, microelectronic packaging. Rodger, D.C., + , EMB-M Sep-Oct 05 52-57 Microelectromechanical devices neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 retinal prostheses, microelectronic packaging. Rodger, D.C., + , EMB-M Sep-Oct 05 52-57 Microscopy 3D culturing of cardiomyocytes, electro-tensile bioreactor. Zhonggang Feng, + , EMB-M Jul-Aug 05 73-79 Mobile communication recording brain activity wirelessly. Irazoqui, P.P., + , EMB-M NovDec 05 48-54 Modeling Entelos, predictive model systems for disease. Demir, S.S., EMB-M May-Jun 05 20-23 Monolithic integrated circuits neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 MOS integrated circuits; cf. CMOS integrated circuits Motion analysis look at. shape and fn. from motion, med. imaging, Part I. Coatrieux, J.L., EMB-M May-Jun 05 29-31 Muscle; cf. Electromyography; Neuromuscular stimulation Muscles afferent input, motor organization, health and disease, role. Rothwell, J.C., + , EMB-M Jan-Feb 05 40-44 cross education and human CNS. Hortobagyi, T., EMB-M Jan-Feb 05 22-28 gastrointestinal endoscopy, imaging pill. Panescu, D., EMB-M JulAug 05 12-14 TMS, primary motor cortex and assoc. spinal systs., impact. ValeroCabre, A., + , EMB-M Jan-Feb 05 29-35 N Nanotechnology book review; Introduction to Nanotechnology (Poole, C.P. and Ownes, F.J.; 2003). Giorgio, T.D., EMB-M Mar-Apr 05 123124 retrospectroscope, Science, technol. and researcher. Geddes, L.A., EMB-M May-Jun 05 123-124 Nervous system afferent input, motor organization, health and disease, role. Rothwell, J.C., + , EMB-M Jan-Feb 05 40-44 anal. of event-related potentials, dyn. time warping. Casarotto, S., + , EMB-M Jan-Feb 05 68-77 bionic cardiovascular med. Sugimachi, M., + , EMB-M Jul-Aug 05 24-31 bipolar coagulation, capable microforceps. Kawai, T., + , EMB-M Jul-Aug 05 57-62 cross education and human CNS. Hortobagyi, T., EMB-M Jan-Feb 05 22-28 fMRI, basic expts., acupuncture. Ueda, Y., + , EMB-M Mar-Apr 05 47-51

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

human motor cortex, health and disease, use-depend. plasticity. Sawaki, L., EMB-M Jan-Feb 05 36-39 Japan, biomedical engng., forefront. Tamura, T., EMB-M Jul-Aug 05 23 neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 open mag. and elec. graphic anal. Muller, H.-P., + , EMB-M MayJun 05 109-116 real and sham acupuncture, fMRI. Parrish, T.B., + , EMB-M MarApr 05 35-40 scientific study of Shirodhara, healing robot. Uebaba, K., + , EMBM Mar-Apr 05 69-78 stress and biofeedback relax., sympathetic nervous syst. activity. Shusterman, V., + , EMB-M Mar-Apr 05 52-57 TMS, primary motor cortex and assoc. spinal systs., impact. ValeroCabre, A., + , EMB-M Jan-Feb 05 29-35 transcranial mag. stimulation. Hortobagyi, T., + , EMB-M Jan-Feb 05 20-21 Networks (circuits); cf. Filters; Printed circuits Neural nets biocompatible parylene neurocages. Tooker, A., + , EMB-M NovDec 05 30-33 Neural network hardware restoring lost cognitive fn. Berger, T.W., + , EMB-M Sep-Oct 05 30-44 Neuromuscular stimulation vagus nerve stimulation for the treatment of depression . Panescu, D., EMB-M Nov-Dec 05 68-72 Neurophysiology biomimetic systems (special section). EMB-M Sep-Oct 05 12-91 biomimetic systems (special section intro.). Weiland, J.D., + , EMBM Sep-Oct 05 12-13 integrated potentiostat for neurotransmitter sensing. Murari, K., + , EMB-M Nov-Dec 05 23-29 vagus nerve stimulation for the treatment of depression . Panescu, D., EMB-M Nov-Dec 05 68-72 Neurophysiology; cf. Neuromuscular stimulation Numerical analysis; cf. Error analysis O Obituaries Ernest Amatniek. Schoenfeld, R.L., EMB-M Sep-Oct 05 6 Herman Paul Schwan. EMB-M May-Jun 05 15 Swamy N. Laxminarayan. Foster, K.R., + , EMB-M Nov-Dec 05 74 Obstetrics biofidelic birthing simulator. Kim, E.J., + , EMB-M Nov-Dec 05 34-39 pharmacists refuse to fill emergency contraception prescrip. Fielder, J.H., EMB-M Jul-Aug 05 88-91 Office automation; cf. Records management Optimization methods string selection and comp. problems, genomics, optim. techs. Meneses, C.N., + , EMB-M May-Jun 05 81-87 Organic compounds; cf. DNA; Proteins Orthotics Japan, biomedical engng., forefront. Tamura, T., EMB-M JulAug 05 23 P Packaging retinal prostheses, microelectronic packaging. Rodger, D.C., + , EMB-M Sep-Oct 05 52-57 PACS book review; PACS and Imaging Informatics; Basic Principles and Applications (Huang, H.K., ed.; 2004). Demiris, G., EMB-M JulAug 05 92

JANUARY/FEBRUARY 2006 123

Annual Index (continued)

Patents close questions of claim interpretation. Klee, M.M., EMB-M NovDec 05 64 filing patents. Klee, M.M., EMB-M May-Jun 05 128 reasonable royalty. Klee, M.M., EMB-M Jan-Feb 05 96 Patient diagnosis; cf. Biomedical imaging Patient rehabilitation robotic vehicle for disabled children. Ceres, R., + , EMB-M Nov-Dec 05 55-63 Patient treatment vagus nerve stimulation for the treatment of depression . Panescu, D., EMB-M Nov-Dec 05 68-72 Patient treatment; cf. Neuromuscular stimulation; Surgery Pediatrics robotic vehicle for disabled children. Ceres, R., + , EMB-M Nov-Dec 05 55-63 Pharmaceutical industry Entelos, predictive model systems for disease. Demir, S.S., EMB-M May-Jun 05 20-23 filing patents. Klee, M.M., EMB-M May-Jun 05 128 Photoelectric devices; cf. Image sensors Photolithography retinal prostheses, microelectronic packaging. Rodger, D.C., + , EMB-M Sep-Oct 05 52-57 Physiological models; cf. Brain models Physiology; cf. Neurophysiology Picture archiving and communication systems demand, WebMIA, med. data. Zhongfei Zhang, + , EMB-M MayJun 05 117-122 integrat. healthcare enterprise, challenge. Grimes, S.L., EMB-M Mar-Apr 05 122-124 Position control myoelectric hand prosthesis, compliant grasp. Okuno, R., + , EMBM Jul-Aug 05 48-56 Predictive control Entelos, predictive model systems for disease. Demir, S.S., EMB-M May-Jun 05 20-23 Pressure measurement; cf. Blood pressure measurement Printed circuits neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 Professional aspects engineering ethics. Baura, G., EMB-M Nov-Dec 05 12, 14 Prosthetics bioimplants, thermal effects. Lazzi, G., EMB-M Sep-Oct 05 75-81 biomimetic retinal stimulating array. Weiland, J.D., + , EMB-M SepOct 05 14-21 biomimetic systems (special section). EMB-M Sep-Oct 05 12-91 biomimetic systems (special section intro.). Weiland, J.D., + , EMBM Sep-Oct 05 12-13 implantable biomimetic microelectronic systs. design. Wentai Liu, + , EMB-M Sep-Oct 05 66-74 Japan, biomedical engng., forefront. Tamura, T., EMB-M JulAug 05 23 myoelectric hand prosthesis, compliant grasp. Okuno, R., + , EMBM Jul-Aug 05 48-56 neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 paralyzed limbs, functional reanimation. Loeb, G.E., + , EMB-M Sep-Oct 05 45-51 restoring lost cognitive fn. Berger, T.W., + , EMB-M Sep-Oct 05 30-44 retinal prostheses, microelectronic packaging. Rodger, D.C., + , EMB-M Sep-Oct 05 52-57 Proteins 3D culturing of cardiomyocytes, electro-tensile bioreactor.

124 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Zhonggang Feng, + , EMB-M Jul-Aug 05 73-79 enabling proteomics discovery, visual anal. Havre, S.L., + , EMB-M May-Jun 05 50-57 functional proteomics, biolinguistic methods. Singh, G.B., + , EMBM May-Jun 05 73-80 hemoglobin for near-IR spectrosc. of tissue, extinction coeffs. Kim, J.G., + , EMB-M Mar-Apr 05 118-121 mining, protein-protein interacts. Mamitsuka, H., EMB-M May-Jun 05 103-108 predicting secondary structs. Blazewicz, J., + , EMB-M May-Jun 05 88-94 protein interactomics, data mining. Chen, J.Y., + , EMB-M May-Jun 05 95-102 proteins and their shape strings. Ison, R.E., + , EMB-M May-Jun 05 41-49 protein struct. influence trypsin miscleavage. Hamady, M., + , EMBM May-Jun 05 58-66 proteomics and proteoinformatics, key challenges. Hamady, M., + , EMB-M May-Jun 05 34-40 proteomics, data mining, quantitat. anal. Chia-Yu Yen, + , EMB-M May-Jun 05 67-72 Protocols; cf. Transport protocols R Records management book review; PACS and Imaging Informatics; Basic Principles and Applications (Huang, H.K., ed.; 2004). Demiris, G., EMB-M JulAug 05 92 Regulation FDA’s annual report on medical devices. Bartoo, G., EMB-M JanFeb 05 99-100 Research and development Entelos, predictive model systems for disease. Demir, S.S., EMB-M May-Jun 05 20-23 research and development expenditures. Baura, G., EMB-M MarApr 05 15-16 Respiratory system cardiopulmonary resuscitation develop. Geddes, L.A., EMB-M MarApr 05 110-113 home rehabilitation, unconstrained monitoring syst. Masuda, Y., + , EMB-M Jul-Aug 05 43-47 scientific study of Shirodhara, healing robot. Uebaba, K., + , EMBM Mar-Apr 05 69-78 stethoscope, birth. Geddes, L.A., EMB-M Jan-Feb 05 84-86 Reviews biomimetic retinal stimulating array. Weiland, J.D., + , EMB-M SepOct 05 14-21 proteomics and proteoinformatics, key challenges. Hamady, M., + , EMB-M May-Jun 05 34-40 Risk analysis book review; Risk Assessment: Basics and Benchmarks (Main, B., Ed.; 2004). King, P., EMB-M Jan-Feb 05 100 Risk management book review; Risk Assessment: Basics and Benchmarks (Main, B., Ed.; 2004). King, P., EMB-M Jan-Feb 05 100 Robots robotic vehicle for disabled children. Ceres, R., + , EMB-M Nov-Dec 05 55-63 Rubber; cf. Silicone rubber S Safety book review; Ten Questions About Human Error, A New View of Human Factors and System Safety (Dekker, S.W.A.; 2005). King, P., EMB-M May-Jun 05 18

JANUARY/FEBRUARY 2006

Scanning electron microscopy biocompatible parylene neurocages. Tooker, A., + , EMB-M NovDec 05 30-33 Scanning probe microscopy; cf. Atomic force microscopy Schwan, Herman Paul obituary. EMB-M May-Jun 05 15 Semiconductor device fabrication neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 Sensors book review; Sensors Applications, Vol. 3. Sensors in Medicine and Health Care (Oberg, P. et al., Eds.; 2004). King, P.J., EMB-M Mar-Apr 05 123 Sensors; cf. Image sensors Servomechanisms; cf. Servomotors Servomotors myoelectric hand prosthesis, compliant grasp. Okuno, R., + , EMBM Jul-Aug 05 48-56 Signal reconstruction; cf. Image reconstruction Silicon neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 Silicone insulation; cf. Silicone rubber Silicone rubber neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 Silicon on insulator technology neuroscience and neural prostheses, Si microsystems. Wise, K.D., EMB-M Sep-Oct 05 22-29 Simulation biofidelic birthing simulator. Kim, E.J., + , EMB-M Nov-Dec 05 34-39 Singular value decomposition detecting evoked potentials, SVD- and ICA-based stat. models. Drozd, M., + , EMB-M Jan-Feb 05 51-58 Skin stress and biofeedback relax., sympathetic nervous syst. activity. Shusterman, V., + , EMB-M Mar-Apr 05 52-57 Spatial variables control; cf. Position control Special issues and sections biomimetic systems (special section). EMB-M Sep-Oct 05 12-91 biomimetic systems (special section intro.). Weiland, J.D., + , EMBM Sep-Oct 05 12-13 TalkAustin, medical communication service. Demir, S.S., EMB-M Jan-Feb 05 14-15, 19 Spectroscopy; cf. Infrared spectroscopy SQUIDS easily miniaturized steerable endoscope. Breedveld, P., + , EMB-M Nov-Dec 05 40-47 Surgery bipolar coagulation, capable microforceps. Kawai, T., + , EMB-M Jul-Aug 05 57-62 bone, A-mode ultrasound, user-interactive registration. Heger, S., + , EMB-M Mar-Apr 05 85-95 cardiac resynchronization therapy. Panescu, D., EMB-M Mar-Apr 05 22-26 isthmus ablation, microwave catheter, dogs. Karolyi, L., + , EMB-M Jan-Feb 05 45-50

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

Japan, biomedical engng., forefront. Tamura, T., EMB-M JulAug 05 23 T Telecommunication; cf. Biomedical communication; Mobile communication Telemetry; cf. Biomedical telemetry Terrorism book review; Novel Vaccination Strategies (Kaufmann, S.H.E., Ed.; 2004). Madhavan, G., EMB-M Jan-Feb 05 98, 100 Testing; cf. Immunity testing Thermodynamic properties; cf. Entropy Training biofidelic birthing simulator. Kim, E.J., + , EMB-M Nov-Dec 05 3439 Transmitters integrated potentiostat for neurotransmitter sensing. Murari, K., + , EMB-M Nov-Dec 05 23-29 Transportation book review; Ten Questions About Human Error, A New View of Human Factors and System Safety (Dekker, S.W.A.; 2005). King, P., EMB-M May-Jun 05 18 Transport protocols home rehabilitation, unconstrained monitoring syst. Masuda, Y., + , EMB-M Jul-Aug 05 43-47 U Unsupervised learning mining, protein-protein interacts. Mamitsuka, H., EMB-M May-Jun 05 103-108 V Vehicles robotic vehicle for disabled children. Ceres, R., + , EMB-M Nov-Dec 05 55-63 Vibrations afferent input, motor organization, health and disease, role. Rothwell, J.C., + , EMB-M Jan-Feb 05 40-44 Visual databases; cf. PACS Visualization enabling proteomics discovery, visual anal. Havre, S.L., + , EMB-M May-Jun 05 50-57 protein interactomics, data mining. Chen, J.Y., + , EMB-M May-Jun 05 95-102 Visual system bioimplants, thermal effects. Lazzi, G., EMB-M Sep-Oct 05 75-81 biomimetic retinal stimulating array. Weiland, J.D., + , EMB-M SepOct 05 14-21 implantable biomimetic microelectronic systs. design. Wentai Liu, + , EMB-M Sep-Oct 05 66-74 neural prostheses, implantable biomedical microsystems. Stieglitz, T., + , EMB-M Sep-Oct 05 58-65 retinal prostheses, microelectronic packaging. Rodger, D.C., + , EMB-M Sep-Oct 05 52-57

JANUARY/FEBRUARY 2006 125

2006 IEEE MEMBERSHIP APPLICATION Start your membership immediately: Join online www.ieee.org/join Please complete both sides of this form, typing or printing in capital letters. Use only English characters and abbreviate only if more than 40 characters and spaces per line. We regret that incomplete applications cannot be processed.

Please PRINT your name as you want it to appear on your membership card and IEEE correspondence. As a key identifier for the IEEE database, circle your last/surname. Date of birth (Day/Month/Year)

Middle

/

Last/Family/Surname

Home Address

Street Address City

State/Province

17. 18.

Postal Code

Country

Home Phone

Home Email

19. 20. 21. 22. 23. 24.

Which of the following best describes the area of your profession? Please check one: ■ engineering ■ computer sciences and information technology ■ biological and medical sciences ■ mathematics ■ technical communications, education, management, law & policy ■ physical sciences ■ other

▼

Business Address

Company Name

Department/Division

Title/Position

Years in Position

Years in Profession

Street Address

City

State/Province

25. 26. 27. 28.

1. 2. 3. 4.

6.

Country

7.

Office Phone Send all IEEE mail:

Office Fax ■ Home Address

Office Email

Education Information

Information about your education is vital to member grade assignment. You are encouraged to attach a resumé to provide additional details. No abbreviations please.

A.

First Professional Degree Received

Program/Course of Study

Which of the following best describes the area of your degree? Please check one: ■ engineering ■ computer sciences and information technology ■ biological and medical sciences ■ mathematics ■ technical communications, education, management, law & policy ■ physical sciences ■ other

B.

College/University

Campus

State/Province

Country

Highest Technical Degree Received

State/Province

General and corporate management Engineering management Project engineering management Research and development management Design engineering management - analog Design engineering management - digital Research and development engineering Design/development engineering - analog

9. Design/development engineering - digital 10. Hardware engineering 11. Software design/development 12. Computer science 13. Science/physics/mathematics 14. Engineering (not elsewhere specified) 15. Marketing/sales/purchasing 16. Consulting 17. Education/teaching 18. Retired 19. Other _______________________

C. Principal responsibility 1. 2. 3. 4. 5.

Engineering and scientific management Management other than engineering Engineering design Engineering Software: science/mngmnt/engineering

6. 7. 8. 9.

Education/teaching Consulting Retired Other_______________________

D. Title 1. 2. 3. 4. 5. 6. 7. 8. 9.

Chairman of the Board/President/CEO Owner/Partner General Manager VP Operations VP Engineering/Dir. Engineering Chief Engineer/Chief Scientist Engineering Management Scientific Management Member of Technical Staff

10. 11. 12. 13. 14. 15. 16. 17. 18.

Design Engineering Manager Design Engineer Hardware Engineer Software Engineer Computer Scientist Dean/Professor/Instructor Consultant Retired Other _______________________

Are you now or were you ever a member of the IEEE? ■ Yes ■ No If yes, provide, if known:

Mo/Yr Received Program/Course of Study

Which of the following best describes the area of your degree? Please check one: ■ engineering ■ computer sciences and information technology ■ biological and medical sciences ■ mathematics ■ technical communications, education, management, law & policy ■ physical sciences ■ other

College/University

8.

■ Business Address

To better serve our members and supplement member dues, your postal mailing address is made available to carefully selected organizations, to provide you with information on technical services, continuing education, and conferences. Your email address is not rented by IEEE. Please check box only if you do not want to receive these postal mailings. ■

2

▼

B. Principal job function

5.

Postal Code

Computers Computer peripheral equipment Software Office and business machines Test, measurement and instrumentation equipment Communications systems and equipment Navigation and guidance systems and equipment Consumer electronics/appliances Industrial equipment, controls and systems ICs and microprocessors Semiconductors, components, sub-assemblies, materials and supplies Aircraft, missiles, space and ground support equipment Oceanography and support equipment Medical electronic equipment OEM incorporating electronics in their end product (not elsewhere classified) Independent and university research, test and design laboratories and consultants (not connected with a mfg. co.) Government agencies and armed forces Companies using and/or incorporating any electronic products in their manufacturing, processing, research or development activities Telecommunications services, telephone (including) cellular Broadcast services (TV, cable, radio) Transportation services (airline, railroad, etc.) Computer and communications and data processing services Power production, generation, transmission and distribution Other commercial users of electrical, electronic equipment and services (not elsewhere classified) Distributor (reseller, wholesaler, retailer) University, college/other educational institutions, libraries Retired Other__________________________

▼

First/Given Name

/

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

▼

▼

Select the numbered option that best describes yourself. This information is used by IEEE magazines to verify their annual circulation. Please enter numbered selections in the boxes provided.

▼

Title

Female ■

Please Tell Us About Yourself

A. Primary line of business

INFORMATION Name & Contact Information 1 PERSONAL

Male ■

3

Membership Number

4

Grade

Year Expired

Please Sign Your Application

I hereby apply for IEEE membership and agree to be governed by the IEEE Constitution, Bylaws and Code of Ethics. I understand that IEEE will communicate with me regarding my individual membership and all related benefits. Application must be signed.

Campus Country

Mo/Yr Received

■ PE/P.Eng (date/location)

Signature

Date Over Please

5

6

Add IEEE Society Memberships (Optional)

The 39 IEEE Societies support your technical and professional interests. Many society memberships include a personal subscription to the core journal, magazine or newsletter of that society. For a complete list of everything included with your IEEE Society membership visit www.ieee.org/join. Please check ✔ the appropriate box.

BETWEEN BETWEEN 16 AUG 2005- 1 MAR 200628 FEB 2006 15 AUG 2006 PAY PAY

2006 IEEE Membership Rates

IEEE member dues and regional assessments are based on where you live and when you apply. Membership is based on the calendar year from 1 January through 31 December. Please check ✔ the appropriate box.

BETWEEN 16 AUG 200528 FEB 2006

BETWEEN 1 MAR 200615 AUG 2006 PAY

PAY RESIDENCE United States ...............................................$156.00 ■ ..........$78.00 ■ Canada (includes GST)* ...............................$142.33 ■ ...........$71.17 ■ Canada (includes HST)* ...............................$151.85 ■ ...........$75.93 ■ Africa, Europe, Middle East..........................$130.00 ■ ...........$65.00 ■ Latin America ...............................................$123.00 ■ ...........$61.50 ■ Asia, Pacific..................................................$124.00 ■ ...........$62.00 ■

IEEE Aerospace and Electronic Systems a `

AES010

25.00 ■

13.00 ■

IEEE Antennas and Propagation a `

AP003

24.00 ■

12.00 ■

IEEE Broadcast Technology ~ a `

BT002

15.00 ■

8.00 ■

IEEE Circuits and Systems a

CAS004

18.00 ■

9.00 ■

IEEE Communications a `

C0M019

40.00 ■

20.00 ■

IEEE Components, Packaging, & Manu. Tech. ~

CPMT021

12.00 ■

6.00 ■

IEEE Computational Intelligence a `

CIS011

20.00 ■

10.00 ■

IEEE Computer a `

C016

46.00 ■

23.00 ■

IEEE Consumer Electronics ~ `

CE008

15.00 ■

8.00 ■

IEEE Control Systems a `

CS023

25.00 ■

13.00 ■

IEEE Dielectrics and Electrical Insulation a `

DEI032

20.00 ■

10.00 ■

IEEE Education ~ a `

E025

20.00 ■

10.00 ■

IEEE Electromagnetic Compatibility ~ `

EMC027

25.00 ■

13.00 ■

IEEE Electron Devices ~ `

ED015

11.00 ■

6.00 ■

IEEE Engineering in Medicine and Biology a

EMB018

35.00 ■

18.00 ■

IEEE Engineering Management ~ a `

EM014

30.00 ■

15.00 ■

IEEE Geoscience and Remote Sensing ~ `

GRS029

20.00 ■

10.00 ■

IEEE Industrial Electronics ~ `

IE013

10.00 ■

5.00 ■

IEEE Industry Applications a `

IA034

20.00 ■

10.00 ■

IEEE Information Theory ~ `

IT012

30.00 ■

15.00 ■

IEEE Instrumentation and Measurement a `

IM009

20.00 ■

10.00 ■

IEEE Intelligent Transportation Systems ~ `

ITSO38

25.00 ■

13.00 ■

IEEE Lasers and Electro-Optics ~ `

LE0O36

25.00 ■

13.00 ■

IEEE Magnetics ~ ` 

MAG033

20.00 ■

10.00 ■

IEEE Microwave Theory and Techniques a `

MTT017

14.00 ■

7.00 ■

IEEE Nuclear and Plasma Sciences ~

NPS005

20.00 ■

10.00 ■

IEEE Oceanic Engineering ~ a `

OE022

19.00 ■

10.00 ■

IEEE Power Electronics ~ `

PEL035

20.00 ■

10.00 ■

IEEE Power Engineering a `

PE031

30.00 ■

15.00 ■

Payment Method

IEEE Product Safety Engineering `

PSE043

35.00 ■

18.00 ■

IEEE Professional Communication ~ a `

PC026

30.00 ■

15.00 ■

IEEE Reliability ~ `

RL007

30.00 ■

15.00 ■

All prices are quoted in US dollars. You may pay for IEEE membership by credit card (see below), check or money order payable to IEEE, drawn on a US bank.

IEEE Robotics and Automation a `

RA024

21.00 ■

11.00 ■

IEEE Signal Processing a `

SP001

27.00 ■

14.00 ■

IEEE Social Implications of Technology a `

SIT030

24.00 ■

12.00 ■

IEEE Solid-State Circuits ~ `

SSC037

18.00 ■

9.00 ■

IEEE Systems, Man, & Cybernetics ~ `

SMC028

9.00 ■

5.00 ■

IEEE Ultrasonics, Ferroelectrics, & Frequency Control ~ ` UFFC020

20.00 ■

10.00 ■

IEEE Vehicular Technology a `

18.00 ■

9.00 ■

VT006

*IEEE Canada Business No. 125634188

Minimum Income or Unemployed Provision Applicants who certify that their prior year income did not exceed US$11,900 (or equivalent) or were not employed are granted 50% reduction in: full year dues, regional assessment and fees for one IEEE Society. If applicable, please check appropriate box and adjust payment accordingly. Student members are not eligible. ■ I certify I earned less than US$11,900 in 2004 ■ I certify that I was unemployed in 2004

7

More Recommended Options

Proceedings of the IEEE ............ print $29.00 ■ or online $29.00 ■ Proceedings of the IEEE (print/online combination) .............$37.00 ■ IEEE Standards Association (IEEE-SA).................................$36.00 ■ IEEE Women in Engineering (WIE) .......................................$25.00 ■

8

Payment Amount

Please total the Membership dues, Society dues and other amounts from this page: IEEE Membership dues 6 .............................................$ IEEE Society dues (optional) 5 .....................................$ IEEE-SA/WIE dues (optional) 7 .....................................$ Proceedings of the IEEE (optional) 7 .............................$

______ ______ ______ ______

Canadian residents pay 7% GST or 15% HST Reg no. 125634188 on Society payments & publications only

................TAX $ ______ AMOUNT PAID ..........................................................TOTAL $ ______

■ Check

■

i

■

[

■

■

w

Credit Card Number

MONTH YEAR EXPIRATION DATE

CARDHOLDER’S 5-DIGIT ZIPCODE (BILLING STATEMENT ADDRESS) USA ONLY

Legend – Society membership includes: a One or more Society publication(s) ~ Society newsletter

` Online access to publication(s)  CD-ROM

Name as it appears on card

Signature

Complete both sides of this form, sign and return to: IEEE Admission and Advancement 445 Hoes Lane, PO Box 6804, Piscataway, NJ 08855-6804 USA or fax to +1 732 981 0225 or join online at www.ieee.org/join

9

Were You Referred to IEEE?

■ Yes ■ No If yes, provide the following: Member Recruiter Name___________________________________ IEEE Recruiter’s Member Number (Required) ______________________

TRACKING CODE

Please reprint your full name here

PROMO CODE

ADVERTISERS’ INDEX The Advertisers’ Index contained in this issue is compiled as a service to our readers and advertisers: the publisher is not liable for errors or omissions although every effort is made to ensure its accuracy. Be sure to let our advertisers know you found them through IEEE Engineering in Medicine and Biology Magazine.

Advertiser

Page

URL

Phone

Ad Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 . . . . . . . . . . .www.adinstruments.com . . . . . . . . . . . . . . . .888-965-6040 Biopac Systems,Inc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .CVR 4 . . . . . . . . . . .www.biopac.com . . . . . . . . . . . . . . . . . . . . . .805-685-0066 IEEE Marketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .CVR3 . . . . . . . . . . .www.ieee.org/ieeemdl . . . . . . . . . . . . . . . . . .732-981-0060 John Wiley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 . . . . . . . . . . .www.wiley.com/ieee . . . . . . . . . . . . . . . . . . .877-762-2974 MathWorks,Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .CVR2 . . . . . . . . . . .www.mathworks.com . . . . . . . . . . . . . . . . . .508-647-7040 Recruitment Harvard Medical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .112 Medtronic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .111 University of Connecticut . . . . . . . . . . . . . . . . . . . . . . . . . . . .112

Coming in March Functional Magnetic Resonance Imaging Call your local advertising sales representative today! Space reservations: Material deadline:

2/2 2/8

Susan E. Schneiderman Business Development Manager [email protected]

+1 732 562 3946 Tel.; +1 732 981 1855 Fax.

ADVERTISING SALES OFFICES IEEE MAGAZINES 445 Hoes Lane, P. O. Box 1331 Piscataway NJ 08855-1331 www.ieee.org/publications/magazines James A. Vick Staff Director, Advertising Businesses +1 212 419 7767; Fax: +1 212 419 7589 [email protected] Susan E. Schneiderman Business Development Manager +1 732 562 3946; Fax: +1 732 981 1855 [email protected] Marion Delaney Advertising Sales Director +1 415 863 4717; Fax + 1 415 863 4717 [email protected] Product Advertising Midatlantic Lisa Rinaldo +1 732 772 0160; Fax: +1 732 772 0161 [email protected] NY, NJ, PA, DE, MD, DC, KY, WV Connecticut Stan Greenfield +1 203 938 2418; Fax: +1 203 938 3211 [email protected] CT New England/Eastern Canada Jody Estabrook +1 978 244 0192; Fax: +1 978 244 0103 [email protected] ME, VT, NH, MA, RI Canada: Nova Scotia, Prince Edward Island, Newfoundland, New Brunswick, Quebec

Southeast Bill Holland +1 770 435 6549; Fax: +1 770 435 0243 [email protected] VA, NC, SC, GA, FL, AL, MS, TN Midwest/Central Canada Dave Jones +1 708 442 5633 Fax: +1 708 442 7620 [email protected] IL, IA, KS, MN, MO, NE, ND, SD, WI, Canada: Manitoba, Saskatchewan Ohio Joe DiNardo +1 440 248 2456 Fax: +1 440 248 2594 [email protected] OH Midwest/Ontario, Canada Will Hamilton +1 269 381 2156 Fax: +1 269 381 2556 [email protected] IN, MI Canada: Ontario Southwest Josh Mayer +1 972 423 5507 Fax: +1 972 423 6858 [email protected] AR, LA, TX, OK Southern California/ Mountain States Marshall Rubin +1 818 888 2407 Fax: +1 818 888 4907 [email protected] AZ, CO,HI, NM, NV,UT, CA 93400 & below

Northern California/ Northwest/Western Canada Peter D. Scott +1 415 421 7950 Fax: +1 415 398 4156 [email protected] AK, ID, MT, WY, OR, WA, CA 93401 & above. Canada: British Columbia, Alberta Europe/Africa/Middle East Heleen Vodegel +44 1875 825 700; Fax: +44 1875 825 701 [email protected] Europe, Africa, Middle East Asia/Far East/Pacific Rim Susan Schneiderman +1 723 562 3946; Fax: +1 723 981 1855 [email protected] Asia, Far East, Pacific Rim, Australia, New Zealand Telephone Sales Manager John Restchack +1 212 419 7578; Fax: ++1 212 419 7589 [email protected] Recruitment Advertising Midatlantic Lisa Rinaldo +1 732 772 0160 Fax: +1 732 772 0161 [email protected] NY, NJ, CT, PA, DE, MD, DC, KY, WV

New England/Eastern Canada John Restchack +1 212 419 7578; Fax: ++1 212 419 7589 [email protected] ME, VT, NH, MA, RI Canada: Quebec Nova Scotia, Prince Edward Island, Newfoundland, New Brunswick Southeast Thomas Flynn +1 770 645 2944 Fax: +1 770 993 4423 [email protected] VA, NC, SC, GA, FL, AL, MS, TN Midwest/Texas/Central Canada Darcy Giovingo +1 847 498 4520 Fax: +1 847 498 5911 [email protected] AR, IL, IN, IA, KS, LA, MI, MN, MO, NE, ND, SD, OH, OK, TX, WI, Canada: Ontario, Manitoba Saskatchewan,Alberta West Coast/Southwest/ Mountain States/Far East Tim Matteson +1 310 836 4064; Fax: +1 310 836 4067 [email protected] AK, AZ, CA, CO, HI, ID, MT, NM, NV, OR, UT, WA, WY Canada: British Columbia Europe/Africa/Middle East Heleen Vodegel +44 1875 825 700; Fax: +44 1875 825 701 [email protected] Europe, Africa, Middle East

IEEE is Beth depends on IEEE publications to bring her organization the latest technology research. Not only do IEEE publications provide Beth with over 1 million online documents — including top-cited journals, cutting-edge conferences and vital standards — they do it for a reasonable price. Research shows that IEEE journals are just 58% of the average journal price in the field on a page-for-page basis. That’s why 81% of IEEE customers are satisfied or highly satisfied with the value of IEEE content. And why Beth has the reputation for spending her budget so wisely. To Beth, IEEE is value. Discover what IEEE can be for you.

Go here. www.ieee.org/discover

............................................. IEEE INFORMATION DRIVING INNOVATION