10.2.2 Education and Training

Education, either formal or informal, is essential for practitioners of one discipline to learn about another, and there are many different venues in which training for the BioComp interface may occur. (Contrast this to a standard program in physics, for example, in which a very typical career path involves an undergraduate major in physics, graduate education in physics culminating in a doctorate, and a postdoctoral appointment in physics.)

Reflecting this diversity, it is difficult to generalize about approaches toward academic training at the BioComp interface, since different departments and institutions approach it with varied strategies. One main difference in approaches is whether the initiative for creating an educational program and the oversight and administration of the program come from the computer science department or the biology department. Other differences include whether it is a stand-alone program or department, or a concentration or interdisciplinary program that requires a student or researcher to have a “home” department as well, and whether the program was established primarily as a research program for postdoctoral fellows and professors (and is slowly trickling down to undergraduate and graduate education), or as an undergraduate curriculum that is slowly building its way up to a research program. Those differences in origin result in varying emphases on what constitutes core subject matter, whether interdisciplinary work is encouraged and how it is handled, and how research is supported and evaluated.

What is clear is that this is an active area of development and investment, and many major colleges and universities have a formal educational program of some sort at the BioComp interface (generally in bioinformatics or computational biology) or are in the process of developing one. Of course, there is not yet widespread agreement on what the curriculum for this new course of study should be3 or indeed if there should be a single, standard, curriculum.

10.2.2.1 General Considerations

As a general rule, serious work at the BioComp interface requires knowledge of both biology and computing. For example, many models and simulations of biological phenomena are constrained by lack of quantitative data. The paucity of measurements of in vivo rates or parameters associated with dynamics means that it is difficult to understand systems from a dynamic, rather than a static, point of view. For example, to further the use of biological modeling and simulation, kinetics should be an important part of early biological courses, including biochemistry and molecular biology, to instill an appreciation in experimental biologists that kinetics is important. The requisite background in quantitative methods is likely to include some nontrivial exposure to continuous mathematics, nonlinear dynamics, linear algebra, probability and statistics, as well as computer programming and algorithm design.

From the engineering side, few nonbiologists get any exposure to biological laboratory research or develop an understanding of the collection and analysis of biological data. This also leads to unrealistic expectations of what can be done practically, how repeatable (or unrepeatable) a set of experiments can be, and how difficult it can be to understand the system in detail. Computer scientists also require exposure to probability, statistics, laboratory technique, and experimental design in order to understand the biologist’s empirical methodology. More fundamentally, nonbiologists working at the BioComp interface must have an understanding of the basic principles relevant to the biological problem domains of interest, such as physiology, phylogeny, or proteomics. (A broad perspective on biology, including some exposure to evolution, ecosystems, and metabolism, is certainly desirable, but is likely not absolutely necessary.)

Finally, it must be noted that many students choose to study biology because it is a science whose study has traditionally not involved mathematics to any significant extent. Similarly, W. Daniel Hillis

3  

R. Altman, “A Curriculum for Bioinformatics: The Time Is Ripe,” Bioinformatics 14(7):549-550, 1998.



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