tential for simulations of systems a little too complicated for complete mathematical analysis, and thus is ideal for teaching simulation as a tool for understanding.

Many topics in biology interact with the engineering viewpoint in such a fashion.

Mathematics and Computer Science


Quantitative analysis, modeling, and prediction play increasingly significant day-to-day roles in today’s biomedical research. To prepare for this sea change in activities, biology majors headed for research careers need to be educated in a more quantitative manner, and such quantitative education may require the development of new types of courses. The committee recommends that all biology majors master the concepts listed below. In addition, the committee recommends that life science majors become sufficiently familiar with the elements of programming to carry out simulations of physiological, ecological, and evolutionary processes. They should be adept at using computers to acquire and process data, carry out statistical characterization of the data and perform statistical tests, and graphically display data in a variety of representations. Furthermore, students should also become skilled at using the Internet to carry out literature searches, locate published articles, and access major databases.

The elucidation of the sequence of the human genome has opened new vistas and has highlighted the increasing importance of mathematics and computer science in biology. The intense interest in genetic, metabolic, and neural networks reflects the need of biologists to view and understand the coordinated activities of large numbers of components of the complex systems underlying life. Biology students should be prepared to carry out in silico (computer) experiments to complement in vitro and in vivo experiments. It is essential that biology undergraduates become quantitatively literate. The concepts of rate of change, modeling, equilibria and stability, structure of a system, interactions among components, data and measurement, visualizing, and algorithms are among those most important to the curriculum. Every student should acquire the ability to analyze issues arising in these contexts in some depth, using analytical methods (e.g., pencil and paper), appropriate computational tools, or both. The course of study would include aspects of probability, statistics, discrete models, linear algebra, calculus and differential equations, modeling, and programming.

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