directly by industry and research institutes whose problem-oriented programs utilize a broader range of approaches, including direct application of mathematical and computational techniques.
The workshop participants' assessment is that in the immediate future, this situation will not undergo a substantial change. Therefore, scientists expecting to enter the academic research world will continue to need a strong disciplinary grounding for their cross-disciplinary work. Employment opportunities in industry and research institutes appear to be stable, or growing slowly. Such centers will continue to be major sites for the development of computational techniques and applications in biology.
Because of their frequently strong mathematical and computational environments, and the less frequent presence of rigid departmental structures, one possible source of future growth for computational biology is the four-year college. Mathematical and computational approaches fit well within the research environments found in these institutions, and they are likely to find effective implementation in the teaching programs. In this context, faculty in these institutions may be expected to employ mathematical and computational techniques in both research and the development of teaching aids that will eventually find their way into research institutions. However, here again, strong disciplinary training will be essential as the basis for the research approach.
In the past, most of the migration of scientists into computational biology has been from disciplines outside of biology (e.g., math, physics, chemistry, computer science, etc.). Physicists become biologists, but not the reverse. This migration and its asymmetry have been prompted by successful application of domain-specific technology to solving biological problems.
Many early successes in computational biology were obtained by scientists who were primarily biologists with marginal skills in computer science and mathematics (programming skills and some algorithmics), while many others were the result of work by scientists with extensive mathematical and computational backgrounds. However, as the problems under investigation become more complex, training which provides great depth in quantitative analysis will be essential.
Current interest and excitement in computational and mathematical biology are driven in large part by neurobiology, global change, and genomics. In all of these areas, vast amounts of information are accumulating at a rate that precludes human absorption and, hence, understanding. Biology needs tools for manipulating and analyzing information. In order for training environments to be maximally effective, there must be a clear understanding of which professional profiles are suitable for current and future researchers in computational and mathematical biology.
The profiles which follow are dependent upon the nature of the position. Academicians tend to reside within traditional departmental units, whereas in industrial settings and research institutes, there is a wider range in the mixtures of disciplines in working groups. The following lists of specialties within computer science, mathematics, and biology are those in which there is substantial research activity today and where there is likely to remain some research focus in the future.
Most computer scientists retain their primary professional identification with computer science. They tend to view biological applications as a source of computer science problems. Biological applications are new to computer scientists, and the traditions across the interface are developing at a moderate pace. The tendency is to cross the line as a senior scientist by developing collaborations. There are some successful scientists in this field whose first exposure to biology was at the graduate level. Examples of the areas of computer science in which such collaborations take place are: