collaborators for whatever difficulties arise at the frontier. That is, specific computing-to-biology “tech transfer” of intellectual ideas will have some impact, but the greatest impact of computing on biology will come from an overall acceleration of the pace of progress.
To fulfill the promise of 21st century biology, research scientists from both computer and biological science need to work together more extensively, more often, and more closely than ever before. As quantitative methods are increasingly adopted within the biological sciences, it will be possible to answer a new range of scientific questions, not just to accelerate research progress. Uncovering the meaning implicit in the complete sequence of the human genome to deliver on the promises of the project for society is an obvious case.
A revitalized enterprise driven by this newly trained cadre of interdisciplinary scientists and maintained through a balance of individual investigator-initiated and group projects along with continued technology and computational advances, will be able (1) to address fundamental questions in biology such as the relationship of structure to function and the basis for homeostasis; (2) to integrate biological knowledge across the vast scales of time, space, and organizational complexity that characterize biology; (3) to translate basic biology to preventive, predictive, and personalized medicine and to extend biological knowledge to engineering soft materials and other industrial nanobiotechnology contributions; and (4) to uncover how biology can contribute to energy production and environmental restoration. The Committee on the Frontiers at the Interface of Computing and Biology believes that such a vision for 21st century biology is realistic, and that the implementation of its recommendations would ensure decades of exponential progress and a major transformation of our understanding of life.
On the other side of the interface, biological inspiration for new approaches to computing continues to be important, in the sense that biology provides existence proofs that information-processing technology based on biochemistry rather than on silicon electronics is possible. For areas of computing that are generally complex and unwieldy in the associated technologies available so far to address them, or areas lacking in empirical and/or theoretical knowledge, inspiration from whatever source is welcome—and biological inspiration is most likely to be valuable in these areas. (For other areas of computing, whose intellectual terrain is well explored and for which a solid base of empirical and theoretical knowledge is available, biological inspiration is both unnecessary and less interesting, because good and useful solutions are available without any kind of biological connection at all.)
Furthermore, computer scientists tend to be most interested in the general applicability of their work and are often less interested in work that is relevant to only one problem domain. Individuals from this perspective should thus understand the key difference between applications-driven research and applications-specific research. That is, problems in the life sciences can be important drivers of computer science research, and in many cases the knowledge developed in seeking solutions to these problems will be applicable in other domains.
Finally, it is worth noting one possible domain of symmetry between the two fields, although it is a symmetry of ignorance rather than one of knowledge. Both computing and biology provide objects of enormous complexity whose behavior is not well understood—consider the Internet and a cell. It may well turn out that studying each of these objects as systems can yield insights useful in understanding the other—and the same kinds of (yet-to-be-developed) formalism may apply to both—but the jury is still out on this possibility.
Apart from computing-enabled biology and biologically inspired computing, a number of other new areas of inquiry are also emerging at the BioComp interface, although in addition to biology and computing they draw from chemistry, materials science, bioengineering, and biochemistry. Some of these efforts can be characterized loosely as different flavors of biotechnology, and three of the most important are analytical biotechnology, materials biotechnology, and computational biotechnology.