cleanup, and more robust food production. Computing and information technology enable human beings to acquire, store, process, and interpret enormous amounts of information, and continue to underpin much of modern society.

Finally, several important areas of interaction between the two fields have already emerged, and there is every expectation that more will emerge in the future. Indeed, the belief of the committee that there are many more synergies at the interface between these two fields than have been exploited to date is the motivation for this report. Against this backdrop, it makes good sense to consider potential interactions between the two fields—what this report calls the “BioComp” interface.

As for the nature of computing that can usefully be exploited by life scientists, there is a range of possibilities. For some problems encountered by biology researchers, a very rudimentary knowledge of computing and information technology is quite sufficient. However, as problems become bigger and/or more complex, what one may pick up by hacking and reading manuals is no longer sufficient. To address such problems, the kinds and levels of expertise needed are more likely to require significant formal study of computer science (e.g., as an undergraduate major in the field). And for still more difficult, larger, or more complex problems, the kinds and levels of expertise needed stretch the current state of knowledge of the field—a point that illuminates the importance of real computer science research in a biological context.

Nor is the utility of computing limited to providing tools or models—no matter how sophisticated—for biologists to use. As discussed in Chapter 6, computing can also provide intellectual abstractions that may provide insight into biological phenomena and a useful language for describing such phenomena. As one example, notions of circuit and network and modularity—originally conceptualized in the world of engineering and computer science—have much applicability to understanding biological phenomena.

On the other side, biology refers to the scientific study of the activities, processes, mechanisms, and other attributes of living organisms. For the purposes of this report, biology, biomedicine, life sciences, and other descriptions of research into how living systems work should be regarded as synonymous. In this context, for the past decade, researchers have spoken increasingly of a new biology, a biology of the 21st century, one that is driven by new technologies, that is more automated with tools and methods provided by industrial models, and that often entails high-throughput data acquisition.1 This report examines the BioComp interface from the perspective of 21st century biology, as a science that integrates traditional empirical and experimental biology with a systems-level biology that considers the multiscale, hierarchical, highly interwoven, or interactive aspects intrinsic to living systems.


This report addresses computationally inspired ways of understanding biology and biologically inspired ways of understanding computing. Although the committee started its work with the idea that it would discover a single community and intellectual synthesis of biology and computing, closer examination showed that the appropriate metaphor is one of an interface between the two fields rather than a common, shared area of inquiry. Thus, the adventures along the frontier cannot be treated as coming from a single community, and the different objectives have to be recognized.


For example, see National Research Council, Opportunities in Biology, National Academy Press, Washington, DC, 1989. High-throughput data acquisition is an approach that relies on the large-scale parallel interrogation of many similar biological entities. Such an approach is essential for the conduct of global biological analyses, and it is often the approach of choice for rapid and comprehensive assessment of biological system properties and dynamics. See, for example, T. Ideker, T. Galitski, and L. Hood, “A New Approach to Decoding Life: Systems Biology,” Annual Review of Genomics and Human Genetics 2:343-372, 2001. A number of the high-throughput data acquisition technologies mentioned in that article are discussed in Chapter 7 of his report.

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