be understood as “letting a thousand flowers bloom” rather than “identifying the prettiest flowers in the landscape.”


Twenty-first century biology will integrate a number of diverse intellectual notions. One integration is that of the reductionist and systems approaches—a focus on components of biological systems combined with a focus on interactions among these components. A second integration is that of many distinct strands of biological research: taxonomic studies of many species, the enormous progress in molecular genetics, steps toward understanding the molecular mechanisms of life, and a consideration of biological entities in relationship to their larger environment. A third integration is that computing will become highly relevant to both hypothesis testing and hypothesis generation in empirical work in biology. Finally, 21st century biology will also encompass what is often called discovery science—the enumeration and identification of the components of a biological system independently of any specific hypothesis about how that system functions (a canonical example being the genomic sequencing of various organisms). Twenty-first century biology will embrace the study of an inclusive set of biological entities, their constituent components, the interactions among components, and the consequences of those interactions, from molecules, genes, cells, and organisms to populations and even ecosystems.

How will computing play in 21st century biology? Life scientists have exploited computing for many years in some form or another. Yet what is different today—and will increasingly be so in the future—is that the knowledge of computing needed to address many interesting biological problems can no longer be learned and exploited simply by “hacking” and reading the manuals. Indeed, the kinds and levels of expertise needed to address the most challenging problems of 21st century biology stretch the current state of knowledge of the field—a point that illuminates the importance of real computing research in a biological context.

This report identifies four distinct but interrelated roles of computing for biology.

  1. Computational tools are artifacts—usually implemented as software but sometimes hardware—that enable biologists to solve very specific and precisely defined problems. Such biologically oriented tools acquire, store, manage, query, and analyze biological data in a myriad of forms and in enormous volume for its complexity. These tools allow biologists to move from the study of individual phenomena to the study of phenomena in a biological context; to move across vast scales of time, space, and organizational complexity; and to utilize properties such as evolutionary conservation to ascertain functional details.

  2. Computational models are abstractions of biological phenomena implemented as artifacts that can be used to test insights, to make quantitative predictions, and to help interpret experimental data. These models enable biological scientists to understand many types of biological data in context, even in very large volume, and to make model-based predictions that can then be tested empirically. Such models allow biological scientists to tackle difficult problems that could not readily be posed without visualization, rich databases, and new methods for making quantitative predictions. Biological modeling itself has become possible because data are available in unprecedented richness and because computing itself has matured enough to support the analysis of such complexity.

  3. A computational perspective on or metaphor for biology applies the intellectual constructs of computer science and information technology as ways of coming to grips with the complexity of biological phenomena that can be regarded as performing information processing in different ways. This perspective is a source of information and computing abstractions that can be used to interpret and understand biological mechanisms and function. Because both computing and biology are concerned with function, information and computing abstractions can provide well-understood constructs that can be used to characterize the biological function of interest. Further,

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