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Catalyzing Inquiry at the Interface of Computing and Biology
understood, or for which the relevant underlying technologies are too complex or unwieldy, and in providing approaches that will address parts of a solution (as described in Section 8.1.2). Nevertheless, the potential benefits that biology might offer to certain problem areas in computing are large, and it is worth exploring different approaches to exploit these benefits; this is the focus of Sections 8.2 to 8.4.
8.1.2The Meaning of Biological Inspiration
What does it mean for something to be biologically inspired? It is helpful to consider several possible interpretations. One interpretation is that significant progress in computing can occur only through the application of principles derived from the study of biology. This interpretation, offered largely as a strawman, is absurd—there are many ways in which computing can progress without the application of biologically derived principles.
A second, somewhat less grandiose and more reasonable interpretation is that significant progress in computing can occur through the application of principles derived from the study of biology. That is, a biological system may operate according to principles that have applicability to nonbiological computing problems. By studying the biological system, one may be able to derive or understand the relevant principles and use them to help solve a nonbiological problem. It is this interpretation—that biology is relevant to computing only when principles emerge directly from a study of biological phenomena—that underlies many claims of biological relevance or irrelevance to computing.
A third interpretation is that certain aspects of biology are analogous to aspects of computing, which means that insights from biology are relevant to aspects of computing. This is the case, for instance, when a set of principles or paradigms turns out to have strong applicability both to a biological system or systems and to interesting problems in computing. These principles or paradigms may have had their intellectual origin in the study of a biological or a nonbiological system.
When their origin is in a biological system, this interpretation reduces to the second interpretation above. What makes the case of an origin in a nonbiological system interesting is that the principles in question may be more manifestly obvious in a biological context than in a nonbiological context. That is, the principles and their application may most easily be seen and appreciated in a biological context, even if they did not initially originate in a biological context. Moreover, the biological context may also provide a source of language, concepts, and metaphors that are useful in talking about a nonbiological problem or phenomenon.
For this report, the term “inspiration” will be used in its broadest sense, that is, the third interpretation above, but there are three other points to keep in mind:
Biological inspiration does not mean that the weaknesses of biology must be adopted along with the strengths. In some cases, it may be possible to overcome problems found in the actual biological system when the principles underlying them are implemented in engineered artifacts.
As noted in Chapter 1, even when biology cannot provide insight into potential computing solutions, the drive to solve biological problems can still inspire interesting, relevant, and intellectually challenging research in computing—so biology can serve as a useful and challenging problem domain for computing.3
For example, IBM used the problem of protein folding to motivate the development of the BlueGene/L supercomputer. Specifically, the problem was formulated in terms of obtaining a microscopic view of the thermodynamics and kinetics of the dynamic protein-folding process over longer time scales than have previously been possible. Because this project involved both computer architecture and the exploration of algorithmic alternatives, the applications architecture was structured in such a way that subject experts in molecular simulation could work on their applications without having to deal with the complexity of the parallel communications environment required by the underlying machine architecture (see BlueGene/L Team, “An Overview