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Global view of bionetwork dynamics:adaptive landscape

Journal of Genetics and Genomics 2009年第2期36卷 63-73页

作者： Ping Ao Department of Mechanical Engineering and Department of Physics University of Washington Seattle WA 98195 USA Shanghai Center for Systems Biomedicine Key Laboratory of Systems Biomedicine of Ministry of Education Shanghai Jiao Tong University Shanghai 200240 China

Based on recent work, I will give a nontechnical brief review of a powerful quantitative concept in biology, adaptive landscape, ini- tially proposed by S. Wright over 70 years ago, reintroduced by one of the founders of molecular biology and by others in different bio- logical contexts, but apparently forgotten by modem biologists for many years. Nevertheless, this concept finds an increasingly important role in the development of systems biology and bionetwork dynamics modeling, from phage lambda genetic switch to endogenous net- work for cancer genesis and progression. It is an ideal quantification to describe the robustness and stability of bionetworks. Here, I will first introduce five landmark proposals in biology on this concept, to demonstrate an important common thread in theoretical biology. Then I will discuss a few recent results, focusing on the studies showing theoretical consistency of adaptive landscape. From the perspec- tive of a working scientist and of what is needed logically for a dynamical theory when confronting empirical data, the adaptive landscape is useful both metaphorically and quantitatively, and has captured an essential aspect of biological dynamical processes. Though at the theoretical level the adaptive landscape must exist and it can be used across hierarchical boundaries in biology, many associated issues are indeed vague in their initial formulations and their quantitative realizations are not easy, and are good research topics for quantitative biologists. I will discuss three types of open problems associated with the adaptive landscape in a broader perspective.

关键词： adaptive landscape stochastic dynamics, bionetworks systems biology

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