Almaas Eivind
Microbial Systems Biology, Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, 7000 East Avenue, PO Box 808, L-452, Livermore, CA 94550, USA.
J Exp Biol. 2007 May;210(Pt 9):1548-58. doi: 10.1242/jeb.003731.
Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large-scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory networks, signal transduction networks, protein interaction networks and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.
许多复杂系统都可以作为网络来表示和分析,受益于这种方法的例子涵盖了自然科学领域。例如,我们现在知道,像万维网、互联网、科学合作、食物网、蛋白质相互作用和新陈代谢等截然不同的系统,在其组织结构上都有共同特征,其中最显著的是它们的无标度连通性分布和小世界行为。最近,涵盖生物体蛋白质组或代谢组的大规模数据集的出现,使得阐明一些支配其功能、稳健性和进化的组织原则和规则成为可能。我们预计,将目前来自基因调控网络、信号转导网络、蛋白质相互作用网络和代谢网络的不同信息层结合起来,将极大地增进我们对细胞功能和动态的理解。