复杂网络的中心化及其在代谢网络中的应用(英文)

Centralization of complex networks:application to metabolic networks

分析基因组规模的生化网络是后基因组时代的一项重要研究任务.由于缺乏详尽的热力学参数,近年来科研人员已经开发出了大量基于网络拓扑结构的分析方法.其中,中心化指标可用于确定网络中的重要节点,因而有助于理解代谢网络的交互和调控机制.本文首先比较地分析了10种不同的中心化指标,随后将它们运用于分析苏云金芽孢杆菌的代谢网络,确定了其代谢网络巨强连通成分中的10个关键节点并分析了它们的生物学功能意义.

One of the most important tasks for post-genomic era is to analyze genome-scale biochemical networks reconstructed. Due to the absence of detailed kinetic parameters, a number of topological structural based approaches have already been developed in the past few years. Among these, centrality measures methods are used to determine which individual nodes of a network are more important than others and hence in helping to elucidate the fundamental mechanisms for the interaction and regulatory. Herein, by comparing 10 different centrality measures with their application to B.thuringiensis metabolic network, we give the top 10 center metabolites in giant strong component （GSC） of B.thuringiensis metabolic network with their biological signification.