Researching on Functional Modules in E. coli Metabolic Network Based on Simulated Annealing Algorithm

Topological methods have been developed to study the fundamental organizational principles of large-scale metabolic networks for years. However, the most important two topological characteristics (small-world and scale-free) could not provide sufficient characterizations of the metabolic networks. To discover functional information involved in metabolic networks, we need identify functional modules. In this paper, after formal introducing clustering coefficient and modularity, two important properties related to functional modules in networks, we study functional modules in E. coli (an important gram-negative bacterium) metabolism based on simulated annealing algorithm, which tries to find the optimal modules by maximizing the network modularity.