Research article

Modular co-evolution of metabolic networks

Jing Zhao^1,2,4 , Guo-Hui Ding³ , Lin Tao² , Hong Yu² , Zhong-Hao Yu¹ , Jian-Hua Luo¹ , Zhi-Wei Cao² and Yi-Xue Li^2,3,1

¹  School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China

²  Shanghai Center for Bioinformation and Technology, Shanghai 200235, China

³  Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

⁴  Department of Mathematics, Logistical Engineering University, Chongqing 400016, China

author email corresponding author email

BMC Bioinformatics 2007, 8:311doi:10.1186/1471-2105-8-311

Published:	27 August 2007

Abstract

Background

The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.

Results

In this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do.

Conclusion

The correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.