Research article

Hierarchical modularity of nested bow-ties in metabolic networks

Jing Zhao^1,2,4 , Hong Yu² , Jian-Hua Luo¹ , Zhi-Wei Cao² and Yi-Xue Li^2,3,1

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

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

³Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

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

author email corresponding author email

BMC Bioinformatics 2006, 7:386doi:10.1186/1471-2105-7-386

Published:	18 August 2006

Abstract

Background

The exploration of the structural topology and the organizing principles of genome-based large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks.

Results

In this work, metabolic networks of 75 organisms were investigated from a topological point of view. Network decomposition of three microbes (Escherichia coli, Aeropyrum pernix and Saccharomyces cerevisiae) shows that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks. Moreover, these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network, and important features of this modularity are not observed in the random shuffled network. In addition, such a bow-tie pattern appears to be present in certain chemically isolated functional modules and spatially separated modules including carbohydrate metabolism, cytosol and mitochondrion respectively.

Conclusion

The highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident. Identification and analysis of such a pattern is helpful for understanding the design principles and facilitate the modelling of metabolic networks.