alexa Symmetry of Metabolic Network
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
Open Access

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Research Article

Symmetry of Metabolic Network

Hua Dong1, 2, Yanghua Xiao3, Wei Wang3, Li Jin1,4, Momiao Xiong1, 2*

1Laboratory of Theoretical Systems Biology and Center for Evolutionary Biology, State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University,   Shanghai 200433, China

2Human Genetics Center, University of Texas School of Public Health, Houston, TX 77030,   USA

3Department of Computing and Information Technology, Fudan University,   Shanghai 200433, China  

4Chinese Academy of Science-Max-Planck-Gesellschaft Partner Institute for Computational   Biology, Shanghai Institutes for Biological Science, CAS, Shanghai, 200433, China 

*Corresponding Author:
Dr. Momiao Xiong,
Phone : 713-500-9894,
Fax : 713-500-0900,
Email : [email protected]

Received Date: December 14, 2008; Accepted Date: December 24, 2008; Published Date: December 26, 2008

Citation: Hua D, Yanghua X, Wei W, Li J, Momiao X (2008) Symmetry of Metabolic Network. J Comput Sci Syst Biol 1: 001-020. doi: 10.4172/jcsb.1000001

Copyright: © 2008 Hua D, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Previous studies of properties of metabolic works have mainly focused on the statistic properties of networks, including the small world, and power-law distribution of node degree, and building block of network motifs. Symmetry in the metabolic networks has not been systematically investigated. In this report, symmetry in directed graph was introduced and an algorithm to calculate symmetry in directed and disconnected graphs was developed. We calculated several indices to measure the degree of symmetry and compared them with random networks. We showed that metabolic networks in KEGG and BioCyc databases are generally symmetric and in particular locally symmetric. We found that symmetry in metabolic networks is distinctly higher than that in random networks. We obtained all the orbits in networks which are defined as structurally equivalent nodes and found that compound pairs in the same orbit show much more similarity in chemical structures and function than random compound pairs in network, which suggests that symmetry in the metabolic network can generate the functional redundancy, increase the robustness and play an important role in network structure, function and evolution.

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