2011•China Journal of BioinformaticsRequires access

Complex Network Perspective on Giant Strong Component of Human Metabolic Network

Xudong Liu

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Abstract

With the assistance of complex network theory,we analyze the giant strong component of human metabolic network in the present paper.We obtain all metabolic reactions in giant strong component of human metabolic network from published high-quality metabolic models.A network which contains 256 nodes and 648 lines is formed when these reactions are denoted by metabolite graph.Community structure of the network is analyzed by simulated annealing method,and the resulted modules all with important biological function.We also discuss the biological and therapeutic significance of hub nodes in the network by some different centrality methods.

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What this paper is about

With the assistance of complex network theory,we analyze the giant strong component of human metabolic network in the present paper.We obtain all metabolic reactions in giant strong component of human metabolic network from published high-quality metabolic models.A network which contains 256 nodes and 648 lines is formed when these reactions are denoted by metabolite graph.Community structure of the network is analyzed by simulated annealing method,and the resulted modules all with important biological function.We also discuss the biological and therapeutic significance of hub nodes in the network by some different centrality methods.

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Available abstract

With the assistance of complex network theory,we analyze the giant strong component of human metabolic network in the present paper.We obtain all metabolic reactions in giant strong component of human metabolic network from published high-quality metabolic models.A network which contains 256 nodes and 648 lines is formed when these reactions are denoted by metabolite graph.Community structure of the network is analyzed by simulated annealing method,and the resulted modules all with important biological function.We also discuss the biological and therapeutic significance of hub nodes in the network by some different centrality methods.

Key concepts: Metabolic network, Centrality, Biological network, Computer science, Giant component, Component (thermodynamics), Simulated annealing, Graph

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