Abstract
Community structure analysis methods are important tools in modeling and analyzing large-scale metabolic networks. However, traditional community structure methods are mainly solved by clustering nodes, which results in each metabolite belonging to only a single community, which limits their usefulness in the study of metabolic networks. In the present paper, we analyze the community structure and functional modules in the Staphylococcus aureus (S. aureus) metabolic network, using a link clustering algorithm, and we obtain 10 functional modules with better biological insights, which give better results than our previous study. We also evaluate the essentiality of nodes in S. aureus metabolic networks. We suggest that link clustering could identify functional modules and key metabolites in metabolic networks.
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