title: Exploring local structural organization of metabolic networks using subgraph patterns.
abstract: Metabolic networks of many cellular organisms share global statistical features. Their connectivity distributions follow the long-tailed power law and show the small-world property. In addition, their modular structures are organized in a hierarchical manner. Although the global topological organization of metabolic networks is well understood, their local structural organization is still not clear. Investigating local properties of metabolic networks is necessary to understand the nature of metabolism in living organisms. To identify the local structural organization of metabolic networks, we analysed the subgraphs of metabolic networks of 43 organisms from three domains of life. We first identified the network motifs of metabolic networks and identified the statistically significant subgraph patterns. We then compared metabolic networks from different domains and found that they have similar local structures and that the local structure of each metabolic network has its own taxonomical meaning. Organisms closer in taxonomy showed similar local structures. In addition, the common substrates of 43 metabolic networks were not randomly distributed, but were more likely to be constituents of cohesive subgraph patterns.
authors.affiliation: Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea.
authors.forename: Young-Ho
authors.initials: YH
authors.lastname: Eom
authors.forename: Soojin
authors.initials: S
authors.lastname: Lee
authors.forename: Hawoong
authors.initials: H
authors.lastname: Jeong
mesh_terms: D000465:Algorithms; D000818:Animals; D001105:Archaea / Q000145:classification / Q000378:metabolism; D001419:Bacteria / Q000145:classification / Q000378:metabolism; D005057:Eukaryotic Cells / Q000145:classification / Q000378:metabolism; D008660:Metabolism; D008954:Models, Biological