title: Identification of functional modules in a PPI network by clique percolation clustering.
abstract: Large-scale experiments and data integration have provided the opportunity to systematically analyze and comprehensively understand the topology of biological networks and biochemical processes in cells. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we apply a graph clustering algorithm based on clique percolation clustering to detect overlapping network modules of a protein-protein interaction (PPI) network. Our analysis of the yeast Sacchromyces cerevisiae suggests that most of the detected modules correspond to one or more experimentally functional modules and half of these annotated modules match well with experimentally determined protein complexes. Our method of analysis can of course be applied to protein-protein interaction data for any species and even other biological networks.
authors.affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China. zsh@amss.ac.cn
authors.forename: Shihua
authors.initials: S
authors.lastname: Zhang
authors.forename: Xuemei
authors.initials: X
authors.lastname: Ning
authors.forename: Xiang-Sun
authors.initials: XS
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D030562:Databases, Protein; D025941:Protein Interaction Mapping; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000145:classification / Q000378:metabolism*; D049490:Systems Biology
chemical_list: D029701:Saccharomyces cerevisiae Proteins