title: Community structure detection for overlapping modules through mathematical programming in protein interaction networks.
abstract: Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non linear programming (MINLP) model, known as OverMod, which transforms disjoint communities to overlapping. The procedure is evaluated through its application to protein-protein interaction (PPI) networks of the rat, E. coli, yeast and human organisms. Connector nodes of hard partitions exhibit topological and functional properties indicative of their suitability as candidates for multiple module membership. OverMod identifies two types of connector nodes, inter and intra-connector, each with their own particular characteristics pertaining to their topological and functional role in the organisation of the network. Inter-connector proteins are shown to be highly conserved proteins participating in pathways that control essential cellular processes, such as proliferation, differentiation and apoptosis and their differences with intra-connectors is highlighted. Many of these proteins are shown to possess multiple roles of distinct nature through their participation in different network modules, setting them apart from proteins that are simply 'hubs', i.e. proteins with many interaction partners but with a more specific biochemical role.
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), Torrington Place, WC1E 7JE, London, United Kingdom.
authors.forename: Laura
authors.initials: L
authors.lastname: Bennett
authors.affiliation: Department of Informatics, King's College London, Strand, WC2R 2LS, London, United Kingdom.
authors.forename: Aristotelis
authors.initials: A
authors.lastname: Kittas
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), Torrington Place, WC1E 7JE, London, United Kingdom.
authors.forename: Songsong
authors.initials: S
authors.lastname: Liu
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), Torrington Place, WC1E 7JE, London, United Kingdom.
authors.forename: Lazaros G
authors.initials: LG
authors.lastname: Papageorgiou
authors.affiliation: Department of Informatics, King's College London, Strand, WC2R 2LS, London, United Kingdom.
authors.forename: Sophia
authors.initials: S
authors.lastname: Tsoka
mesh_terms: D000465:Algorithms; D000818:Animals; D004926:Escherichia coli / Q000378:metabolism; D029968:Escherichia coli Proteins / Q000378:metabolism; D006801:Humans; D008962:Models, Theoretical; D060066:Protein Interaction Maps; D051381:Rats; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
chemical_list: D029968:Escherichia coli Proteins; D029701:Saccharomyces cerevisiae Proteins