title: Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data.
abstract: BACKGROUND
  Identification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules.
  
  
  RESULTS
  In this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using "Biological Process" annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of "Biological Process". More importantly, the relationship between protein complexes and functional modules are studied.
  
  
  CONCLUSIONS
  The proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index.html.
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, China. limin@mail.csu.edu.cn
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.forename: Xuehong
authors.initials: X
authors.lastname: Wu
authors.forename: Jianxin
authors.initials: J
authors.lastname: Wang
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis
chemical_list: D029701:Saccharomyces cerevisiae Proteins