title: A comparison of the functional modules identified from time course and static PPI network data.
abstract: BACKGROUND
  Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems.
  
  
  RESULTS
  In this paper, Time Course Protein Interaction Networks (TC-PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three kinds of networks: the TC-PINs, a static PPI network and a pseudorandom network. For the functional modules from the TC-PINs, repetitive modules and modules contained within bigger modules are removed. Finally, matching and GO enrichment analyses are performed to compare the functional modules detected from those networks.
  
  
  CONCLUSIONS
  The comparative analyses show that the functional modules from the TC-PINs have much more significant biological meaning than those from static PPI networks. Moreover, it implies that many studies on static PPI networks can be done on the TC-PINs and accordingly, the experimental results are much more satisfactory. The 36 PPI networks corresponding to 36 time points, identified as part of this study, and other materials are available at http://bioinfo.csu.edu.cn/txw/TC-PINs.
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, 410083, China.
authors.forename: Xiwei
authors.initials: X
authors.lastname: Tang
authors.forename: Jianxin
authors.initials: J
authors.lastname: Wang
authors.forename: Binbin
authors.initials: B
authors.lastname: Liu
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D015398:Signal Transduction; D013997:Time Factors
chemical_list: D011506:Proteins