Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

A comparison of the functional modules identified from time course and static PPI network data

Xiwei Tang12, Jianxin Wang1*, Binbin Liu1, Min Li13*, Gang Chen1 and Yi Pan13

Author Affiliations

1 School of Information Science and Engineering, Central South University, Changsha, 410083, China

2 School of Information Science and Engineering, Hunan First Normal University, Changsha, 410205, China

3 Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA

For all author emails, please log on.

BMC Bioinformatics 2011, 12:339  doi:10.1186/1471-2105-12-339

Published: 15 August 2011

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. webcite