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BioMed Research International
Volume 2014 (2014), Article ID 375262, 10 pages
http://dx.doi.org/10.1155/2014/375262
Research Article

Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

1School of Information Science and Engineering, Central South University, Changsha 410083, China
2Department of Mechanical Engineering, University of Saskatchewan, SK, Canada S7N 5A9
3Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA

Received 25 January 2014; Accepted 6 March 2014; Published 18 May 2014

Academic Editor: Luonan Chen

Copyright © 2014 Min Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.