Research Article Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks Min Li, 1 Weijie Chen, 1 Jianxin Wang, 1 Fang-Xiang Wu, 2 and Yi Pan 1,3 1 School of Information Science and Engineering, Central South University, Changsha 410083, China 2 Department of Mechanical Engineering, University of Saskatchewan, SK, Canada S7N 5A9 3 Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA Correspondence should be addressed to Jianxin Wang; jxwang@csu.edu.cn Received 25 January 2014; Accepted 6 March 2014; Published 18 May 2014 Academic Editor: Luonan Chen Copyright © 2014 Min Li et al. his 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. Identiication 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 proiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. he protein-complex cores are identiied 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. he protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. he 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. 1. Introduction In the postgenomic era, more and more attention has been paid to proteomics. Proteins are central part of life activity. Within a cell, proteins cannot work alone to carry out cel- lular functions while these cellular functions are performed by many proteins bound together into protein complexes [1]. With the development of high-throughput techniques, amount of protein-protein interactions (PPI) has been cata- logued. Such protein-protein interaction data can provide us with a chance to understand complicated biological systems from a network view. Up to now, many computational methods have been proposed for identifying protein complexes from PPI net- works. he most common network-based methods are to detect dense subgraphs from PPI networks as complexes for the researchers believe that proteins in the same complex generally implement the same or similar function and tend to interact with each other [2, 3]. Spirin and Mirny [2] pro- posed to enumerate all the maximal cliques (fully connected subgraphs) as protein complexes. Liu et al. [4] presented a method called CMC (Clustering-based on Maximal Cliques) which also identiies protein complexes based on maximal cliques. he maximal cliques are weighted and the highly overlapping cliques are merged or removed. Palla et al. [5] proposed a clique percolation method, named CPM, to identify overlapping communities in complex networks. Our group also proposed a clique-based method IPC-MCE [6] which detects maximal cliques irst and then extends from the maximal cliques to generate protein complexes. MCODE proposed by Bader and Hogue [7] is a local- searched method to detect protein complexes based on the proteins’ connectivity values in PPI network. Altaf-UI-Amin et al. [8] gave an algorithm DPClus based on the combination of density and peripheral proteins to mine densely connected subgraphs. By modifying the DPClus algorithm based on new Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 375262, 10 pages http://dx.doi.org/10.1155/2014/375262