This article is part of the supplement: Selected articles from the Third International Symposium on Optimization and Systems Biology
Determining modular organization of protein interaction networks by maximizing modularity density
1 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
2 Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
3 College of Science, Beijing Forestry University, Beijing 100083, China
4 Department of Computer Science and Engineering, University of Texas at Arlington Arlington, TX 76019, USA
BMC Systems Biology 2010, 4(Suppl 2):S10 doi:10.1186/1752-0509-4-S2-S10Published: 13 September 2010
With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable.
The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network.
Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method.