Determining modular organization of protein interaction networks by maximizing modularity density

Shihua Zhang,Xiang-Sun Zhang,Chris Ding,Xue-Mei Ning

doi:10.1186/1752-0509-4-s2-s10

Abstract

BackgroundWith 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.ResultsThe 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.ConclusionsOur 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.

Highlights

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
Comparison with modularity Q (MQ) and Markov Cluster algorithm (MCL), we show that the Spectral method for maximizing D (SpeMD) can obtain competitive performance with the well-known MCL method and resolve much finer modular structure than MQ method
Results on a protein-protein interaction (PPI) network The budding yeast S. cerevisiae PPI network was obtained from the DIP database, which contains human-curated high-throughput and small-scale binary interactions directly observed in experiments, as well as binary interactions inferred from high-confidence protein complex data

Summary

Introduction

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. The well-understood graphtheoretical concepts has become a powerful tool to explore the topology, organization, function and evolution of biological networks. In this field, recent studies have made great progresses which considerably. How to uncover modular structures in various biological networks is a basic step for understanding cellular functions and organizational mechanisms of biosystems. By using the network partition, Zhao et al (2006) investigated the functional and evolutionary modularity of human metabolic networks from a topological perspective [3]

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