A Graph-Theoretic Method for Mining Functional Modules in Large Sparse Protein Interaction Networks

Abstract

With ever increasing amount of available data on protein-protein interaction (PPI) networks, understanding the topology of the networks and then biochemical processes in cells has become a key problem. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we propose a method that combines the line graph transformation and clique percolation clustering algorithm to detect network modules which may overlap each other in large sparse protein-protein interaction (PPI) networks. The resulting modules by the present method show a high coverage among yeast, fly, and worm PPI networks respectively. Our analysis of the yeast PPI network suggests that most of these modules have well biological significance in context of protein localization, function annotation, and protein complexes. The overlapping modules form a cartographic network representation which also shows well scale-free property.

Recent advances in clustering methods for protein interaction networks

“…Jonsson et al [ 71 ] constructed a weighted protein interaction network for rat proteome and used CPM to identify key protein clusters involved in cancer metastasis. Zhang et al proposed two types of strategies: size control [ 72 ] and line graph transformation [ 73 ] when using CPM. For size control, they used k =3 to generate initial clusters and then iteratively used k +1 to separate the clusters of size larger than a given integer S until all the identified clusters of size were less than S .…”