Functional flow simulation based analysis of protein interaction network

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes and differ based on the composition, affinity and lifetime of the association. A vast amount of PPI data for various organisms is avaiable from MIPS, DIP and other sources. The identification of functional modules in PPI network is of great interest because they often reveal unknown functional ties between proteins and hence predict functions for unknown proteins. In this paper, we propose using functional flow simulation and the topology of the network for the functional module detection and function prediction problem. Our approach is based on the functional influence model that quantifies the influence of a biological component on another. We introduce a flow simulation algorithm to generate a functional profile for each component. In addition, a new clustering method FMD (Functional Module Detection) is designed to associate with functional profiles to detect functional modules. We evaluate the proposed technique on three different yeast networks with MIPS functional categories and compare it with several other existing techniques in terms of precision and recall. Our experiments show that our approach achieves better accuracy than other existing methods.