MPageRank: A Novel Method to Effectively Mine Functional Modules in Heterogeneous Biological Networks

The advent of systems biology approaches, which typically integrate ‘omics’ technologies that generate and analyze large scale gene expression, molecular and chemical interaction profiles, have made an opportune time to study and answer how biological processes and complex phenotypes (also called traits) are regulated in living cells. One of major challenges in current bioinformatics and systems biology researches is the identification of functional modules/sub-networks in such big multiclass of ‘omics’ data. Identification of context-dependent active functional modules using graph-based approaches is a promising strategy. However, to date, few of such approaches have been developed to extract functional modules/sub-networks by integrative analyzing the topological structures of multi-classes of biological networks

In this abstract, we present a novel approach, called MPageRank, to mine functional modules in heterogonous biological networks, particular in the expression-based gene-gene association networks and protein-protein interaction networks. MPageRank simulates “bias random walking” on multiple networks to rank nodes in multiplex systems, in which the importance of a node in one network is affected by the importance that node gained in another network, therefore the MPageRank is able to effectively leverage network transitivity to rank nodes in heterogeneous networks. We demonstrate the capabilities of our approach comparing the performance of our method with that of other published methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results suggest that the MPageRank method is effective for mining functional modules/sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks. Furthermore, the MPageRank approach can to be extended for the discovery of functional modules/sub-networks in other types of heterogeneous biological networks, such as signaling transduction, metabolism and gene regulation networks.