About

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

We present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other 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 demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks.

Supplementary Information

The mPageRank executable program, the datasets and results of the presented two case studies and source code are publicly and freely available.