Modeling Gene Regulatory Subnetworks from Time Course Gene Expression Data

Abstract

Identifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN econstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to thers in the sub-networks. In this paper, we propose an efficient algorithm for identifying multiple sub-networks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and thus promisingly applies to large-scale GRNs. Experimental studies on synthetic datasets validate the effectiveness of the proposed algorithm in the inference of sub-networks.