Network decomposition based large-scale reverse engineering of gene regulatory network

A Gene Regulatory Network (GRN) is the functional circuitry of a living organism that exhibits the regulatory relationships among genes of a cellular system at the gene level. In real-life biological networks, the number of genes present are very large exhibiting both, the instantaneous and time-delayed regulations. While our recent technique [1] addresses the modeling of time-delays occurring in genetic interactions, the issue of large-scale GRN modeling still remains. In this paper, we propose a novel methodology for large-scale modeling of GRNs by decomposing the GRN into two independent sub-networks utilizing its biological traits. Using the time-delayed S-system model [1], these two sub-networks are learnt separately and then combined to get the entire GRN. To speed up the inference mechanism, a cardinality-based fitness function, especially developed for inferring large-scale GRNs is proposed to allow incorporation of knowledge of maximum in-degree. A novel local-search method is also proposed to further facilitate the incorporation of biological knowledge by gene clustering and gene ranking. Experimental studies demonstrate that the proposed approach is successful in learning large genetic networks, currently not achievable with existing S-system based modeling approaches.