An efficient method for dynamic analysis of gene regulatory networks and in silico gene perturbation experiments

Abstract. With the increasing availability of experimental data on gene-gene and protein-protein interactions, modeling of gene regulatory networks has gained a special attention lately.Tohave abetter understand-ing of these networks it is necessary to capture their dynamical proper-ties, by computing its steady states. Various methods have been proposed to compute steady states but almost all of them suffer from the state space explosion problem with the increasing size of the networks. Hence it be-comes difficult to model even moderate sized networks using these tech-niques. In this paper, we present a new representation of gene regulatory networks, which facilitates the steady state computation of networks as large as 1200 nodes and 5000 edges. We benchmarked and validated our algorithm on the T helper model from [8] and performed in silico knock out experiments: showing both a reduction in computation time and cor-rect steady state identification.