Improving Inference of Transcriptional Regulatory Networks Based on Network Evolutionary Models

Abstract. Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present evolutionary approaches to improve the inference of regulatory networks for a family of organisms by developing an evolutionary model for these networks and taking advantage of established phylogenetic relationships among these organisms. In previous work, we used a simple evolutionary model for regulatory networks and provided extensive simulation results showing that phylogenetic information, combined with such a model, could be used to gain significant improvements on the performance of current inference algorithms. In this paper, we extend the evolutionary model so as to take into account gene duplications and losses, which are viewed as major drivers in the evolution of regulatory networks. We show how to adapt our evolutionary approach to this new model and provide detailed simulation results, which sho...

• Type: Conference
• Year: 2009
• Where: WABI
• Authors: Xiuwei Zhang, Bernard M. E. Moret
• Keywords: Algorithms, Evolutionary Model, Inference, Regulatory Networks, WABI 2009