Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Refining transcriptional regulatory networks using network evolutionary models and gene histories. | Zhang and Moret Algorithms for Molecular Biology 2010 5 1 http content 5 1 1 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Refining transcriptional regulatory networks using network evolutionary models and gene histories Xiuwei Zhang Bernard ME Moret Abstract Background 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 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. Results 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 show significant improvement on the reference network inference algorithms. Different evolutionary histories for gene duplications and losses are studied showing that our adapted approach is feasible under a broad range of conditions. We also provide results on biological data cis-regulatory modules for 12 species of Drosophila confirming our simulation results. Introduction Transcriptional regulatory networks are models of the cellular regulatory system that governs transcription. Because establishing the topology of the network from bench experiments is very difficult and time-consuming regulatory networks are commonly inferred from geneexpression data. Various computational models such as Boolean networks 1 Bayesian networks 2
