Abstract not found

Abstract. Inferring transcriptional regulatory networks from gene-expression data remains a challenging problem, in part because of the noisy nature of the data and the lack of strong network models. Time-series expression data have shown promise and recent work by Babu on the evolution of regulatory networks in E. coli and S. cerevisiae opened another avenue of investigation. In this paper we take the evolutionary approach one step further, by developing ML-based refinement algorithms that take advantage of established phylogenetic relationships among a group of related organisms and of a simple evolutionary model for regulatory networks to improve the inference of these networks for these organisms from expression data gathered under similar conditions. We use simulations with different methods for generating gene-expression data, different phylogenies, and different evolutionary rates, and use different network inference algorithms, to study the performance of our algorithmic boosters. The results of simulations (including various tests to exclude confounding factors) demonstrate clear and significant improvements (in both specificity and sensitivity) on the performance of current inference algorithms. Thus geneexpression studies across a range of related organisms could yield significantly more accurate regulatory networks than single-organism studies. 1

Abstract not found

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 show significant improvement on the reference network inference algorithms. We also provide results on biological data (cis-regulatory modules for 12 species of Drosophila), confirming our simulation results. 1

Inferring transcriptional regulatory networks from geneexpression data remains a challenging problem, in part because of the noisy nature of the data and the lack of strong network models. Time-series expression data have shown promise and recent work by Babu on the evolution of regulatory networks in E. coli and S. cerevisiae opened another avenue of investigation. In this paper we take the evolutionary approach one step further. We conjecture that established phylogenetic relationships among a group of related organisms can be used to improve the inference of regulatory networks for these organisms from expression data gathered under similar conditions. We develop an inference algorithm to take advantage of such information and present the results of simulations (including various tests to exclude confounding factors) that clearly show the added value of the phylogenetic information. Our algorithm and results offer support for our conjecture and indicate that gene-expression studies under identical conditions across a range of related organisms could yield significantly more accurate regulatory networks than single-organism studies. 1.