ProPhyC: a probabilistic phylogenetic model for refining regulatory networks

Authors:
Xiuwei Zhang;Bernard M. E. Moret
Affiliations:
Laboratory for Computational Biology and Bioinformatics, EPFL, Switzerland and Swiss Institute of Bioinformatics;Laboratory for Computational Biology and Bioinformatics, EPFL, Switzerland and Swiss Institute of Bioinformatics
Venue:
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Year:
2011

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Abstract

The experimental determination of transcriptional regulatory networks in the laboratory remains difficult and time-consuming, while computational methods to infer these networks provide only modest accuracy. The latter can be attributed in part to the limitations of a single-organism approach. Computational biology has long used comparative and, more generally, evolutionary approaches to extend the reach and accuracy of its analyses. We therefore use an evolutionary approach to the inference of regulatory networks, which enables us to study evolutionary models for these networks as well as to improve the accuracy of inferred networks. We describe ProPhyC, a probabilistic phylogenetic model and associated inference algorithms, designed to improve the inference of regulatory networks for a family of organisms by using known evolutionary relationships among these organisms. ProPhyC can be used with various network evolutionary models and any existing inference method. We demonstrate its applicability with two different network evolutionary models: one that considers only the gains and losses of regulatory connections during evolution, and one that also takes into account the duplications and losses of genes. Extensive experimental results on both biological and synthetic data confirm that our model (through its associated refinement algorithms) yields substantial improvement in the quality of inferred networks over all current methods.