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.
Keywords
- Regulatory Network
- Gene Duplication
- Inference Algorithm
- Transcriptional Regulatory Network
- Adjacency Matrice
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zhang, X., Moret, B.M.E. (2009). Improving Inference of Transcriptional Regulatory Networks Based on Network Evolutionary Models. In: Salzberg, S.L., Warnow, T. (eds) Algorithms in Bioinformatics. WABI 2009. Lecture Notes in Computer Science(), vol 5724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04241-6_34
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DOI: https://doi.org/10.1007/978-3-642-04241-6_34
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