Improving Inference of Transcriptional Regulatory Networks Based on Network Evolutionary Models

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]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[2]  D. Hillis.  Approaches for Assessing Phylogenetic Accuracy , 1995 .

[3]  Anton Crombach,et al.  Evolution of Evolvability in Gene Regulatory Networks , 2008, PLoS Comput. Biol..

[4]  Bengt Sennblad,et al.  Gene tree reconstruction and orthology analysis based on an integrated model for duplications and sequence evolution , 2004, RECOMB.

[5]  Lars Arvestad,et al.  Evolution after gene duplication: models, mechanisms, sequences, systems, and organisms. , 2007, Journal of experimental zoology. Part B, Molecular and developmental evolution.

[6]  Satoru Miyano,et al.  Identification of Genetic Networks from a Small Number of Gene Expression Patterns Under the Boolean Network Model , 1998, Pacific Symposium on Biocomputing.

[7]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[8]  Rita Casadio,et al.  Algorithms in Bioinformatics, 5th International Workshop, WABI 2005, Mallorca, Spain, October 3-6, 2005, Proceedings , 2005, WABI.

[9]  Satoru Miyano,et al.  Inferring gene networks from time series microarray data using dynamic Bayesian networks , 2003, Briefings Bioinform..

[10]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[11]  R. Page,et al.  From gene to organismal phylogeny: reconciled trees and the gene tree/species tree problem. , 1997, Molecular phylogenetics and evolution.

[12]  David Sankoff,et al.  Improving Gene Network Inference by Comparing Expression Time-series across Species, Developmental Stages or Tissues , 2004, J. Bioinform. Comput. Biol..

[13]  A. Regev,et al.  Conservation and evolvability in regulatory networks: the evolution of ribosomal regulation in yeast. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[14]  David J. Galas,et al.  A duplication growth model of gene expression networks , 2002, Bioinform..

[15]  Nicola J. Rinaldi,et al.  Transcriptional regulatory code of a eukaryotic genome , 2004, Nature.

[16]  R. Shamir,et al.  A fast algorithm for joint reconstruction of ancestral amino acid sequences. , 2000, Molecular biology and evolution.

[17]  Manolis Kellis,et al.  Reliable prediction of regulator targets using 12 Drosophila genomes. , 2007, Genome research.

[18]  Luonan Chen,et al.  Inferring transcriptional regulatory networks from high-throughput data , 2007, Bioinform..

[19]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.

[20]  Saurabh Sinha,et al.  Evolution of Regulatory Sequences in 12 Drosophila Species , 2009, PLoS genetics.

[21]  S. Teichmann,et al.  Evolutionary dynamics of prokaryotic transcriptional regulatory networks. , 2006, Journal of molecular biology.

[22]  Paul P. Wang,et al.  Advances to Bayesian network inference for generating causal networks from observational biological data , 2004, Bioinform..

[23]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[24]  Kevin P. Murphy,et al.  Learning the Structure of Dynamic Probabilistic Networks , 1998, UAI.

[25]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[26]  Manolis Kellis,et al.  Systematic discovery and characterization of fly microRNAs using 12 Drosophila genomes. , 2007, Genome research.

[27]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[28]  S. Teichmann,et al.  Gene regulatory network growth by duplication , 2004, Nature Genetics.

[29]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[30]  M. Gerstein,et al.  Structure and evolution of transcriptional regulatory networks. , 2004, Current opinion in structural biology.

[31]  Xiuwei Zhang,et al.  Boosting the Performance of Inference Algorithms for Transcriptional Regulatory Networks Using a Phylogenetic Approach , 2008, WABI.

[32]  Catherine C. McGeoch.  Experimental algorithmics , 2007, CACM.

[33]  Tandy J. Warnow,et al.  Reconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics , 2000, Experimental Algorithmics.

[34]  Dannie Durand,et al.  A hybrid micro-macroevolutionary approach to gene tree reconstruction. , 2006 .