title: Reconstruction of large-scale gene regulatory networks using Bayesian model averaging.
abstract: Gene regulatory networks provide the systematic view of molecular interactions in a complex living system. However, constructing large-scale gene regulatory networks is one of the most challenging problems in systems biology. Also large burst sets of biological data require a proper integration technique for reliable gene regulatory network construction. Here we present a new reverse engineering approach based on Bayesian model averaging which attempts to combine all the appropriate models describing interactions among genes. This Bayesian approach with a prior based on the Gibbs distribution provides an efficient means to integrate multiple sources of biological data. In a simulation study with maximum of 2000 genes, our method shows better sensitivity than previous elastic-net and Gaussian graphical models, with a fixed specificity of 0.99. The study also shows that the proposed method outperforms the other standard methods for a DREAM dataset generated by nonlinear stochastic models. In brain tumor data analysis, three large-scale networks consisting of 4422 genes were built using the gene expression of non-tumor, low and high grade tumor mRNA expression samples, along with DNA-protein binding affinity information. We found that genes having a large variation of degree distribution among the three tumor networks are the ones that see most involved in regulatory and developmental processes, which possibly gives a novel insight concerning conventional differentially expressed gene analysis.
authors.affiliation: Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College London, London SW72AZ, UK. haseong.kim08@imperial.ac.uk
authors.forename: Haseong
authors.initials: H
authors.lastname: Kim
authors.forename: Erol
authors.initials: E
authors.lastname: Gelenbe
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000378:metabolism; D001923:Brain Chemistry / Q000235:genetics; D001932:Brain Neoplasms / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D013269:Stochastic Processes; D009626:Terminology as Topic