Recovering Genetic Regulatory Networks from Chromatin Immunoprecipitation and Steady-State Microarray Data
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Research Article Recovering Genetic Regulatory Networks from Chromatin Immunoprecipitation and Steady-State Microarray Data Wentao Zhao,1 Erchin Serpedin,1 and Edward R. Dougherty2 1 Electrical 2 The
and Computer Engineering Department, Texas A&M University, College Station, TX 77843, USA Translational Genomics Research Institute (TGen), 400 North Fifth Street, Suite 1600, Phoenix, AZ 85004, USA
Correspondence should be addressed to Erchin Serpedin, [email protected] Received 28 November 2007; Accepted 20 May 2008 Recommended by Z. Wang Recent advances in high-throughput DNA microarrays and chromatin immunoprecipitation (ChIP) assays have enabled the learning of the structure and functionality of genetic regulatory networks. In light of these heterogeneous data sets, this paper proposes a novel approach for reconstruction of genetic regulatory networks based on the posterior probabilities of gene regulations. Built within the framework of Bayesian statistics and computational Monte Carlo techniques, the proposed approach prevents the dichotomy of classifying gene interactions as either being connected or disconnected, thereby it reduces significantly the inference errors. Simulation results corroborate the superior performance of the proposed approach relative to the existing state-of-the-art algorithms. A genetic regulatory network for Saccharomyces cerevisiae is inferred based on the published real data sets, and biological meaningful results are discussed. Copyright © 2008 Wentao Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction Currently, one of the most important research problems in molecular biology and bioinformatics consists of finding out the mechanisms that govern the gene regulations, which are considered to play fundamental roles in the operation of all processes taking place in living cells. Learning the structure and machinery of gene regulations opens up the possibility for understanding and controlling the functioning of organisms at the molecular level, and for designing intelligent therapies and drugs. In a biological process such as cell cycle or environmental response, a gene’s product, the protein, can serve as a transcription factor of a target gene by binding to the target gene’s regulatory region on chromatin and affect its transcription. The protein can also influence another gene’s expression in subsequent stages, for example, through splicing or translation. Alternatively, these proteingene relationships can be viewed as gene-gene interactions, and are modeled in general as genetic regulatory networks. Recent years have witnessed a number of different frameworks for modeling genetic regulatory networks, ranging from fine-scale modeling at the molecular level in terms of partial differential equations and stochastic equations,
to large scale modeling at the gene and protein-level in terms of Bo
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