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Integration of Epigenetic Data in Bayesian Network Modeling of Gene Regulatory Network

  • Jie Zheng
  • Iti Chaturvedi
  • Jagath C. Rajapakse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)

Abstract

The reverse engineering of gene regulatory network (GRN) is an important problem in systems biology. While gene expression data provide a main source of insights, other types of data are needed to elucidate the structure and dynamics of gene regulation. Epigenetic data (e.g., histone modification) show promise to provide more insights into gene regulation and on epigenetic implication in biological pathways. In this paper, we investigate how epigenetic data are incorporated into reconstruction of GRN. We encode the histone modification data as prior for Bayesian network inference of GRN. Bayesian framework provides a natural and mathematically tractable way of integrating various data and knowledge through its prior. Applying to the gene expression data of yeast cell cycle, we demonstrate that integration of epigenetic data improves the accuracy of GRN inference significantly. Furthermore, fusion of gene expression and epigenetic data shed light on the interactions between genetic and epigenetic regulations of gene expression.

Keywords

Bayesian networks gene regulatory network epigenetics histone modification priors gene expression yeast cell cycle 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jie Zheng
    • 1
  • Iti Chaturvedi
    • 1
  • Jagath C. Rajapakse
    • 1
    • 2
    • 3
  1. 1.Bioinformatics Research Centre, School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Singapore-MIT AllianceSingapore

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