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Euphytica

, Volume 183, Issue 3, pp 361–377 | Cite as

Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series

  • Frank Dondelinger
  • Dirk HusmeierEmail author
  • Sophie Lèbre
Article

Abstract

To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level. The advent of high-throughput postgenomic technologies has spurred substantial interest in reverse engineering these networks from data, and several techniques from machine learning and multivariate statistics have recently been proposed. The present article discusses the problem of inferring gene regulatory networks from gene expression time series, and we focus our exposition on the methodology of Bayesian networks. We describe dynamic Bayesian networks and explain their advantages over other statistical methods. We introduce a novel information sharing scheme, which allows us to infer gene regulatory networks from multiple sources of gene expression data more accurately. We illustrate and test this method on a set of synthetic data, using three different measures to quantify the network reconstruction accuracy. The main application of our method is related to the problem of circadian regulation in plants, where we aim to reconstruct the regulatory networks of nine circadian genes in Arabidopsis thaliana from four gene expression time series obtained under different experimental conditions.

Keywords

Gene regulatory networks Reverse engineering Dynamic Bayesian networks Data integration Information sharing Arabidopsis thaliana Circadian regulation 

Notes

Acknowledgments

This study was supported by the Scottish Government Rural and Environment Research and Analysis Directorate (RERAD). Frank Dondelinger’s PhD research is partly funded by the Engineering and Physical Sciences Research Council (EPSRC).

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Frank Dondelinger
    • 1
  • Dirk Husmeier
    • 1
    Email author
  • Sophie Lèbre
    • 2
  1. 1.Biomathematics and Statistics ScotlandEdinburghUK
  2. 2.Université de Strasbourg, LSIIT - UMR 7005IllkirchFrance

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