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A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

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  • Grzegorczyk Marco

    (Department of Statistics, TU Dortmund University)

  • Husmeier Dirk

    (School of Mathematics and Statistics, University of Glasgow, and Biomathematics and Statistics Scotland (BioSS), Edinburgh)

Abstract

An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.

Suggested Citation

  • Grzegorczyk Marco & Husmeier Dirk, 2012. "A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-62, July.
  • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:4:n:7
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    References listed on IDEAS

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    1. Makram Talih & Nicolas Hengartner, 2005. "Structural learning with time‐varying components: tracking the cross‐section of financial time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 321-341, June.
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    Cited by:

    1. Aderhold Andrej & Husmeier Dirk & Grzegorczyk Marco, 2014. "Statistical inference of regulatory networks for circadian regulation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 1-47, June.
    2. Azzimonti, Laura & Corani, Giorgio & Zaffalon, Marco, 2019. "Hierarchical estimation of parameters in Bayesian networks," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 67-91.
    3. Liang Yulan & Kelemen Arpad, 2016. "Bayesian state space models for dynamic genetic network construction across multiple tissues," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 273-290, August.
    4. Grzegorczyk Marco & Aderhold Andrej & Husmeier Dirk, 2015. "Inferring bi-directional interactions between circadian clock genes and metabolism with model ensembles," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(2), pages 143-167, April.

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