Journal Article

Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler

Marco Grzegorczyk, Dirk Husmeier, Kieron D. Edwards, Peter Ghazal and Andrew J. Millar

in Bioinformatics

Volume 24, issue 18, pages 2071-2078
Published in print September 2008 | ISSN: 1367-4803
Published online July 2008 | e-ISSN: 1460-2059 | DOI:
Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler

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Method: The objective of the present article is to propose and evaluate a probabilistic approach based on Bayesian networks for modelling non-homogeneous and non-linear gene regulatory processes. The method is based on a mixture model, using latent variables to assign individual measurements to different classes. The practical inference follows the Bayesian paradigm and samples the network structure, the number of classes and the assignment of latent variables from the posterior distribution with Markov Chain Monte Carlo (MCMC), using the recently proposed allocation sampler as an alternative to RJMCMC.

Results: We have evaluated the method using three criteria: network reconstruction, statistical significance and biological plausibility. In terms of network reconstruction, we found improved results both for a synthetic network of known structure and for a small real regulatory network derived from the literature. We have assessed the statistical significance of the improvement on gene expression time series for two different systems (viral challenge of macrophages, and circadian rhythms in plants), where the proposed new scheme tends to outperform the classical BGe score. Regarding biological plausibility, we found that the inference results obtained with the proposed method were in excellent agreement with biological findings, predicting dichotomies that one would expect to find in the studied systems.

Availability: Two supplementary papers on theoretical (T) and experi-mental (E) aspects and the datasets used in our study are available from


Journal Article.  6139 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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