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Advances in Bioinformatics
Volume 2010 (2010), Article ID 749848, 17 pages
Research Article

Modelling Nonstationary Gene Regulatory Processes

1Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany
2Biomathematics and Statistics Scotland, JCMB, The King's Buildings, Edinburgh EH93JZ, UK

Received 18 December 2009; Accepted 29 April 2010

Academic Editor: Yves Van de Peer

Copyright © 2010 Marco Grzegorcyzk 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.


An important objective in systems biology is to infer gene regulatory networks from postgenomic data, and dynamic Bayesian networks have been widely applied as a popular tool to this end. The standard approach for nondiscretised data is restricted to a linear model and a homogeneous Markov chain. Recently, various generalisations based on changepoint processes and free allocation mixture models have been proposed. The former aim to relax the homogeneity assumption, whereas the latter are more flexible and, in principle, more adequate for modelling nonlinear processes. In our paper, we compare both paradigms and discuss theoretical shortcomings of the latter approach. We show that a model based on the changepoint process yields systematically better results than the free allocation model when inferring nonstationary gene regulatory processes from simulated gene expression time series. We further cross-compare the performance of both models on three biological systems: macrophages challenged with viral infection, circadian regulation in Arabidopsis thaliana, and morphogenesis in Drosophila melanogaster.