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

• Published in: Statistical applications in genetics and molecular biology Vol. 11; no. 4
• Main Authors: Grzegorczyk, Marco, Husmeier, Dirk
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 12.07.2012
• Subjects: Algorithms; Arabidopsis - genetics; Bayes Theorem; Bayesian multiple changepoint processes; Bayesian regularization; Computational Biology - methods; Computational Biology - statistics & numerical data; Gene Expression Profiling - methods; Gene Expression Profiling - statistics & numerical data; Gene Expression Regulation, Fungal; Gene Expression Regulation, Plant; gene regulatory networks; Gene Regulatory Networks - physiology; Genetic Heterogeneity; Models, Statistical; non-stationary gene expression time series; Oligonucleotide Array Sequence Analysis; reversible jump Markov chain Monte Carlo; Saccharomyces cerevisiae - genetics; Synthetic Biology - methods; Synthetic Biology - statistics & numerical data; Systems Biology - methods; Systems Biology - statistics & numerical data; time varying dynamic Bayesian networks
• ISSN: 1544-6115; 1544-6115
• DOI: 10.1515/1544-6115.1761

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.