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This article is part of the supplement: Articles selected from posters presented at the Tenth Annual International Conference on Research in Computational Biology

Open Access Research

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks

Fulvia Ferrazzi12*, Paola Sebastiani3, Marco F Ramoni2 and Riccardo Bellazzi1

Author Affiliations

1 Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, via Ferrata 1, 27100 Pavia, Italy

2 Children's Hospital Informatics Program, Division of Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, 300 Longwood Avenue, Boston MA 02115, USA

3 Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston MA 02118, USA

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BMC Bioinformatics 2007, 8(Suppl 5):S2  doi:10.1186/1471-2105-8-S5-S2

Published: 24 May 2007



Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models.


We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time.


The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.