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dc.contributor.authorFerrazzi, Fulviaen_US
dc.contributor.authorSebastiani, Paolaen_US
dc.contributor.authorRamoni, Marco Fen_US
dc.contributor.authorBellazzi, Riccardoen_US
dc.date.accessioned2012-01-11T15:51:11Z
dc.date.available2012-01-11T15:51:11Z
dc.date.copyright2007
dc.date.issued2007-5-24
dc.identifier.citationFerrazzi, Fulvia, Paola Sebastiani, Marco F Ramoni, Riccardo Bellazzi. "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks" BMC Bioinformatics 8(Suppl 5):S2. (2007)
dc.identifier.issn1471-2105
dc.identifier.urihttps://hdl.handle.net/2144/3067
dc.description.abstractBACKGROUND. 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. RESULTS. 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. CONCLUSION. 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.en_US
dc.description.sponsorshipItalian Ministry of University and Scientific Research; National Institutes of Health & National Human Genome Research Institute (HG003354-01A2); Collegio Ghislieri, Pavia Italy fellowshipen_US
dc.language.isoen
dc.publisherBioMed Centralen_US
dc.rightsCopyright 2007 Ferrazzi et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.titleBayesian Approaches to Reverse Engineer Cellular Systems: A Simulation Study on Nonlinear Gaussian Networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/1471-2105-8-S5-S2
dc.identifier.pmid17570861
dc.identifier.pmcid1892090


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Copyright 2007 Ferrazzi et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright 2007 Ferrazzi et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.