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

Background: 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 evalua...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Fulvia Ferrazzi, Paola Sebastiani, Marco Ramoni, Riccardo Bellazzi
• Keywords: BMCBI 2007, Cellular Systems, Cellular Variables, Gaussian Models