Objective The overall goal of this multidisciplinary project is to combine functional genomics and computational modelling into a novel integrative systems approach aimed at identifying key components of the regulatory networks involved in cell physiology. The proposal aims to develop a computational framework based on a probabilistic modelling technique (Bayesian state-space models), within the context of real-world scientific problems.In this project we propose innovative directions to significantly extend this network modelling approach, incorporating into the model learning and inference process nonlinearities that reflect the underlying biological mechanisms and prior knowledge in the form of known connections. In the first phase of this research, the focus will be to develop the computational framework to effectively model the temporal gene expression profiles of a subset of genes derived from differential expression profiling. Fields of science natural sciencesbiological sciencesgeneticsnatural sciencescomputer and information sciencesdatabasesnatural sciencesbiological sciencesmicrobiologybacteriologynatural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statisticsnatural sciencescomputer and information sciencesartificial intelligencemachine learning Keywords Bayesian methods Kalman filtering gene regulatory networks genomics metabolomics microarrays proteomics state-space models Programme(s) FP6-MOBILITY - Human resources and Mobility in the specific programme for research, technological development and demonstration "Structuring the European Research Area" under the Sixth Framework Programme 2002-2006 Topic(s) MOBILITY-4.2 - Marie Curie International Reintegration Grants (IRG) Call for proposal FP6-2004-MOBILITY-12 See other projects for this call Funding Scheme IRG - Marie Curie actions-International re-integration grants Coordinator UNIVERSITY OF WARWICK EU contribution No data Address Gibbet Hill Road COVENTRY United Kingdom See on map Links Website Opens in new window Total cost No data
