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
- engineering and technologyindustrial biotechnologymetabolic engineering
- natural sciencescomputer and information sciencesdatabases
- natural sciencesbiological sciencesmicrobiologybacteriology
- natural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statistics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Call for proposal
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