PhD studentship in Bayesian Methods in Bioinformatics, Warwick Systems Biology Centre

Maintenance grant of £12 283 pa plus academic fees
Duration: 3 years

You should have a good first degree in a relevant quantitative field such as theoretical physics, engineering, applied mathematics, statistics etc and a strong interest in molecular biology.

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 project 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. The second phase of this work will involve a tightly coupled iterative cycle of computational modelling and independent experimental validation of model predictions using over-expression and gene silencing experiments by collaborating groups. The new computational tools will be developed in the context of real-world scientific problems: the understanding of stress response in the model plant species Arabidopsis at a molecular level and metabolic regulation in the bacteria Streptomyces, major producers of antibiotics and bioactive products. These are well-defined systems of biological interest that can be easily manipulated experimentally and for which genomic information and the necessary investigative tools are available.

Warwick Systems Biology Centre has a thriving research and postgraduate training programme at the interface of the life sciences and the mathematical and physical sciences.

1. Beal, M.J., Falciani, F., Ghahramani, Z., Rangel C. and Wild, D.L. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics, 21: 349-356 (2005).
2. Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Sotheran, E., A., Gaiba, A.,.Wild, D.L. and Falciani, F. Modeling T-cell activation using gene expression profiling and state space models. Bioinformatics, 20(9):1361-1372 (2004).
3. Rangel C., Angus J., Ghahramani Z. and Wild D.L. Modeling genetic regulatory networks using gene expression profiling and state space models. In Husmeier, D., Roberts, S. and Dybowski, R. (Eds.) , Applications of Probabilistic Modelling in Medical Informatics and Bioinformatics. Springer Verlag, (2004), pp. 269-293.

Details of requirements can be found here:
http://www2.warwick.ac.uk/study/postgraduate/apply/

Applicants should apply online at:
http://www2.warwick.ac.uk/study/postgraduate/apply/process/

Informal enquiries: Prof. David Wild ( )
Closing date: Applications accepted all year round, start date asap
Please quote job vacancy reference number WSB01-017.

The closing date for applications is 31 January 2007.