PhD studentship in Bayesian Methods in Bioinformatics Warwick Systems Biology Centre
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.
Closing date: Please send a CV; a letter detailing your expertise, experience and interest; copies of any degree certificates; and contact details of at least two referees to: Dr. B. W. Kiernan, Warwick Systems Biology Centre, Coventry House, University of Warwick, Coventry, UK CV4 7AL (b.w.kiernan@ warwick.ac. uk) by the 30th March 2007. If English is not your first language, please also detail your English language qualifications, or similar experience.
*Please quote job vacancy reference number WSB01-017.*
*The closing date for applications is 31 March 2007.*
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