Chemical and Process Engineering
Systems Biology is an emerging discipline combining quantitative experimental techniques and mathematical modelling; it has been expanding at a tremendous pace over the last decade, with excellent opportunities both in academic and industrial research. Systems Biology is a key priority area for research at Sheffield. Drawing on the strength of Sheffield’s engineering and biology (both leaders nationally and internationally), a series of activities have been initiated recently including regular seminar and journal club series and Roberts training for young researchers.
A fully funded new route (4 year) PhD studentship is available starting in October 2008 as part of the ChELSI centre (http://www.shef.ac.uk/chelsi/). The project will be carried out under the supervision of Dr G Sanguinetti (co-supervisor Prof P.C. Wright) and will be mostly centred on mathematical modelling, but will involve some training in experimental techniques. The successful candidate will be expected to have a good first degree in a numerate subject (e.g. engineering, physics, mathematics, computer science) and an interest in biology and biotechnological applications. The student will be based in the Department of Chemical and Process Engineering, but it is anticipated that the student will interact substantially with staff at the Department of Computer Science, Automatic Control and Systems Engineering and Molecular Biology and Biotechnology. The following two projects are available; please state clearly in your application which project you are interested in.
Network based integration of high throughput biological data. Technological advances in molecular biology have led to the production of huge amounts of high-throughput data. These data sets describe the global behaviour of cellular products at various levels of organisation, genomic, transcriptomic, proteomic and metabolomic. While each of these reveal important characteristics of cellular processes at a specific level, it is not always clear how the information can be pieced together into a meaningful, systems level picture. Recently, we have proposed a probabilistic model which integrated
metabolic network information with high-throughput proteomic to identify pathways connected with hydrogen production in bacteria (Bioinformatics, 24(7): 1078-1085). In this project you will build on this approach to produce models capable of integrating data at various levels of organisation. This approach will be applied to the problem of hydrogen production in bacteria, with potential for important applications in synthetic biology and sustainable biofuels research. The mathematical tools required for the project will be centred on probability theory and statistical machine learning.
Control theory of bacterial metabolism. Bacterial responses to environmental changes often have important metabolic consequences. A case of particular interest involves the bacterial genus Nostoc which in certain conditions releases hydrogen which can then potentially be used as fuel. The dynamics of the response to changed conditions depends on proteins called transcription factors mediating the external signal and turning on and off enzymes that catalyse the metabolic reactions. Unfortunately, accurate measurement of these transcription factor activities is unfeasible. We recently have proposed models to perform statistical inference of transcription factors activity (Bioinformatics, 22(22), NIPS 19) . In this project you will turn these approaches into a control problem, where the goal is not only the inference of the transcription factor activities but how we can engineer the system to maximise the hydrogen yield. The mathematical tools involved in this prohect will be centred on ordinary differential equations, stochastic processes and control theory.
Please address enquiries to Mrs A Fidler:
Email: a.fidler@sheffield.ac.uk
Tel: +44 (0)114 2227557
Applications in writing to Mrs A Fidler (details above)
Closing date for applications: 8th August 2008
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