In this project the student will develop next-generation methods for strain optimization and join the thriving multidisciplianry research ecosystem at Edinburgh. Growth of microorganisms under different conditions and with different carbon sources influences their biochemistry dramatically and has significant implications in industrial biotechnology, for example for the optimisation of culture or fermentation conditions. Metabolomics can be used to probe the global biochemistry of an organism, allowing insights into the biochemical pathways and mechanisms underlying an organism’s response to its environment. In this project the student will explore the effect of different carbon sources on E. coli and other organisms, developing a predictive model of their behaviour when challenged with a variety of culture conditions.
The successful applicant will: (i) develop and optimise methods to grow arrays of bacterial cultures and prepare them for metabolomics analysis, (ii), optimise liquid and gas chromatography – mass spectrometry methods for high throughput analysis of bacterial extracts (iii) build models of the effect of different culture conditions on bacterial biochemistry (iv) use the models to predict the effects of new conditions and then (v) test the models in real world situations.
KB will supervise the metabolomics and data collection, DO will supervise the machine learning and modelling aspects.
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van der Hooft, J. J.J., Alghefari, W., Watson, E., Everest, P. , Morton, F. R., Burgess, K. E.V. and Smith, D. G.E. (2018) Unexpected differential metabolic responses of Campylobacter jejuni to the abundant presence of glutamate and fucose. Metabolomics, 14(11), 144.
Zambelloni, R., Connolly, J. P.R., Huerta Uribe, A., Burgess, K., Marquez, R. and Roe, A. J. (2017) Novel compounds targeting the enterohemorrhagic Escherichia coli type three secretion system reveal insights into mechanisms of secretion inhibition. Molecular Microbiology, 105(4), pp. 606-619.
How good is research at University of Edinburgh in Biological Sciences?
FTE Category A staff submitted: 109.70
Research output data provided by the Research Excellence Framework (REF)
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