Dr V A Smith, Dr J Mitchell
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
BBSRC Theme: Industrial Biotechnology and Bioenergy
This studentship uses bioinformatics, systems biology, and machine learning approaches to probe the robustness and fragility of microbial metabolic networks. Understanding these network features will have impacts across bioscience, in particular for understanding the stability of microbes in industrial bioreactors and/or used for biofuel production with regards to their desired output, how they respond to changes in culture environment, and how they would respond to synthetic biology modifications which co-opt or otherwise interact with their metabolism.
Our increasing understanding of the complex interactions between proteins and small organic molecules allows us to decipher metabolic networks where enzyme-catalysed reactions link together substrates and products to form pathways and cycles (Nigsch & Mitchell 2008; Holliday et al 2011). Network inference techniques can be combined with prior knowledge to develop a working structure of functional metabolic networks (Smith 2010). You will use data on the interaction between proteins and small organic molecules to decipher metabolic networks. You will work with bioinformatics data to trace to both the variation of networks across different species and also the networks’ evolution. We already have software that simulates metabolism’s evolution; you will apply these simulations to work backwards in time and suggest plausible evolutionary trajectories.
Ultimately, you will develop predictions of perturbations that would disrupt metabolic networks, and those which would have little effect. You will characterise the architecture and robustness or fragility of systems across both biological species and time. Likely future applications include the use of synthetic biology to maximise bioreactor production of industrial products and biofuels.
You will obtain training in bioinformatics, systems biology, modelling, and machine learning, as well as a working knowledge of microbial metabolic networks.
You will be jointly supervised by Dr V Anne Smith (Biology) and Dr John Mitchell (Chemistry). Both groups work in computational systems biology and machine learning, with Dr Smith’s research concentrating on network analysis and Dr Mitchell’s on enzymes and computational chemistry. For more information on their research please visit:
Dr V Anne Smith’s research pages: http://biology.st-andrews.ac.uk/vannesmithlab/
Dr John Mitchell’s research pages: http://chemistry.st-andrews.ac.uk/staff/jbom/group/
Please direct informal enquiries to [Email Address Removed].
Funding Notes
This project is eligible for the EASTBIO Doctoral Training Partnership: http://www.eastscotbiodtp.ac.uk/
This opportunity is only open to UK nationals (or EU students who have been resident in the UK for 3+ years immediately prior to the programme start date) due to restrictions imposed by the funding body.
Apply by 5.00 pm on 7 May 2018 following the instructions on how to apply at: http://www.eastscotbiodtp.ac.uk/how-apply-0
Informal inquiries to the primary supervisor are very strongly encouraged.
References
Holliday GL, Fischer JD, Mitchell JBO & Thornton JM (2011) Characterizing the complexity of enzymes on the basis of their mechanisms and structures with a bio-computational analysis. FEBS J, 278, 3835-3845
Nigsch F & Mitchell JBO (2008) Toxicological Relationships Between Proteins Obtained from Protein Target Predictions of Large Toxicity Databases. Toxicology and Applied Pharmacology, 231, 225-234.
Smith VA (2010) Revealing structure of complex biological systems using Bayesian networks. In: Network Science: Complexity in Nature and Technology (Estrada E, Fox M, Higham DJ , Oppo G-L, eds; Springer) pp 185-204.
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