About the Project
The supervisory team for this PhD project will be:
Dr Nicholas Priest (Department of Biology & Biochemistry and the Milner Centre for Evolution)
Dr Tiffany Taylor (Department of Biology & Biochemistry and the Milner Centre for Evolution)
Prof Michael Tipping (Department of Mathematical Sciences and the Institute for Mathematical Innovation)
The design principles underlying life are obscure. Though it is generally assumed that organismal diversity is driven by adaptation, recent work indicates that non-adaptive processes have a critical role in the evolution of the gene regulatory networks which guide the formation of complex lifeforms. But, little attention has been placed on how genetic structure differs between biological systems evolved through adaptive and non-adaptive processes.
Using computational methods informed by biological data, this PhD studentship will identify how adaptive and non-adaptive processes contribute to the evolution of gene regulatory networks. The project will generate simulations of gene regulatory network evolution using standard gene regulatory network paradigms. From these results, it will develop methodologies in machine learning to train computers to characterize patterns of gene networks which contribute to adaptive and non-adaptive processes. The student on the project will have the opportunity to contribute to studies of experimental evolution and genomics in our well-established Pseudomonas system. And, employing the University’s Balena system (a massively parallel architecture, high-performance computing platform), the student will employ the methodologies developed in the first part of the PhD to assess whether novel mutations affecting gene regulation in Pseudomonas are adaptive or non-adaptive. The overarching goal is to uncover the origin of complexity in genetic circuits.
This project represents a collaboration between members of the Milner Centre for Evolution and the Institute of Mathematical Innovation. The studentship offers interdisciplinary training and research. It combines the fields or computation biology and evolutionary genetics. It will employ mathematical methods, in silico modelling, network theory and machine learning along with laboratory methods in microbiology and genomics.
Applicants must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree (or the equivalent qualifications gained outside the UK) in an appropriate area of science or technology with substantial mathematical content. A master’s level qualification would also be advantageous.
Informal enquiries should be directed to Dr Nicholas Priest ([Email Address Removed]).
Formal applications should be made via the University of Bath’s online application form for a PhD in Biology:
https://www.bath.ac.uk/samis/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUBB-FP02&code2=0012
More information about applying for a PhD at Bath may be found here:
http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/
Anticipated start date: 1 October 2018.
Funding Notes
Some Research Council funding is available on a competition basis to Home and EU students who have been resident in the UK for 3 years prior to the start of the project. For more information on eligibility, see: https://www.epsrc.ac.uk/skills/students/help/eligibility/.
Funding will cover Home/EU tuition fees, a stipend (£14,553 per annum for 2017/18) and a training support fee of £1,000 per annum for 3.5 years. Early application is strongly recommended.
Applicants classed as Overseas for tuition fee purposes are NOT eligible for funding; however, we welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.
References
Azevedo, R. B. R., Lohaus, R., Srinivasan, S., Dang, K. K. & Burch, C. L., 2006. Sexual reproduction selects for robustness and negative epistasis in artificial gene networks. Nature 440, pp. 87–90.
Lynch, M. 2007. The evolution of genetic networks by non-adaptive processes. Nature Reviews Genetics, 8 pp. 803-813.
Taylor, T. B., Mulley, G., Dills, A. H., Alsohim, A. S., McGuffin, L. J., Studholme, D. J., Silby, M. W.,Brockhurst, M. A., Johnson, L. J. and Jackson, R. W., 2015. Evolutionary resurrection of flagellar motility via rewiring of the nitrogen regulation system. Science 347, pp. 1014-1017.
Taylor, T. B., Mulley, G., McGuffin, L. J., Johnson, L. J., Brockhurst, M. A., Arseneault, T., Silby, M. W. andJackson, R. W., 2015. Evolutionary rewiring of bacterial regulatory networks. Microbial Cell Factories 2, pp. 256-258.
Wang, Y., Lan, Y., Weinreich, D., Priest, N. and Bryson, J., 2015. Recombination Is Surprisingly Constructive for Artificial Gene Regulatory Networks in the Context of Selection for Developmental Stability. In: The 13th European Conference on Artificial Life, 2015-07-20 - 2015-07-24.
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