School of Computing and Mathematics
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- Liz Aston
Dr Liz Aston
|Title:||Postdoctoral Research Associate|
|Phone:||+44 (0)1782 733593|
|Location:||Colin Reeves 141|
I have a background in biology and computer science, obtaining both a BSc in Biological Science and an MSc in Computer Science from the University of Birmingham. I then moved to Keele University where I completed my PhD entitled: 'Critical mutation rates in small populations'. I was supervised by Dr Alastair Channon, with Dr Charles Day as second supervisor, and funded as part of a £500k EPSRC project on information dynamics in evolutionary systems which included members from Manchester, Middlesex and Warwick Universities. Members of the project team subsequently obtained £580k funding from the BBSRC for a project on the theory and practice of evolvability: effects and mechanisms of mutation rate plasticity, with which I undertook a one year postdoc position. Following this, the team were successful in obtaining further funding from the BBSRC for a project on adaptive landscapes of antibiotic resistance: population size and 'survival-of-the-flattest'. With this funding I started a three year postdoc in August 2015 working as a Researcher Co-Investigator.
My main research interest and focus of my PhD and current postdoc is the relationship between critical mutation rates and the size of an evolving population in silico. Specifically, as part of my PhD and contrary to previous studies, I demonstrated that in the case of small populations and a simple two-peak fitness landscape, there is an exponential relationship between population size and the critical mutation rate at which individuals more robust to mutation outcompete individuals that are fitter but less robust ('survival-of-the-flattest'). I have already demonstrated that this relationship is maintained when moving from a haploid to a diploid population. I am currently working on bridging the gap between artificial and biological evolution by developing the model to focus on the small population of a microbe with a newly arisen antibiotic resistance mutation. My work in silico is being carried out alongside laboratory experiments and the development of theory at Manchester and Middlesex respectively as part of the BBSRC-funded interdisciplinary project on adaptive landscapes of antibiotic resistance: population size and 'survival-of-the-flattest'.
Full Publications List show
Critical mutation rate has an exponential dependence on population size. In T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo & R. Doursat (Eds.). Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (pp. 117-124). Heidelberg: MIT Press. doi> full text>2011.
Theory and practice of optimal mutation rate control in hamming spaces of DNA sequences. In T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo & R. Doursat (Eds.). Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (pp. 85-92). Heidelberg: MIT Press. doi> full text>2011.