BSc, MSc, PhD
In the following table, contact information relevant to the page. The first column is for visual reference only. Data is in the right column.
|Organisation:||University of Exeter|
|Tags:||Fellowship: Established Career, Fellowship: Previous Fellow, Researcher, University of Exeter|
|Related theme:||Global uncertainties Healthcare technologies LWEC Mathematical sciences|
- BSc Mathematics, 1st Class Edinburgh Uni 1994
- MSc Nonlinear Mathematics (EPSRC funded) Bath Uni 1995
- Project Engineer, SEA Ltd, Somerset, 1996
- PhD Electrical Engineering Brunel University 1997-2000
- EPSRC PDRA Mechanical Engineering Bath, 2000
- Lecturer in Mathematics, Bristol Uni, 2000
- Lecturer, SL, Reader in Mathematics, Imperial College, 2000-2011
- Professor in Mathematical Biosciences, Exeter Uni, 2011-present
The original purpose of the fellowship was to create an evolutionary biology laboratory where we could use experimental systems, computational whole-genome sequencing approaches and single-cell assays to test mathematical predictions about rapid evolution, particularly as that term relates to antibiotic resistance in bacteria and fungi. The main idea was to understand whether any ideas from control theory could be applied to what is essentially a 'human behavioural problem', namely how, exactly, should we use antibiotics to treat disease and yet also mitigate resistance? Even professional scientists are not, perhaps, aware of just how fast Darwinian evolution can occur in microbial species but it is exactly this rapidity that allows mathematics to play a role in understanding the datasets we generate to probe this question.
The fellowship is now into its final year and many of the original goals have been achieved, indeed several results have been published in leading biological journals, including Nature, but we are now using the funding to be much more ambitious in terms of the problems we tackle. For example, as a group of physical scientists, we are developing novel culture devices in the lab to study the effect antibiotics have on spatially structured microbial communities consisting of several species, all supported by our data-driven, mathematical approach. In fact, almost all of the hypothesis that we test are the result of some mathematical idea, calculation or computation.
I was awarded the fellowship to set up an evolutionary biology laboratory in which mathematical theories of optimal antibiotic use could be tested. Although much is of course known about the genetics of antibiotic resistance, it is important to generate and analyse novel datasets that elucidate the evolutionary processes that lead to resistance so that we can better understand how to deal with it.