To be able to predict evolution, we must know something about the mutations that contribute to adaptive evolution. Having the ability to predict adaptive evolution is important when faced with threats like antibiotic resistant ‘super bugs’ or evolving zoonotic or opportunistic pathogens.
I study adaptive evolution using using microbes as a model system. At present, I am involved in a multi-disciplinary BBSRC project to study the adaptive landscapes of antibiotic resistance evolution. This project combines wet-lab experimental evolution with mathematical models and advanced computer simulations to predict how population size and mutational target size interact to shape resistance evolution.
I also have on-going collaborations studying the genomics and bioinformatics of antibiotic resistance evolution, including species-level differences in beta-lactam resistance evolvability (with Victoria Furio), chromosomal versus plasmid-borne beta-lactam resistance evolvability (with Alvaro San Millan), mutational robustness (with Karl Heilbron), effective population size and the repeatability of resistance evolution (with Tom Vogwill) and phage/bacteria co-evolution (with Alex Betts).
In my DPhil (PhD), I used rifampicin resistant P. aeruginosa adapting to use a non-preferred carbon substrate (L-serine) as a model system. Different resistance mutations seem to confer a different ability to adapt to this substrate, which may be due to interactions between rpoB and a gene called PA2449. PA2449 is a transcriptional regulator involved in glycine and serine metabolism, as well as pyocyanin biosynthesis.
In my MSc work, I studied compensatory adaptation of Aspergillus nidulans in a complex medium. Using a maximum likelihood-based model, I found that the number of major fitness jumps during adaptive walks is independent of starting fitness—populations undergo two major changes in fitness, on average. On-going research will determine whether the number of fitness increases corresponds to the number of mutations fixed.