Understanding the emergence and spread of antimicrobial resistance is one of the grand challenges for global human health, but it is also a fascinating biological problem, concerning the evolution of populations experiencing complex environmental conditions.
My research combines experimental, mathematical, and bioinformatic approaches to predict how and when bacteria will evolve antibiotic resistance under biologically-realistic growth conditions. The aim of this research is to develop a rational framework for designing combination therapies to suppress resistance evolution and regain usefulness of antibiotics where resistance has become wide-spread. This work is crucial for establishing combination therapies as a viable solution to the antibiotic resistance crisis. The project is funded through a UKRI Innovation Fellowship, involving collaborations with Tobias Galla, Chris Knight, and Simon Lovell.
I am also 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 am also collaborating with Rok Krašovec on the effects of mutation rate plasticity on the evolutionary genomics of E. coli.
Other projects I’ve been involved with on include genomics and bioinformatics of antibiotic resistance evolution, e.g. 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, I used rifampicin resistant Pseudomonas 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 (gcsR). PA2449 is a transcriptional regulator involved in glycine and serine metabolism.
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 just two major changes in fitness, on average.