Research Interests

All organisms face competition from others, which drives the evolution of strategies to fight back and/or resist their weapons of attack. Bacteria are no exception to this rule. They possess a diverse array of weapons to kill other bacteria including the production of secreted antibiotics, direct injection of toxins into competitor cells, carrying viruses (bacteriophage) that they can release to infect competitors, and even deliberate provocation of our immune system to clear more susceptible competitors. The antibiotics that we use to treat bacterial infections were in fact isolated from other microbes. In this case we see the critical importance of understanding the evolution of bacterial attack and defence strategies in order to combat the evolution of antibiotic resistance.

Work in my lab focuses on the ecology and evolution of these attack and defence strategies in bacteria to unravel why bacteria use particular mechanisms of attack and defence and the consequences of these strategies for their communities. We take a multidisciplinary approach to these problems, using a combination of mathematical models, experiments, and statistical analysis of large-scale bioinformatic and epidemiological datasets. Some current projects are:

  • How does competition shape the spatial structure of microbial communities? Bacteria live in highly structured communities, where certain species or strains will either be found close together or spatially separated. We are using a combination of mathematical modelling and experiments with the pathogen Vibrio cholerae to understand how the competitive strategies bacteria use affect this spatial organisation of their communities, and how this in turn affects community stability and future social evolution.
  • What determines the rate of emergence of antibiotic resistance? While bacteria appear to inevitably evolve resistance to any antibiotics we use to kill them, how quickly this happens varies hugely across antibiotics and bacterial species. We are using mathematical and statistical modelling of large scale clinical and epidemiological datasets to understand why resistance evolution is sometimes quick and others slow, and also trying to identify sequences of antibiotic treatments that minimise total resistance evolution.
  • How will bacteria evolve in response to manipulation of the microbiome? Modifying to composition of the beneficial bacteria that live inside us – our ‘microbiome’ – offers great promise in allowing us to fight infections by introducing competitors to repel pathogens. However, will pathogens evolve to resist these treatments just like they have for antibiotics? We are using a combination of mathematical modelling and bioinformatic analysis to help predict and counteract the mechanisms pathogens could use to resist these treatments.