EastBio: Modelling stochastic emergence of antibiotic resistance in bacterial populations.

Bacterial evolution of antibiotic resistance within patients can compromise the efficacy of antibiotic treatment and represents a serious threat to public health. However, outcomes vary among patients and even in controlled lab populations of bacteria. This suggests that the emergence of antibiotic resistance is subject to stochastic effects within the bacterial population, in both the occurrence of mutations and their chances of establishment (i.e. survival and growth to large numbers).

The emergence of resistance occurs in a complex environment that changes over time. Abiotic factors, such as nutrient availability and antibiotic dose, impact cell division and death rates. Some antibiotics induce stress responses that increase mutation rates. Importantly, bacteria also feed back on their own environment, e.g. by consuming nutrients, secreting metabolites, and degrading antibiotics. This implies that the environment faced by nascent resistant lineages is strongly shaped by the surrounding bacterial population.

Experiments from our lab show that the presence of sensitive bacteria can either increase or reduce the probability that a resistant cell establishes a surviving lineage under different conditions. The interacting factors driving these effects are presently unclear.

To address this knowledge gap, this PhD project aims to:

• Develop mathematical models describing bacterial population dynamics under antibiotic treatment, incorporating bacterial interactions with environmental factors. We anticipate using ordinary differential equations, stochastic birth-death processes, and stochastic simulations.

• Implement computational frameworks to solve model equations and calculate the probability that resistance emerges under different environmental conditions and timings of appearance.

• Compare model predictions to experimental data generated in our lab: specifically, the estimated establishment probability of resistant cells (ref.1).

• Use the model to explore optimal antibiotic dosing patterns that reduce the probability of resistance emerging (cf. ref.2).

The ideal candidate for this interdisciplinary PhD will have strong mathematical/computational skills, e.g. a degree in Mathematics, Physics, or another discipline with substantial mathematical training and experience with coding. Extensive biological knowledge is not required at the outset, but the candidate should be motivated to learn, to answer biological research questions, and to interact with both theoretical and experimental researchers.

The PhD programme will provide training in mathematical modelling, computational methods (including reproducible coding), and data analysis, through supervisor guidance and/or formal courses. The candidate will also gain skills in communicating with diverse academic teams and broader professional skills through EastBio training opportunities.