What drives antibiotic resistance diversity? Modelling for MRSA control

Since the middle C20th, antibiotics have become our primary line of defence against bacterial infections. However, the occurrence of antimicrobial resistance has been dangerously increasing over the past decades, and this phenomenon is now recognised as a global public health threat. Methicilin-resistant Staphylococcus aureus (MRSA) is one of the most commonly encountered antimicrobial resistant bacteria, especially in healthcare settings. In order to control such a pathogen, it is crucial to develop effective interventions to limit its transmission. Although real-life experiments remain the most reliable source of scientific evidence, mathematical modelling is often used to simulate outbreaks and evaluate the potential impact of interventions on reducing disease spread. Nevertheless, to generate accurate simulations, it is essential that such models correctly represent the key biological aspects of the studied pathogen, and are properly parameterised using real-life data.

However, for MRSA a major assumption is generally made in mathematical models: at any given time, individuals can at most only be infected by one strain of bacteria. This effectively ignores any bacterial processes occurring within individuals, thereby missing a crucial and proven biological characteristic of the pathogen. Effectively, individuals can simultaneously be infected by more than one strain, and these different strains can "horizontally" exchange (i.e. between them) genes encoding antimicrobial resistance. This project therefore aims to investigate the dynamics generated by these biological processes when they are included in a mathematical model. For example, is the inclusion of horizontal transfer able to justify why antimicrobial resistance can persist in bacterial populations in certain contexts? And if that is the case, in the context of an antibiotic treatment, does horizontal transfer impact the effect of the treatment (e.g. optimal treatment duration)?

To answer such questions, this interdisciplinary project aims to consider both the mathematical modelling and microbiology aspects of these. Effectively, such quantitative skills applied to data obtained experimentally can facilitate the understanding of biological processes. For example, while experimentally measuring rates of horizontal transfer is complex, these can be approximated by fitting models to the data. This project will therefore require the design in parallel of novel mathematical models and laboratory experiments, leading to the creation of a comprehensive framework to study the dynamics of MRSA transmission while including elements at the microbiological level.

We hope that our results can highlight the importance of including more complex biological processes in mathematical modelling. This work has the potential to allow greater understanding of the effect of interventions against MRSA, and perhaps identify new dynamics at the microbiological level that can be targeted to control the pathogen. Although this project will focus on MRSA, we expect that the modelling and experimental framework we will develop will be applicable to some degree to other pathogens displaying antimicrobial resistance. Effectively, horizontal transfer of resistance is common, both amongst and between bacterial species. Although there are admittedly differences in this process according to the chosen bacteria, we hope that our project will serve as a basis and motivate further research on the topic of modelling bacterial strain heterogeneity in individuals and horizontal transfer of antimicrobial resistance between these strains.