Supervisors
Michael Gutmann (e-mail)
Andrea Weisse (e-mail)
Project Description
Antimicrobial resistance poses an imminent threat to human health and medicine as we know it. Bacterial resistance to antibiotics, which are the drugs used to treat bacterial infections, can take different forms. It can be acquired via mutations, or it can be an inherent, adaptive response by the bacterial cells that renders them less susceptible to treatment with antibiotics. In this project, we will investigate a cellular repair system that confers adaptive resistance to a class of broadly used antibiotics. The project will build on recent work that developed the first mechanistic model of the repair system and its genetic control. We will integrate data collected in the labs of our collaborators and use and develop Bayesian experimental design to infer experiments that are maximally informative to constrain model parameters or missing model components. The project will assess the effectiveness of Bayesian experimental design within the biomedical field and extend these methods to ensure their robustness against potential model inaccuracies. It will increase our understanding of the biological mechanisms underlying antibiotic resistance and improve our ability to predict potential treatment interventions. The project is an opportunity to contribute to scientific efforts to use the antibiotics available to us in maximally effective ways.
