Modeling Variability in Persistence Induced From Within by a Toxic Metabolite

Project Team: Christopher Marx (PI), Andreas Vasdekis (Co-PI), Chris Remien, Siavash Riazi, Denis Liyu

Multidrug antibiotic persistence, which allows some cells that lack genetic resistance to survive antibiotic stresses by becoming dormant, is a major public health concern. This exploratory project will use data from state-of-the-art image cytometry and single-cell analysis in combination with mechanistic mathematical modeling to study the formaldehyde-sensing network that was recently discovered in Methylobacterium by the Marx Lab. The formaldehyde-sensing network in Methylobacterium shares many characteristics with antibiotic persistence, but has the advantage of allowing us to externally manipulate factors governing the transition from growth to stasis, and all the cells in a population go dormant. The research team wishes to develop mathematical models in combination with relevant experimentation 1) to study the ability the biochemical network to allow for distinct cell fate outcomes as a function of key parameters such as protein levels of EfgA 2) to analyze how stochasticity in the form of spontaneous fluctuations in protein levels, which can lead to a potentially toxic pulse of formaldehyde, influences cell transitions between phenotypes such as growth, death, or persistence. This pilot grant will position the researchers to explore fundamental processes associated with antimicrobial resistance, which if eventually manipulated could prevent disease and promote health.