*EASTBIO* Understanding the effect of antibiotic combinations: a quantitative approach

Our society is currently facing an important problem with the rise of multiple antibiotic resistant bacterial strains. As the development of new antibiotics has stalled in recent years, new strategies are urgently needed to supplement current available therapies. This involves increasing the efficiency of already available antibiotics as well as identifying new targets.

Antibiotics are increasingly used in combination to increase efficacy and limit emergence of resistance. Two important classes of clinically relevant antibiotics target bacteria by either inducing DNA damage or inhibiting cell well synthesis which ultimately leads to stopping bacterial cells growth and killing them. However, the relationship between the cellular physiology of bacteria and their susceptibility to antibiotics is not yet clearly understood. We have recently shown that in slow growth conditions, treatment with a DNA damaging antibiotic leads to tolerance to some cell wall synthesis inhibitors. Thus, counter-intuitively, treatment with one antibiotic may lead to decreased efficacy of the second one. The reasons underlying this tolerance are not yet known but this is only true when bacteria are growing slowly indicating that cell physiology plays an important role.

The objective of the project is to characterize experimentally and theoretically how the susceptibility of Escherichia coli cells to combination of various DNA damaging agent and cell wall inhibitors depends on the cells’ growth rate and other parameters of their physiology. Measurement will be performed at the population and single cell level using custom microfluidics devices and state of the art quantitative microscopy. The experimental results will be used to inform mathematical models of bacterial cell growth under combinations of antibiotics exposure.

Candidates with a background in physics, applied mathematics or quantitative biology interested in working in a highly interdisciplinary environment and keen to learn experimental biology and/or modeling are encouraged to apply.