Sauer Lab, Predicting antimicrobial interactions

The emergence of antibiotic-resistant microbial species raises the urgent need for novel antibacterial tactics. As a matter of fact, over the past 60 years, a handful of new antibiotics have been discovered, but only few were developed in the past 40 years. To cope with both problems, emerging resistance and lack of newly developed drugs, research-focus turned towards understanding how to slow down the development of resistance by finding optimal drug combinations. Combining antibiotics is a promising strategy to combat resistance evolution and to increase treatment efficacy. However, systematically exploring the effects of multiple drug combinations (e.g. drug epistatic interactions) rapidly becomes prohibitive due to a combinatorial explosion.

The goal of this project is to derive predictive models that, on the basis of metabolic changes induced upon exposure of E.coli cells to individual antibiotics, can infer the outcome of combinatorial treatments. Derived principles will be applied to predict combinatorial drug-interactions from a large screen metabolome-dataset monitoring the individual effect of more than 300 bioactive compounds on Mycobacteria.

The candidate will familiarize with mass-spectrometry techniques to quantitatively describe the short and long-term metabolic changes induced upon exposure of E.coli cells to individual and combinations of antibiotics with different mode of interactions, and with computational frameworks, such as Flux Balance Analysis, to interpret and analyze the metabolome data. Basic experience in programming (Matlab) is required.

Contact: Mattia Zampieri