Predicting microbial population dynamics – from microcosms to polymicrobial infections

• Applicant: Professor Dr. Tobias Bollenbach
• Subject Area: Medical Microbiology and Mycology, Hygiene, Molecular Infection Biology ; Bioinformatics and Theoretical Biology ; Microbial Ecology and Applied Microbiology
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 498531688

Microbes live in communities containing many species. Ecological interactions, in which different types of microbes affect each other’s growth, play a key role in the assembly, stability, and dynamics of these communities. Mathematical models can be used to describe population dynamics in such ecosystems. However, to date, predictions of such models have only been successful for artificial in vitro ecosystems (microcosms) consisting of few - often synthetically designed - strains. A predictive theory of microbial ecosystem dynamics remains elusive. To develop such a theory, quantitative measurements of ecological interactions are essential but generally lacking. In this project, our main goal is to build and validate mathematical models to predict microbial population dynamics, starting from controlled microcosms in the lab but ultimately expanding to polymicrobial infection communities. To this end, we will use robotic high-throughput techniques to measure pairwise ecological interactions between large numbers of clinical isolates, perform co-culture assays, and analyze time-resolved in vivo microbiome data from patients with chronic polymicrobial infections. We will develop mathematical models in an iterative process, starting with relatively simple microcosms of manipulated Escherichia coli strains and then extending this approach to isolates from more complex polymicrobial infections of the lung and urinary tract. Throughout this project, we focus on small microbial assemblages for which there is a realistic hope of understanding their emergent properties. The successful completion of this work will advance predictive ecology and inspire new treatment strategies for polymicrobial infections targeted not just at individual strains but at a disruption of entire microbial communities.

• DFG Programme: Research Grants