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Modelling cell-cell interactions in heterogeneous microbial communities

   Faculty of Biology, Medicine and Health

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  Dr Rok Krasovec, Dr I Chernyaysky, Dr C Knight  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

We need to understand microbial communities, whether in the context of drug sensitive and resistant pathogens growing in a human host or the soil organisms critical to global carbon cycling. This requires understanding their cellular interactions. Microbial cell-cell interactions are mediated indirectly via small molecules or by direct cell–cell connections. In a bacterial biofilm, particular highly mobile molecules are both secreted and taken up by cells, which increases production of the same molecules. Such auto-inducer systems are considered means for cells to sense local population density (so called quorum-sensing1). In well-mixed environments cell can connect to other cells via unique membrane derived nanotubes and directly exchange cytoplasmic constituents, including plasmids, chemical signals, proteins or nutrients2.

However, observing such systems at the single cell level unravelled vast heterogeneity, which can depend on population density and parent cell’s state, rate and direction of nanotube exchange, intracellular protein dynamics, stochastic effects3 and fluid flows4. This project will iterate between mathematical models, simulations and experiments to understand the role of heterogeneous microbial cell-cell interactions at different scales. Such an understanding is critical to determining the effects of drug regimes5 and feeds back into evolutionary processes6 essential for evolution of resistance, cooperation and other traits.


1. Williams, P., et al. Look who's talking: communication and quorum sensing in the bacterial world. Philos Trans R Soc Lond B Biol Sci 362, 1119-1134, doi:10.1098/rstb.2007.2039 (2007).
2. Pande S., et al. Metabolic cross-feeding via intercellular nanotubes among bacteria. Nat Commun 6: 6238, doi: 10.1038/ncomms7238 (2015)
3. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183-1186, doi:10.1126/science.1070919 (2002).
4. Dalwadi MP & Pearce P (2021). Emergent robustness of bacterial quorum sensing in fluid flow. Proceedings of the National Academy of Sciences 118: e2022312118, doi: 10.1073/pnas.2022312118
5. Anguige, K., King, J. R. & Ward, J. P. Modelling antibiotic- and anti-quorum sensing treatment of a spatially-structured Pseudomonas aeruginosa population. J Math Biol 51, 557-594, doi:10.1007/s00285-005-0316-8 (2005).
6. Krašovec, R., et al. Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions. Nat Commun 5, 3742, doi:10.1038/ncomms4742 (2014)
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