We need to understand microbial communities, whether in the context of antimicrobial resistance (AMR) or their key role in global carbon cycling. This requires understanding cell-environment and cell-cell interactions. Bacterial biofilms are the prevalent form of life, conferring numerous benefits to their inhabitants including AMR. In biofilms, highly mobile molecules are both secreted and taken up by cells, some of which increase production of the same molecules. Such auto-inducer systems enable cells to sense local population density (so called quorum-sensing, QS ). However, observing such systems at the single cell level has revealed great heterogeneity.
This project will iterate between mathematical models, simulations and experiments to understand how, within bacterial biofilms, spatially confined fluid flows, epigenetic inheritance, protein dynamics and multi-scale stochasticity in small-molecule-transport mediated auto-inducer systems, result in a heterogeneous gene expression and macroscopic biological function.
The student will 1) Use Bayesian approaches to connect current models of bacterial quorum sensing in fluid flow to data 2) Couple the population-level transport model to intracellular dynamics and to the effect of cell lineage. 3) Use agent-based and upscaled continuum models to test biological hypotheses and scenarios under different assumptions. Feedback and iteration with wet-lab data will identify and test the effects of key parameters that can be manipulated experimentally (e.g. growth chamber geometry, growth media, quorum-sensing genotype, inter-and intracellular mobility or identifying new targets for the fluorescence imaging). The outcome will be an integrated understanding of how effects at different scales (gene networks, quorum sensing, cell lineages, fluid flows) determine biofilm function.
The supervisory team is built with to facilitate feedback between modelling and application. Two are biologists by background (Chris and Rok)  and two mathematicians (Igor and Philip) [4-5]. From those backgrounds, Chris has developed as a modeller and Philip has specialised in the biological study system.
The successful candidate will play an active role in MADSIM PhD Training Centre, which is an exciting community of PhD students at the University of Manchester. The student will also be part of the Microbial Evolution Research Manchester (MERMan) grouping – a dynamic and nationally-leading group of evolutionary microbiologists.
Applicants are expected to hold, or about to obtain, a minimum upper second class undergraduate degree (or equivalent) in Applied Mathematics, Mathematics, Physics, Biophysics, Mathematical Biology, Fluid dynamics, Computer Science or Data analysis. A Masters degree in relevant field or relevant research experience at the boundary of mathematics and biology is desirable. Experience of experimental work (e.g. microscopy), and interests in Evolutionary Biology are also desirable.
For project enquiries contact;
Chris Knight; [Email Address Removed]
Rok Krasovec; [Email Address Removed]
Igor Chernyavsky; [Email Address Removed]
Philip Pearce; [Email Address Removed]
To make an application please visit - https://www.ees.manchester.ac.uk/study/postgraduate-research/how-to-apply/
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