Modelling the effect of quorum sensing in fluid flow on biofilm function

我们需要了解微生物群落，无论是在抗菌素耐药性(AMR)的背景下，还是它们在全球碳循环中的关键作用。这需要了解细胞-环境和细胞-细胞相互作用。细菌生物膜是普遍存在的生命形式，给包括AMR在内的居民带来了许多好处。在生物膜中，高流动性的分子既由细胞分泌又被细胞吸收，其中一些增加了相同分子的产生。这种自动诱导系统使细胞能够感知局部种群密度(所谓的群体感应，QS[2])。然而，在单细胞水平上观察这样的系统已经显示出极大的异质性。本项目将在数学模型、模拟和实验之间迭代，以了解在细菌生物膜内，小分子运输介导的自动诱导系统中空间受限的流体流动、表观遗传、蛋白质动力学和多尺度随机性是如何导致异质基因表达和宏观生物功能的。学生将1)使用贝叶斯方法将当前流体中细菌群体感应模型与数据联系起来2)将种群水平的运输模型与细胞内动力学和细胞谱系的影响相结合。3)使用基于主体的和扩展的连续介质模型来检验不同假设下的生物学假设和情景。湿法实验室数据的反馈和迭代将识别和测试可通过实验操作的关键参数(例如，生长室几何形状、生长介质、群体感应基因、细胞间和细胞内迁移率或识别荧光成像的新目标)的效果。结果将是对不同尺度(基因网络、群体感应、细胞谱系、流体流动)的影响如何决定生物膜功能的综合理解。监督团队的建立是为了促进建模和应用之间的反馈。其中两位是背景生物学家(克里斯和洛克)[3]，两位数学家(伊戈尔和菲利普)[4-5]。在这些背景下，克里斯发展成为一名模特师，菲利普专门研究生物研究系统。

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 [2]). 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) [3] 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.