Modelling concerted microbial metabolic activities to mimic multicellular behaviour and its applications in biotechnology and biomanufacturing

细菌通过细胞分工受益于多细胞合作，获得单细胞无法有效利用的资源，集体防御拮抗剂，并通过分化成不同的细胞类型来优化种群生存。这些合作结构可以由不同物种的群落组成，也可以由单一物种的集合体组成。结构内的角色分工可以帮助有效利用环境，并帮助实现竞争优势，这可能是有用的生物技术应用和生物制造。该项目将重点关注两种常见的细菌种群结构，它们表现出类似多细胞生物的行为;生物膜和微生物组，并探索如何通过基于模型的分析在生物制造和生物降解中有效利用这一概念。(i)为了应对与塑料废物积累相关的环境挑战迅速升级的严重性，微生物系统对塑料生物降解的研究最近取得了进展。已经确定了几种细菌酶促途径用于塑料利用，但降解速率各不相同，通常较低。最近的研究表明，微生物组环境中多种细菌物种的协调作用可以成为应对塑料降解挑战的有效解决方案。该项目的这一部分将通过代谢模型研究群落结构如何有助于降解过程的有效性。将通过计算机模拟研究对黄粉虫肠道微生物组有贡献的主要物种，并将通过不同细菌内和不同细菌之间的代谢通量分布来确定单个物种在对群落有贡献方面的作用。(ii)生物膜是表面相关的结构，其包含被自我产生的基质包围的微生物群体，所述基质允许它们附着到惰性或有机表面。微生物在生物膜中采取多细胞行为，这有助于和/或促进在不同环境小生境中的生存。作为一种生存策略，休眠状态允许细菌分散和新环境的定殖，而在生物膜中，细胞遵循一种协调的、永久的生活方式，有利于它们的增殖。无张力状态和固着状态之间的交替循环是高度协调的动作，这需要新陈代谢的实质性重新布线。生物膜是生物技术的兴趣，使合理的设计，了解这样的应用程序所需的双峰生长。本节的拟议工作的目的是探讨这在酶的生物制造领域的盐单胞菌的基因组规模的代谢网络模型表达重组酶将被重建。然后将该模型与固着生长的空间模型相结合，以确定导致固着生活方式并回到无张力状态的代谢驱动。最小代谢网络然后将被用于识别可调参数，从而使得能够在两个状态之间切换。在这个项目中，学生将接受生物信息学和代谢建模方面的一系列工具和方法的培训，因为该项目需要重建粗略的代谢网络模型，并在生物信息学工具的帮助下微调这些模型（例如BLAST），通过线性/非线性分析线性规划和系统地解释使用统计工具的结果。该项目福尔斯特别属于生物信息学，数学生物学和过程系统：EPSRC职权范围内的组件和集成研究领域。

Bacteria benefit from multicellular cooperation through cellular division of labour, accessing resources that cannot effectively be utilized by single cells, collectively defending against antagonists, and optimizing population survival by differentiating into distinct cell types. These cooperative structures can comprise of communities of different species, or of single-species assemblies. The division of roles within the structure can assist efficient utilisation of the environment and assist achieving competitive advantage, which could potentially be useful for biotechnological applications and in biomanufacturing. This project will focus on two commonly encountered bacterial population structures that demonstrate multicellular organism-like behaviour; biofilms and microbiomes and explore how this notion can effectively be exploited in biomanufacturing and biodegradation through a model-based analysis. (i) Research on the biological degradation of plastics by microbial systems has recently gained momentum in response to the rapidly escalating severity of the environmental challenge associated with plastic waste accumulation. Several bacterial enzymatic routes have been identified for plastics utilisation, but the rates of degradation vary and are typically low. Recent research has demonstrated that the coordinated action of multiple bacterial species in a microbiome environment can be an effective solution to address the challenge of plastics degradation. This part of the project will investigate how the community structure assists efficacy of the degradation process through metabolic modelling. The principal species contributing to the microbiome of the mealworm gut will be investigated in silico, and the role of individual species in contributing to the community will be identified through the distribution of the metabolic fluxes within and across different bacteria. (ii) Biofilms are surface-associated structures comprising populations of microorganisms surrounded by a self-produced matrix that allows their attachment to inert or organic surfaces. Microorganisms adopt a multicellular behaviour in a biofilm, which facilitates and/or prolongs survival in diverse environmental niches. As a survival strategy, the planktonic state allows for bacterial dispersion and the colonization of new environments, whereas in biofilms cells follow a coordinated, permanent lifestyle that favours their proliferation. The alternating cycle between planktonic and sessile states is a highly coordinated action, which requires a substantial rewiring of the metabolism. Biofilms are of biotechnological interest rendering a rational design of bi-modal growth necessary for understanding such applications. This section of the proposed work aims to explore the impact of this in the domain of enzyme biomanufacturing by Halomonas sp. The genome-scale metabolic network model of the species expressing recombinant enzymes will be reconstructed. This model will then be incorporated with the spatial model of sessile growth to identify the metabolic drive leading to the sessile lifestyle and back to planktonic state. The minimal metabolic networks will then be utilised to identify the tuneable parameters than enable the switch between two states. In this project, the student will be trained in a range of tools and approaches in bioinformatics and metabolic modelling as the project requires the re-construction of coarse metabolic network models, fine tuning these models with the assistance of bioinformatics tools (e.g. BLAST), analysing them through linear/non-linear programming and systematically interpreting the results using statistical tools.The project falls specifically within the Biological Informatics, Mathematical Biology, and Process systems: components and integration Research Areas within the EPSRC remit.