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，也可以由单物种集合组成。该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，这可能对生物技术应用和生物制造有用。该项目将把重点放在两种常见的细菌种群结构上，它们展示了多细胞生物体的行为；生物膜和微生物群，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地利用这一概念。(1)针对塑料废物堆积带来的日益严峻的环境挑战，微生物系统对塑料的生物降解的研究最近获得了势头。已经确定了几种细菌酶法途径来利用塑料，但降解率各不相同，通常都很低。最近的研究表明，微生物环境中多个细菌物种的协同作用可以有效地解决塑料降解的挑战。该项目的这一部分将调查社区结构如何通过新陈代谢模型来帮助提高退化过程的效率。对粉虫肠道微生物群的主要物种将在Silico中进行调查，并将通过不同细菌内部和之间代谢通量的分布来确定单个物种在群落中的作用。(2)生物膜是一种表面相关结构，由微生物种群组成，周围环绕着一种自产基质，使其能够附着在惰性或有机表面上。微生物在生物膜中采取多细胞行为，这有助于和/或延长在不同环境中的生存。作为一种生存策略，浮游状态允许细菌扩散和在新环境中定居，而在生物膜中，细胞遵循一种协调的、有利于它们增殖的永久生活方式。浮游状态和固着状态之间的交替循环是一种高度协调的行为，这需要对新陈代谢进行实质性的重新布线。生物膜具有生物技术价值，因此合理设计双模式生长对于理解这种应用是必要的。这一部分的拟议工作旨在探索这一点在盐生单胞菌生物制造酶领域的影响。表达重组酶的物种的基因组规模代谢网络模型将被重建。然后，这个模型将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浮游状态的代谢驱动。然后，最小代谢网络将被用来识别可调参数，从而实现两种状态之间的切换。在这个项目中，学生将在生物信息学和代谢建模方面接受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，在生物信息学工具(例如BLAST)的帮助下对这些模型进行微调，通过线性/非线性编程进行分析，并使用统计工具系统地解释结果。该项目具体属于EPSRC职权范围内的生物信息学、数学生物学和过程系统：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.