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

细菌通过细胞分工从多细胞合作中获益，获取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群体生存。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群落组成。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，这可能对生物技术应用和生物制造有用。该项目将重点关注两种常见的细菌种群结构，它们表现出多细胞生物样行为；生物膜和微生物组，并通过基于模型的分析探索如何有效地利用这一概念在生物制造和生物降解中。(i)关于微生物系统对塑料的生物降解的研究最近取得了势头，以应对与塑料废物积累有关的环境挑战迅速升级的严重性。已经确定了几种用于塑料利用的细菌酶途径，但降解率各不相同，而且通常很低。最近的研究表明，微生物组环境中多种细菌的协同作用可以有效解决塑料降解的挑战。该项目的这一部分将通过代谢模型研究群落结构如何帮助降解过程的效率。对粉虫肠道微生物群有贡献的主要物种将在计算机上进行研究，并通过不同细菌内部和之间的代谢通量分布来确定单个物种对该群落的贡献。（ii）生物膜是一种表面相关结构，包括由自产基质包围的微生物种群，使它们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，促进和/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、永久的生活方式，有利于它们的增殖。浮游和无根状态之间的交替循环是一种高度协调的行为，它需要新陈代谢的大量重新布线。生物膜是生物技术的兴趣，呈现合理的双峰生长设计是理解这种应用所必需的。本部分的工作旨在探讨这在盐单胞菌（Halomonas sp.）的酶生物制造领域的影响。将重建表达重组酶的物种的基因组尺度代谢网络模型。该模型随后将与无根生长的空间模型相结合，以确定导致无根生活方式并回到浮游状态的代谢驱动。然后，最小代谢网络将用于识别可调参数，而不是在两种状态之间切换。在这个项目中，学生将接受一系列生物信息学和代谢建模的工具和方法的培训，因为该项目需要重建粗代谢网络模型，在生物信息学工具（例如BLAST）的帮助下微调这些模型，通过线性/非线性规划分析它们，并使用统计工具系统地解释结果。该项目属于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.