Exploiting Community Drivers for Population Control in Microbial Communities

利用群落驱动因素控制微生物群落的数量

• 批准号: 2595413
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2595413
• 关键词: Exploiting; Community; Drivers; Population; Control

IntroductionCellular communities are collections of interacting microbial species found in all natural environments ranging from plant root ecosystems to the human gut microbiome. They are highly dynamic systems that respond to changes in their environment, but also influence their environment in extremely consequential ways: e.g. affecting crop yield or drought resistance in plants, or causing diseases in humans. While improvements in sequencing technology allow for a growing understanding of these communities and their relationship with their environment, the ability to control and direct them towards beneficial compositions are needed to reap the benefits.Control mechanisms for communities also allow them to be used for biotechnological production. While current applications in biotechnology predominantly involve a single strain in an axenic monoculture, a community of several strains has many potential benefits such as the ability to utilise more complex substrates, increased robustness to environmental fluctuations, or division of labour for decreased metabolic burden. The inability to maintain population ratios is a key limitation preventing widespread adoption of microbial communities across biomedicine and manufacturing: these systems are susceptible to population crashes where they move away from an ideal population with maximal yield to one where dominant species outcompete and overwhelm others.Aims & MethodThis research aims to engineer control mechanisms that regulate and fine tune the composition of cellular communities in real time based on the concept of 'driver species', a subset of species that are sensitive to easily controlled inputs such as light or chemical inducers. Because they interact with other species in the community (e.g. by competing for resources or producing toxic metabolites), these driver species can be regulated to steer overall community composition. While a theoretical framework has been published, this has yet to be shown empirically, possibly due to the complexity in implementing the control scheme.This research will consist of growing pairs of species in the laboratory and measuring the strength of inter-species interactions from steady-state abundance data in order to define the ecological network (i.e. the relationship between the individuals in the community). This will be used to generate mathematical models of the community's growth and dynamics and develop control schemes. The species can then be combined into a community using Chi.Bio, an automated robotic platform developed by the lab which performs the in-situ measurement and control necessary for implementing control schemes based on these models, as real-time data of community composition is required to regulate feedback. After testing this in small engineered synthetic communities, the control schemes will then be tested with a (subset of) a natural microbiome to engineer its behaviour and achieved a desired application composition.This project falls directly within the EPSRC Engineering research theme, and particularly the areas of Control Engineering and Synthetic Biology. Similarly, outcomes of this project will have broad impact in realising EPSRC Priorities including 21st Century Products (by making smart, multi-functional communities), and Sustainable Industries (by unlocking new methods for distributed biomanufacturing).

细胞群落是在从植物根系生态系统到人类肠道微生物组的所有自然环境中发现的相互作用的微生物物种的集合。它们是高度动态的系统，对环境的变化作出反应，但也以极其重要的方式影响环境：例如影响作物产量或植物的抗旱性，或引起人类疾病。虽然测序技术的改进使人们能够越来越多地了解这些群落及其与环境的关系，但需要有能力控制和引导它们产生有益的成分，以获得利益。虽然目前在生物技术中的应用主要涉及无菌单一培养中的单一菌株，但几种菌株的群落具有许多潜在的益处，例如利用更复杂底物的能力，对环境波动的鲁棒性增加，或减少代谢负担的劳动分工。无法维持种群比例是阻止微生物群落在生物医学和制造业中广泛采用的一个关键限制：这些系统很容易发生种群崩溃，即它们从具有最大产量的理想种群转移到优势物种胜过和压倒其他物种的种群。方法本研究旨在基于“驱动物种”的概念，设计控制机制，在真实的时间内调节和微调细胞群落的组成，对容易控制的输入（如光或化学诱导剂）敏感的物种子集。由于它们与群落中的其他物种相互作用（例如，通过竞争资源或产生有毒代谢物），这些驱动物种可以受到调控，以引导整个群落的组成。虽然理论框架已经发表，但这尚未得到经验证明，可能是由于实施控制方案的复杂性。这项研究将包括在实验室中培养成对的物种，并根据稳态丰度数据测量物种间相互作用的强度，以定义生态网络（即群落中个体之间的关系）。这将用于生成社区增长和动态的数学模型，并制定控制计划。然后，这些物种可以使用Chi.Bio组合成一个社区，Chi.Bio是实验室开发的自动化机器人平台，它可以进行基于这些模型实施控制方案所需的原位测量和控制，因为需要社区组成的实时数据来调节反馈。在小型工程合成社区中进行测试后，将使用天然微生物组（子集）测试控制方案，以设计其行为并实现所需的应用组合。该项目福尔斯直接属于EPSRC工程研究主题，特别是控制工程和合成生物学领域。同样，该项目的成果将对实现EPSRC优先事项产生广泛影响，包括21世纪世纪产品（通过打造智能多功能社区）和可持续产业（通过解锁分布式生物制造的新方法）。