Exploiting Community Drivers for Population Control in Microbial Communities

前言细胞群落是相互作用的微生物物种的集合，存在于从植物根生态系统到人类肠道微生物群的所有自然环境中。它们是高度动态的系统，对环境的变化做出反应，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抗旱性，或导致人类疾病。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，但需要有能力控制它们并将它们引向有益成分，才能获得好处。群落的控制机制也使它们能够用于生物技术生产。虽然目前生物技术中的应用主要涉及无菌单一培养中的单一菌株，但由几个菌株组成的群落具有许多潜在的好处，例如能够利用更复杂的底物，提高对环境波动的稳健性，或者为了减少代谢负担而进行分工。无法维持种群比例是阻碍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：这些系统容易受到种群崩溃的影响，即它们从具有最大产量的理想种群转移到优势物种竞争并压倒其他物种的种群。目的与方法本研究旨在设计控制机制，基于“驱动物种”的概念实时调节和微调细胞群落的组成，驱动物种是对光或化学诱导剂等容易控制的输入敏感的物种的子集。因为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监管，以指导整个群落组成。虽然理论框架已经公布，但这还没有得到经验证明，可能是因为实施控制方案的复杂性。这项研究将包括在实验室中生长物种对，并从稳态丰度数据测量物种间相互作用的强度，以确定生态网络(即群落中个体之间的关系)。这将被用来生成社区增长和动态的数学模型，并制定控制方案。然后，这些物种可以使用Chi.Bio组合成一个群落，Chi.Bio是实验室开发的一个自动化机器人平台，它执行基于这些模型的控制方案所需的现场测量和控制，因为需要群落组成的实时数据来调节反馈。在小型工程合成社区中测试后，控制方案将与自然微生物群(子集)一起测试，以设计其行为并实现所需的应用成分。该项目直接属于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的优先事项产生广泛影响，包括21世纪的产品(通过制造智能、多功能的社区)和可持续产业(通过开启分布式生物制造的新方法)。

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).