Pioneering the new genomics era in environmental microbiology for engineering design

天然微生物群落在废水处理，生物能源生产和生物修复中发挥着许多重要的工程功能，但我们对这些复杂群落如何组装，功能和应对环境变化的理解有限。在过去，我们已经使用了经验的方法来利用这些系统的基础上积累的知识。这已经取得了一些成功，例如在废水处理方面，但以前的经验将无法帮助我们处理新的环境和问题。为了开始设计这些系统并针对不同的应用进行优化，我们需要能够预测其行为的数学模型。我们现在面临的气候、能源、污染和水资源危机使利用微生物充分发挥其潜力的迫切需要成为焦点。预测模型的发展一直受到难以获得这些社区的信息的限制。历史上，分析仅限于那些可以在实验室中分离和生长的微生物，但这些微生物只代表了群落的一小部分。环境基因组学这门新科学通过直接提取和扩增DNA，避开了培养生物体的需要，但直到最近几年，这种DNA的实际测序仍然缓慢而昂贵。因此，与微生物的巨大多样性和数量相比，样本量很小。现在，新的高通量测序技术已经出现，这些技术将数据采集的速度提高了几个数量级，使我们能够第一次详细了解这些社区的组成以及它们如何在空间和时间上变化。使用宏基因组学，我们还可以开始将社区成员的身份与他们的代谢功能联系起来。最后，我们有足够的数据来开始构建我们迫切需要的模型。该研究金将利用这一机会，通过综合方法，开发一种新的组合基因组学建模范式，用于微生物系统的研究。将开发新的统计工具和软件，从测序数据中过滤噪音，并提取信息，然后将其输入多尺度数学模型。在最基本的层面上，这些模型将明确描述个体通过化学底物的消耗和生产而移动、繁殖和相互作用。使用先进的数学技术，他们将扩大到整个人口的描述。这将使我们能够根据在个体水平上操作的过程来定义模型，但用提供群体水平图片的基因组学数据来验证它们。它还将允许这些模型应用于其工业应用所需的整个系统规模。开发的统计工具和数学模型将是完全通用的，但我们将通过关注两个具体的案例研究来说明这种方法：低温厌氧废水处理和微生物燃料电池。前者有可能降低英国和其他温带国家废水处理的能源成本和碳足迹。后者可以使用几乎任何有机物质作为燃料，为偏远地区提供廉价、清洁的电力来源。它们在发展中国家可能特别有用。我们将进一步探讨我们的范例，以其他重要的微生物工程问题的应用。此外，开发的工具和模型将适用于任何领域的微生物群落研究，从人类健康到维持地球生命的生物地球化学循环。

Natural microbial communities perform many vital engineering functions in wastewater treatment, bioenergy production, and bioremediation, but our understanding of how these complex communities assemble, function and respond to environmental change is limited. In the past we have used an empirical approach to utilising these systems based on accumulated knowledge. This has had some success, for example in wastewater treatment, but previous experience will not help us deal with novel environments and problems. To start engineering these systems and optimise them for different applications we need mathematical models that are capable of predicting their behaviour. The urgent need to harness microbes to their full potential has been brought into sharp focus by the climate, energy, pollution, and water crises we now face. Development of predicative models has been constrained by the difficulty of obtaining information on these communities. Historically analysis was restricted to those microbes that could be isolated and grown in the laboratory but these represent only a fraction of the community. The new science of environmental genomics, by direct extraction and amplification of DNA, has sidestepped the need to culture organisms, but until the last few years actual sequencing of this DNA was slow and expensive. Consequently sample sizes were small compared to the huge diversity and numbers of microbes. Now new high throughput sequencing technologies are available, which have increased the rate of data acquisition by orders of magnitude, allowing us for the first time to obtain a detailed picture of the composition of these communities and how they vary through space and time. Using metagenomics we can also start linking the identity of the community members to their metabolic functions. Finally, we have sufficient data to start constructing the models we desperately need. This fellowship will exploit this opportunity, through an integrated approach, to develop a new combined genomics modelling paradigm for the study of microbial systems. New statistical tools and software will be developed to filter noise from the sequencing data, and extract information which can then be fed into multi-scale mathematical models. At the most fundamental level these models will have an explicit description of individuals moving, reproducing and interacting through the consumption and production of chemical substrates. Using advanced mathematical techniques they will be scaled-up to a description of whole populations. This will enable us to define the models in terms of processes operating on the level of individuals but validate them with the genomics data which provides a population level picture. It will also allow these models to be applied on the whole system scales necessary for their industrial application. The statistical tools, and mathematical models developed will be completely generic but we will illustrate the approach by focusing on two specific case studies: low temperature anaerobic wastewater treatment and microbial fuel cells. The former has the potential to reduce the energetic costs and carbon footprint of the treatment of wastewater in the UK and other temperate countries. The latter could provide a cheap, clean source of electricity in remote locations using virtually any organic substance as fuel. They could be particularly useful in the developing world. We will explore further applications of our paradigm to other vital microbial based engineering problems. In addition, the tools and models developed will be applicable to the study of microbial communities in any area, from human health to the biogeochemical cycles that sustain life on this planet.