Mapping Microbial Community Structure and Function in Enhanced Biological Phosphorus Systems

强化生物除磷（EBPR）是活性污泥技术的一种变体，除了使用微生物菌群去除有机物外，还可以从进水废水中去除磷，以防止接收体的富营养化。与活性污泥一样，采用EBPR的废水处理厂也会因功能微生物数量失衡而表现不佳。了解性能恶化背后的根本原因，并采取可靠的纠正措施，迄今为止已经证明自己hierarchic.Advances在培养独立的微生物技术和宏基因组学刺激了工作对在这些系统中的微生物群落的原位表征的多样性，相对丰富的代表性的个别门，以及各自的功能生态。补充这项工作的是实验室规模的研究，利用高度富集的细菌培养物，以及全面的设施调查，其中相关的相对丰度的感知核心成员的微生物群落的利益与理化分析，以他们各自的功能。然而，现有信息的性质仍然是支离破碎的，而且由于信息是在孤立的情况下产生的，因此没有结论-更不用说在理解所报告的技术不稳定性方面，相互矛盾。这个博士项目的总体目标是开发和部署统计模型，以评估和统一的要素（1a）微生物群落的组成，（2a）它们各自的生态功能和门间的相互作用，以及（3a）工艺设计和操作。因此，本项目的组成目标包括但不限于：（1b）收集现有的信息库，并将其纳入一个统一的工作数据库;（2b）构建网络模型，利用该网络模型来表示、链接和评估所组装的数据;以及（3b）调查和评估相关的统计工具，例如自学习贝叶斯统计模型，以便利用可用的证据来推断EBPR微生物聚生体内不同群体之间关系的性质，本项目的目标是提高对与EBPR相关的微生物菌群的整体理解。特别是，该项目的潜在影响将（1c）能够制定具体的战略，以培养合适的社区，确保采用该技术的废水处理设施的持续令人满意的性能，因此合规性，（2c）为未来EBPR过程的设计优化提供框架，以及（3c）提供工具，以系统地识别知识缺陷，从而提供未来研究关键领域的路线图。从更广泛的意义上说，本项目中开发的拟议工具可以应用于任何生物系统的开发，回答以下问题：（1d）哪些是在全面处理和/或生产设施中监测以确保有效性能的关键参数，（2）什么是（3d）如何利用现有数据，或（4d）如何定义众包策略以更好地做出明智的决策。该项目将把水工程领域与统计学领域联系起来，应用概率，其中两个关键增长领域所指出的EPSRC专题研究组合。

Enhanced Biological Phosphorus Removal (EBPR) is a variation of the activated sludge technology that in addition to removal of organic matter using a microbial consortium, enacts phosphorus removal from the influent wastewater to prevent eutrophication of the receiving body. Like activated sludge, wastewater treatment plants employing EBPR are known to suffer from poor performance attributed to population imbalances of the functional microorganisms. An understanding of the root causes behind the deteriorated performance, and of the reliable corrective measures have thus far proven themselves elusive.Advances in culture-independent microbiological techniques and metagenomics have stimulated work towards the in situ characterisation of microbial communities in these systems in terms of their diversity, relative abundance of the representative individual phyla, as well as their respective functional ecology. Complementing this work are laboratory-scale studies utilising highly enriched bacterial cultures, as well as surveys of full-scale facilities which correlate the relative abundance of perceived core members of the microbial community of interest in tandem with physiochemical analyses to their respective function. In its current form however, the nature of the available information remains fragmented and, owing to the isolation in which it was produced, inconclusive - not to mention that with regards to understanding the reported instability of the technology, contradictory.In light of this, the overall aim of this PhD project is to develop and deploy statistical models with which to evaluate and unite elements of the (1a) composition of the microbial community, (2a) their respective ecological function and inter-phyla interactions and (3a) process design and operation.The component objectives of this project thus include but are not limited to: (1b) collecting and framing the existing pool of information into a unified working database; (2b) constructing a network model with which to represent, link and evaluate the assembled data; and (3b) investigating and assessing relevant statistical tools such as a self-learning Bayesian statistical models in order to make use of the available evidence to infer the nature of the relationships among different groups within EBPR microbial consortia, their relation to the process configuration and operational parameters and environmental conditions.The ambition of this project is to improve the holistic understanding of the microbial consortia relevant to EBPR. In particular, the potential impact of this project will (1c) enable the formulation of concrete strategies to cultivate the right communities that would ensure consistently satisfactory performance, and therefore compliance, of wastewater treatment facilities employing this technology, (2c) provide the framework for design optimisation of future EBPR processes and (3c) provide the tools to enable systematic identification of knowledge deficiencies, thereby providing a roadmap of key areas of future research.In a broader sense, the proposed tools to be developed in this project could find application in the exploitation of any biological system, answering questions such as: (1d) which are the key parameters to monitor in full-scale treatment and/or production facilities to ensure efficient performance, (2d) what is the (smallest) required sample size of this pool of data, (3d) how can one make use of the data already available, or (4d) how to define crowd-sourcing strategies for better informed decision making.This project will connect the field of water engineering to that of statistics & applied probability, two among the key growth areas as indicated by the EPSRC thematic research portfolio.