EAGER SitS: Developing a Next Generation Modeling Approach for Predicting Microbial Processes in Soil

微生物是地球上许多生命支持系统的核心生物。在过去的十年里，在鉴定存在于许多环境中的微生物种类方面取得了很大的进展。此外，许多控制这些微生物活动的基因已经被发现。这项研究将揭示微生物如何控制关键的生态系统服务。该项目将开发一种新的建模框架，将有关微生物活动的信息与来自传感器网络的环境数据结合起来。这种方法将促进对微生物如何控制元素循环和地球上的能量流动的理解。这项工作将为管理自然生态系统和工业过程创造有价值的工具。该项目还将支持本科生的研究，作为马萨诸塞州伍兹霍尔海洋生物实验室本学期环境科学项目的一部分。在大多数自然微生物系统中，微生物代谢和调控网络的细节在可预见的未来仍将是未知的，太复杂了，无法纳入气候、农业土壤肥力或废物管理的模型。相反，开发一种可扩展的生物地球化学模拟方法对于检测复杂土壤系统在微生物和环境因素共同控制下运行的原理至关重要。该项目将开发一个灵活的框架，从最大熵产生(MEP)的热力学角度分析微生物生物地球化学。这项工作利用微生物群落的高度多样性，使基于热力学的预测成为可能，以预测系统水平对全球变化的生物地球化学反应。计划中的热力学约束代谢建模方法将解决与微生物群落建模相关的两个关键挑战：(1)捕捉微生物群落的自组织和代谢功能的表达；(2)随着微生物群落组成随当地环境条件的变化而动态地重新参数化反应动力学。分布式代谢网络建模方法以随时间和空间变化的最小最优控制变量集为特征。这些变量控制着分布式代谢网络的化学计量和热力学以及反应速率。为了利用现有的建模和实验工作进行模型测量比较，第一阶段的研究将集中在包括甲烷生成和甲烷营养的简化网络上。最终，目标是将传感器获得的信息与各种已知的微生物能力(受热力学原理的限制)相结合，使用比传统建模所需的参数少得多的参数来预测土壤中微生物群落的变化活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Microbes are the key organisms at the core of many of Earth's life support systems. Over the last decade, great progress has been made in identifying the species of microbes present in many environments. In addition, many of the genes that control what these microbes do have been discovered. This research will uncover how microbes control critical ecosystem services. This project will develop a new modeling framework to combine information about microbial activities with environmental data from sensor networks. This approach will advance understanding of how microbes control cycles of elements and the flow of energy on Earth. This work will create valuable tools for managing natural ecosystems and industrial processes. The project will also support undergraduate research as part of the Semester in Environmental Science program at the Marine Biological Laboratory in Woods Hole, MA.In most natural microbial systems, details of microbial metabolic and regulatory networks will remain unknown in the foreseeable future and are too complex to be folded into models of climate, soil fertility for agriculture, or waste management. Rather, development of a scalable biogeochemical modeling approach is critical for detecting the principles by which complex soil systems operate under co-control by microbes and environmental factors. This project will develop a flexible framework for analyzing microbial biogeochemistry from the thermodynamic perspective of maximum entropy production (MEP). The work takes advantage of the high diversity of microbial communities to enable thermodynamically-based predictions about system-level biogeochemical response to global change. The planned thermodynamically-constrained metabolic modeling approach will address two key challenges associated with modeling microbial communities: (1) capturing community self-organization and expression of metabolic function, and (2) re-parameterizing reaction kinetics dynamically as microbial community composition shifts in response to local environmental conditions. The distributed metabolic network modeling approach features a minimal set of optimal control variables that vary over time and space. These variables control stoichiometry and thermodynamics of a distributed metabolic network as well as reaction rates. To leverage existing modeling and experimental work for model-measurement comparisons, the first phase of research will focus on a simplified network including methanogenesis and methanotrophy. Ultimately, the goal is to integrate sensor-derived information with diverse, known microbial capabilities (constrained by thermodynamic principles), to predict shifting activities of microbial communities in soils using far fewer parameters than would be required with conventional modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.