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

EAGER SitS：开发下一代建模方法来预测土壤中的微生物过程

基本信息

批准号：
1841599
负责人：
Joseph Vallino
金额：
$ 30万
依托单位：
Marine Biological Laboratory
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841599&HistoricalAwards=false
关键词：
EAGER SitS Developing Next Generation

项目摘要

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.

微生物是地球上许多生命支持系统的核心。在过去的十年中，在识别许多环境中存在的微生物物种方面取得了很大进展。此外，许多控制这些微生物的基因也被发现。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联合收割机信息与来自传感器网络的环境数据相结合。这种方法将促进对微生物如何控制地球上元素循环和能量流动的理解。这项工作将为管理自然生态系统和工业流程创造宝贵的工具。该项目还将支持本科生的研究作为在伍兹霍尔，MA的海洋生物实验室在环境科学计划学期的一部分。在大多数自然微生物系统中，微生物代谢和监管网络的细节将在可预见的未来仍然未知，太复杂，被折叠成气候模型，农业土壤肥力，或废物管理。相反，一个可扩展的土壤地球化学建模方法的发展是至关重要的检测复杂的土壤系统在微生物和环境因素的共同控制下运行的原则。本计画将从最大熵产生（MEP）的热力学观点，发展一个分析微生物生物地球化学的弹性架构。这项工作利用了微生物群落的高度多样性，以实现对全球变化的系统级生物地球化学响应的基于化学的预测。计划中的代谢约束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）捕获群落自组织和代谢功能的表达，以及（2）随着微生物群落组成响应于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的特征在于随时间和空间变化的最优控制变量的最小集合。这些变量控制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一个简化的网络，包括甲烷生成和甲烷营养。最终，目标是将传感器获得的信息与不同的已知微生物能力（受热力学原理约束）相结合，使用比传统建模所需的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，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。