Collaborative Research: Predicting the Spatiotemporal Distribution of Metabolic Function in the Global Ocean

合作研究：预测全球海洋代谢功能的时空分布

• 批准号: 1558702
• 负责人: Michael Follows
• 金额: $ 17.64万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1558702&HistoricalAwards=false
• 关键词: Collaborative; Research; Predicting; Spatiotemporal; Distribution

Predicting how marine chemistry and biology will respond to global change is a pressing issue for society. This project will develop new modeling techniques for predicting such changes using ideas derived from physics in the subdiscipline of thermodynamics that concerns how energy moves in a system. Recent advancements in the thermodynamics of systems that change over time indicate that systems will internally organize so as to maximize the flow and dissipation of energy. For example, the temperature difference that develops between the ocean and atmosphere over the summer drives the formation of hurricanes (the organized structures) whose presence hastens the dissipation of the temperature difference. This project utilizes this fundamental property but extends it to microbial communities, such as bacteria and phytoplankton, which form the base of the ocean food web and strongly influence ocean chemistry. Based on information on how biology utilizes solar and chemical energy to construct itself from carbon, nitrogen, phosphorus and other elements in the environment, the model can predict how metabolic functions, such as photosynthesis or nitrogen fixation from the atmosphere, are expressed over time and space within the ocean. These predictions can be compared to existing oceanographic observations, including newly developed techniques that rely on DNA and RNA sequencing to determine metabolic function of the microbial community. This project will support one postdoctoral scholar in this new interface between ocean biogeochemistry modeling, thermodynamics and molecular observations. The project will also support summer internships as part of the Woods Hole Partnership Education Program, a consortium of institutions committed to increasing student diversity in Woods Hole, as well as support two independent undergraduate research projects per year as part of the Semester in Environmental Science Program at the Marine Biological Laboratory (MBL). A workshop will be held in year 2 of the project to broaden exposure of thermodynamic approaches in marine biogeochemistry and explore its place in the broader context of recent advances in metabolic modeling and theory. Ocean model code developed during the project will be open source and publicly disseminated.This project builds upon the Darwin Project, a trait and selection based modeling approach for describing marine plankton communities and biogeochemical cycles. The approach relies on local competition to select from a diverse population and determines the functional characteristics of microorganisms that mediate biogeochemical cycles. The project will combine this selection-based modeling approach with a distributed metabolic network perspective previously developed to facilitate calculating reaction thermodynamics. This will provide mechanistic and quantitative description of key metabolic functions and allow the new model to be directly mappable to omics-based observations. The project will utilize new modeling design criteria based on the maximum entropy production (MEP) conjecture to determine allocation of metabolic machinery and its expression, such as metabolic switching between nitrogen fixation and ammonium uptake. Model testing will rely on existing oceanographic surveys and observations. Once validated, the coupled model will be used to investigate losses of functional biodiversity, generalist versus specialists, temporal planktonic strategies as well as losses in community complementarity on ecosystem biogeochemistry. A significant output from the project will be a predicted global biogeography map of metabolic function and expression (such as nitrogen fixation and ammonium oxidation) that can be tested with, and used to interpret, directed omics observations.

预测海洋化学和生物学将如何应对全球变化是社会的一个紧迫问题。 这个项目将开发新的建模技术，用于预测这种变化，使用来自热力学子学科物理学的思想，关注能量如何在系统中移动。 随着时间的推移，系统热力学的最新进展表明，系统将内部组织，以最大限度地提高能量的流动和耗散。 例如，夏季海洋和大气之间形成的温差促使飓风（有组织的结构）的形成，飓风的存在加速了温差的消散。 该项目利用这一基本特性，但将其扩展到微生物群落，如细菌和浮游植物，它们构成海洋食物网的基础，并对海洋化学产生重大影响。基于生物如何利用太阳能和化学能从环境中的碳，氮，磷和其他元素构建自身的信息，该模型可以预测代谢功能，如光合作用或大气中的固氮作用，如何在海洋中随时间和空间表达。 这些预测可以与现有的海洋学观测进行比较，包括新开发的依赖DNA和RNA测序来确定微生物群落代谢功能的技术。 该项目将支持一名博士后学者在海洋地球化学建模，热力学和分子观测之间的新接口。 该项目还将支持暑期实习作为伍兹霍尔合作教育计划的一部分，该计划是一个致力于增加伍兹霍尔学生多样性的机构联盟，并支持每年两个独立的本科生研究项目作为海洋生物实验室（MBL）环境科学项目学期的一部分。 将在该项目的第二年举办一个讲习班，以扩大海洋生物地球化学热力学方法的范围，并探讨其在代谢建模和理论最新进展的更广泛背景下的地位。 在该项目期间开发的海洋模型代码将是开放源代码并公开传播。该项目建立在达尔文项目的基础上，该项目是一种基于特征和选择的建模方法，用于描述海洋浮游生物群落和生物地球化学循环。该方法依赖于本地竞争，从不同的人群中选择，并确定介导微生物地球化学循环的微生物的功能特性。 该项目将结合联合收割机这种选择为基础的建模方法与分布式代谢网络的角度以前开发的，以方便计算反应热力学。这将提供关键代谢功能的机制和定量描述，并允许新模型直接映射到基于组学的观察。 该项目将利用基于最大熵产生（MEP）猜想的新建模设计标准来确定代谢机制及其表达的分配，例如固氮和铵吸收之间的代谢转换。模型测试将依靠现有的海洋调查和观测。一旦得到验证，耦合模型将用于调查功能生物多样性的丧失、通才与专家、时间浮游策略以及生态系统生物地球化学群落互补性的丧失。该项目的一个重要成果将是一个预测的全球代谢功能和表达（如固氮和铵氧化）的地理图，可以用定向组学观察进行测试，并用于解释。