MIM: Using Machine Learning and a Model Watershed to Understand how Microbes Govern Food Web Architecture and Efficiency

MIM：使用机器学习和模型分水岭来了解微生物如何控制食物网架构和效率

• 批准号: 2124922
• 负责人: Anthony Amend
• 金额: $ 249.94万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124922&HistoricalAwards=false
• 关键词: MIM; Using; Machine; Learning; Model

Rules that govern food web dynamics, which describe how energy is transferred among different living organisms, are among the most universal laws of nature. Consumption up a food-chain is an inherently inefficient process that leads to significant and predictable losses through waste and respiration. This rule of life can be leveraged to model how biological diversity will respond to phenomena such as sudden changes in the environment or species extinctions, and is an important constraint in food production. Until now, food web research has largely focused on the interactions among plants and animals, however, microbes living in and on larger organisms play essential roles in their health, rates of reproduction, and ability to digest food. This project will examine how symbiotic microbes govern the efficiency of food webs, and how aspects of food webs, in turn, determine the composition of symbiotic microbes. The predictive insight gained from this research may make it possible to manipulate the composition of microbes to create more efficient food webs that can potentially guide restoration of degraded habitats, capture carbon, and increase yield in agriculture, aquaculture and biofuels systems. In addition, workforce development and outreach to under-represented groups including native Hawaiians and Pacific Islanders, will be performed. Postdoctoral researchers, graduate students and undergraduates will be trained in microbiome science through research experiences and class modules. This proposal addresses the hypothesis that canonical laws governing the transfer of energy among trophic levels of food webs both constrain, and are constrained by the composition and function of microbiomes. Leveraging a model Hawaiian watershed system, this project aims to understand how host-associated microbiomes govern food chain efficiency and how, in turn, trophic position within a food web affects the microbiome. The project will develop transfer learning approaches based on machine-learning tools trained on higher-feature datasets (such as the Earth Microbiome Project) to enable robust predictions of the interaction between food chain length, trophic position and microbiome diversity. Two tractable experimental systems will be used to explore these predictions. The first is a simple four-tiered bromeliad food web mesocosm where the number and of trophic levels is controlled. The second consists of a three-tiered mosquito microcosm in which all microbial symbionts are isolated and manipulated. Associated genomic data will enable a mechanistic understanding of how microbiomes influence food web efficiency and function by altering metabolic capacity of hosts. In summary, this project will employ food web theory to explain and predict the interactions between the microbiome, the host, and the environment.This project is jointly funded by the Understanding Rules of Life: Microbiome Interactions and Mechanisms Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

控制食物网动态的规则描述了能量如何在不同的生物体之间转移，是最普遍的自然法则之一。食物链的消费本质上是一个低效的过程，会因浪费和呼吸而导致重大且可预测的损失。这一生命规则可用于模拟生物多样性如何应对环境突然变化或物种灭绝等现象，并且是粮食生产的重要限制。到目前为止，食物网研究主要集中在植物和动物之间的相互作用，然而，生活在较大生物体内和体表的微生物在其健康、繁殖率和消化食物的能力方面发挥着重要作用。该项目将研究共生微生物如何控制食物网的效率，以及食物网的各个方面如何决定共生微生物的组成。从这项研究中获得的预测见解可能使操纵微生物的组成以创建更有效的食物网成为可能，从而有可能指导退化栖息地的恢复、捕获碳并提高农业、水产养殖和生物燃料系统的产量。此外，还将开展劳动力发展和对代表性不足的群体（包括夏威夷土著和太平洋岛民）的外展活动。博士后研究人员、研究生和本科生将通过研究经验和课程模块接受微生物组科学方面的培训。 该提案提出了这样一个假设：控制食物网营养级之间能量转移的规范法则既限制微生物组的组成和功能，又受到微生物组的组成和功能的限制。该项目利用夏威夷流域系统模型，旨在了解宿主相关微生物组如何控制食物链效率，以及食物网中的营养位置如何影响微生物组。该项目将开发基于在更高特征数据集（例如地球微生物组项目）上训练的机器学习工具的迁移学习方法，以实现对食物链长度、营养位置和微生物组多样性之间相互作用的可靠预测。将使用两个易于处理的实验系统来探索这些预测。第一个是简单的四层凤梨科植物食物网中生态系统，其中营养级的数量和营养级受到控制。第二个由三层蚊子微观世界组成，其中所有微生物共生体都被隔离和操纵。相关的基因组数据将能够从机制上理解微生物组如何通过改变宿主的代谢能力来影响食物网的效率和功能。总之，该项目将利用食物网理论来解释和预测微生物组、宿主和环境之间的相互作用。该项目由理解生命规则：微生物组相互作用和机制计划和刺激竞争性研究既定计划（EPSCoR）共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，认为值得支持。 标准。