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

控制食物网动态的规则描述了能量如何在不同生物体之间转移，是最普遍的自然法则之一。食物链上游的消费本来就是一个低效的过程，会通过浪费和呼吸造成重大和可预测的损失。这一生命规律可以用来模拟生物多样性如何应对环境突变或物种灭绝等现象，也是粮食生产的一个重要制约因素。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表面的微生物在其健康，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反过来决定共生微生物的组成。从这项研究中获得的预测性见解可能使操纵微生物的组成以创造更有效的食物网成为可能，这些食物网可能指导退化栖息地的恢复，捕获碳，并增加农业，水产养殖和生物燃料系统的产量。此外，还将发展劳动力，并向包括夏威夷土著人和太平洋岛民在内的代表性不足的群体开展外联活动。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进行微生物组科学培训。 这一建议解决了这样一个假设，即控制食物网营养级之间能量转移的规范定律既约束微生物组的组成和功能，又受到微生物组的组成和功能的约束。利用夏威夷流域系统模型，该项目旨在了解宿主相关微生物组如何控制食物链效率，以及食物网内的营养位置如何影响微生物组。该项目将开发基于机器学习工具的迁移学习方法，这些机器学习工具在更高特征的数据集（如地球微生物组项目）上进行训练，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稳健预测。两个易于处理的实验系统将被用来探索这些预测。第一个是一个简单的四层凤梨食物网中生态系统的数量和营养级的控制。第二个由一个三层的蚊子微观世界组成，其中所有的微生物共生体都被隔离和操纵。相关的基因组数据将有助于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，本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。本项目由Understanding Rules of Life联合资助：微生物组相互作用和机制计划以及刺激竞争研究的既定计划（EPSCoR）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.