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的生物有机体之间转移，是最普遍的自然法则之一。食物链上游的消费是一个天生低效的过程，会通过浪费和呼吸导致重大的、可预见的损失。这一生命法则可以用来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，这是粮食生产中的一个重要制约因素。到目前为止，食物网的研究主要集中在植物和动物之间的相互作用上，然而，生活在大型有机体内和大型有机体上的微生物对它们的健康、繁殖率和消化食物的能力起着至关重要的作用。这个项目将研究共生微生物如何管理食物网的效率，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洞察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，并在农业、水产养殖和生物燃料系统中提高产量。此外，还将开展劳动力发展，并向代表不足的群体，包括夏威夷土著和太平洋岛民开展外联活动。博士后研究人员、研究生和本科生将通过研究经验和课堂模块接受微生物组科学方面的培训。这一建议解决了这样一种假设，即控制食物网营养水平之间能量转移的规范定律既制约着微生物群的组成和功能，也受到微生物群的组成和功能的制约。利用夏威夷分水岭模型系统，该项目旨在了解与宿主相关的微生物群如何管理食物链效率，以及食物网中的营养位置如何影响微生物群。该项目将根据在较高特征数据集(如地球微生物组项目)上培训的机器学习工具，开发转移学习方法，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。第一个是一个简单的四层菠萝食物网中围体，其中营养水平的数量和数量受到控制。第二个包括一个三层的蚊子微观世界，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使人们能够机械地理解微生物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，这个项目将使用食物网理论来解释和预测微生物群、宿主和环境之间的相互作用。这个项目由生命的理解规则：微生物群相互作用和机制计划和既定的刺激竞争研究计划(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优势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.