INSPIRE Track 1: Microbial systems biology in freshwater lakes: a new framework for scaling from genes to ecosystems

该INSPIRE奖的部分资金来自生物科学局环境生物学部门的生态系统研究计划和工程局化学、生物工程、环境和运输系统部门的环境工程计划。微生物在所有生态系统中都是关键的，但往往被忽视。它们对生态系统的恢复力有巨大的影响，对预测从气候变化到土地利用变化和管理等各种驱动因素的反应和反馈都有影响。然而，将微生物及其巨大的多样性纳入预测性环境模型一直是一项挑战。具体地说，需要取得重大进展，以解决阻碍微生物基因组信息融入生态系统规模科学的知识差距。解决方案将是高度跨学科的，微生物学家、生态学家、计算生物学家和工程师将做出贡献。该项目旨在利用基因组数据来确定淡水湖中的微生物如何控制碳循环和水质，并将这种新的理解转化为预测湖泊行为的预测计算模型。环境工程师在量化水质模型方面有着悠久的历史，但他们并没有将微生物纳入其中。生态系统科学家研究生态系统中物质和能量的流动，但他们很少考虑微生物，而且通常不会根据第一原理构建模型。这项工作将把微生物学、生态系统科学和环境工程这三个很少整合的学科结合在一起。这项工作将对科学以及教育和推广工作产生广泛影响。微生物生物学家和生态学家渴望找到新的方法，将复杂的数据集和基本概念转化为理解和预测。微生物学、生态学和工程学领域的学生和博士后需要在计算生物学、基因组学和建模方面进行交叉培训。他们将需要推动他们的科学不那么描述性，更具预测性，这需要超越这些学科当前研究生课程结构的计算和综合思维。该项目将利用相关的网络科学倡议，如全球湖泊生态观测站网络(GLEON)和新成立的基因组观测站(GOS)网络。最后，我们使微生物生态系统科学与广大公众相关的能力取决于说明微小实体如何对高价值生态系统服务产生重大影响。淡水湖是实现这一目标的优秀系统，因为水质与微生物密切相关，对当地经济活动、生态系统管理决策和公众对生态系统健康的看法具有重要反馈作用。

This INSPIRE award is partially funded by the Ecosystem Studies Program in the Division of Environmental Biology in the Directorate for Biological Sciences and the Environmental Engineering Program in the Division of Chemical, Bioengineering, Environmental, and Transport Systems in the Directorate for Engineering.Microbes are critical, but often ignored, players in all ecosystems. They have tremendous influence on ecosystem resilience, with implications for predicting the response and feedback to drivers ranging from climate change to land-use change and management. However, it has been challenging to incorporate microbes and their vast diversity into predictive environmental models. Specifically, key advances are needed to address the knowledge gaps that prevent integration of microbial genomic information into ecosystem-scale science. Solutions will be highly interdisciplinary, with contributions from microbiologists, ecologists, computational biologists, and engineers. This project aims to use genomic data to determine how microbes in freshwater lakes control carbon cycling and water quality, and to convert this new understanding into predictive computational models that forecast how lakes will behave. Environmental engineers have a long history of working quantitatively with water quality models, but they do not incorporate microbes into them. Ecosystem scientists study the flux of matter and energy through ecosystems but they rarely consider microbes and often do not construct models based on first principles. This work will bring together three disciplines that are rarely integrated: microbiology, ecosystem science, and environmental engineering.This work will have broad impacts on science as well as education and outreach efforts. Microbial biologists and ecologists are hungry for new ways to transform complex datasets and fundamental concepts into understanding and prediction. Students and post-docs in fields of microbiology, ecology, and engineering need cross-training in computational biology, genomics, and modeling. They will need to push their science to be less descriptive and more predictive, which requires computational and integrative thinking that transcends the structure of current graduate programs in these disciplines. The project will leverage relevant network science initiatives such as the Global Lake Ecological Observatory Network (GLEON) and the nascent Genomic Observatories (GOs) network. Finally, our ability to make microbial ecosystem science relevant to the broader public depends on illustrating how tiny entities can have large impact on high-value ecosystems services. Freshwater lakes are excellent systems for this purpose since water quality is so tightly linked to microbes, with major feedbacks to local economic activity, ecosystem management decisions, and public perception of ecosystem health.