CAREER: Harnessing Emergent Simplicity for High-Precision Predictions in High-Diversity Microbial Ecosystems

职业：利用新兴的简单性在高多样性微生物生态系统中进行高精度预测

• 批准号: 2340791
• 负责人: Mikhail Tikhonov
• 金额: $ 65.88万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340791&HistoricalAwards=false
• 关键词: CAREER; Harnessing; Emergent; Simplicity; Precision

Microbial communities play a defining role in global climate, agriculture, food safety, and environmental health. Many of their practically relevant properties (for example, rate of consumption of a contaminant) result from the interactions of many species, making them challenging to predict even in the simplified laboratory conditions. Natural systems are even more complex, often composed of hundreds of interacting species, and one expects predictions to be even more difficult. However, recent evidence shows that, at least in some cases, communities with many species may in fact obey simpler "emergent" relationships. This project will develop a body of theory explaining this empirical observation; use methods of physics to classify the kinds of emergent simplification that ecosystem properties can exhibit; adapt this understanding into a methodology for disentangling the biological mechanism of emergent community function, and validate this methodology in a model complex ecosystem (minimally processed agricultural soil). The research is integrated with educational activities integrating curriculum changes and high school outreach, aiming to rebrand the Physics major as the intellectual home for students passionate about working with highly quantitative real-world data. This will be achieved by creating an integrated three-level set of courses (for high school students, freshmen and advanced undergraduates), built on common library of data-driven projects that foster curiosity and focus on relatable real-life questions, and collaborating with high school teachers in the St. Louis area to integrate “Physicists as data wizards” angle into lesson plans at grades 9-10.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.

微生物群落在全球气候、农业、食品安全和环境健康中发挥着决定性作用。它们的许多实际相关特性（例如，污染物的消耗速率）来自许多物种的相互作用，即使在简化的实验室条件下也难以预测。自然系统甚至更为复杂，通常由数百个相互作用的物种组成，人们预计预测会更加困难。然而，最近的证据表明，至少在某些情况下，有许多物种的社区实际上可能遵循更简单的“涌现”关系。该项目将开发一套理论来解释这一经验观察;使用物理学方法对生态系统特性可能表现出的紧急简化进行分类;将这种理解转化为一种方法，用于解开紧急社区功能的生物机制，并在模型复杂生态系统（最低限度处理的农业土壤）中验证这种方法。 该研究与整合课程变化和高中外展的教育活动相结合，旨在将物理专业重新打造为热衷于使用高度定量现实数据的学生的智力家园。这将通过创建一套综合的三级课程来实现（高中生，新生和高年级本科生），建立在数据驱动的项目，培养好奇心和关注相关的现实生活中的问题，并与圣路易斯地区的高中教师合作，将“物理学家作为数据向导”的角度纳入9年级的课程计划-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。