Building Predictive Coarse-Graining Schemes for Complex Microbial Ecosystems

为复杂的微生物生态系统构建预测粗粒度方案

• 批准号: 2310746
• 负责人: Mikhail Tikhonov
• 金额: $ 48.58万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310746&HistoricalAwards=false
• 关键词: Building; Predictive; Coarse; Graining; Schemes

Microbial communities play a defining role in global climate, agriculture, food safety, and environmental health. These systems are highly complex, often composed of hundreds of interacting species, which makes it very difficult to predict their behavior in detail. However, it is known that important ecosystem properties (e.g., the overall production of a nutrient) can sometimes be predicted without tracking every species, instead using simpler, coarse representations. Leveraging such "coarse-grainability" could transform our ability to predict and control these systems, but the phenomenon remains poorly understood: which properties are expected to be coarse-grainable, and under what conditions, is not known. This project will draw on methods of theoretical physics, statistics and learning theory to develop a systematic theory of ecosystem coarse-grainability, validate it experimentally, and translate it into practical software tools disseminated to the broader research community. This work will significantly advance our ability to model and predict the behavior of microbial ecosystems of complexity as found in nature, such as those responsible for global nutrient cycling, nitrogen fixation or other key environmental functions. On a more technical level, the objectives of the project include: (1) Establishing the theoretical and computational methodology for quantifying ecosystem coarse-grainability and identifying the Pareto front of the tradeoff between description complexity and its prediction error for a given community-level observable; (2) Validating this approach in the laboratory, applying it to communities of marine bacteria assembled in hundreds of environments spanning the axes of variation relevant for ocean ecosystems; and finally, (3) Translating the methodology into shareable software for identifying observable-specific predictive coarsening of compositional microbial data. Additionally, this award will support outreach activities targeting students of grades 6-12 in the St Louis area, emphasizing how the pursuit of predictable models of complex systems unites diverse areas of science (math, physics, biology and medicine, including this research), and increase the participation of underrepresented groups in research through the MIT Summer Research Program initiative.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。这些系统非常复杂，通常由数百个相互作用的物种组成，这使得很难详细预测它们的行为。然而，已知重要的生态系统特性（例如，一种营养素的总产量）有时可以不跟踪每一个物种，而是使用更简单、粗略的表示来预测。利用这种“粗粒度”可以改变我们预测和控制这些系统的能力，但对这种现象仍然知之甚少：哪些属性预计是粗粒度的，以及在什么条件下，尚不清楚。该项目将利用理论物理学、统计学和学习理论的方法，开发一个系统的生态系统粗粒化理论，通过实验对其进行验证，并将其转化为实用的软件工具，传播给更广泛的研究界。这项工作将大大提高我们建模和预测自然界中复杂微生物生态系统行为的能力，例如负责全球营养循环，固氮或其他关键环境功能的微生物生态系统。在技术层面上，该项目的目标包括：（1）建立量化生态系统粗粒度性的理论和计算方法，并确定给定社区级观测值的描述复杂性和预测误差之间的Pareto前沿;（2）在实验室中验证该方法，将其应用于数百个环境中的海洋细菌群落，这些环境跨越与海洋生态系统相关的变化轴;最后，（3）将该方法转化为可共享的软件，用于识别组成微生物数据的可观察特异性预测粗化。此外，该奖项将支持针对圣刘易斯地区6-12年级学生的外展活动，强调对复杂系统可预测模型的追求如何将不同的科学领域结合起来（数学，物理，生物和医学，包括这项研究），并通过麻省理工学院夏季研究计划倡议增加代表性不足的群体在研究中的参与。该奖项反映了NSF的法定使命，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。

项目成果

Statistically learning the functional landscape of microbial communities

统计了解微生物群落的功能景观

• DOI: 10.1038/s41559-023-02197-4
• 发表时间: 2023
• 期刊: Nature Ecology & Evolution
• 影响因子: 16.8
• 作者: Skwara, Abigail;Gowda, Karna;Yousef, Mahmoud;Diaz-Colunga, Juan;Raman, Arjun S.;Sanchez, Alvaro;Tikhonov, Mikhail;Kuehn, Seppe
• 通讯作者: Kuehn, Seppe