eMB: Collaborative Research: ML/AI-assisted environmental scale microbial nonlinear metabolic models

eMB：协作研究：ML/AI 辅助的环境规模微生物非线性代谢模型

• 批准号: 2325172
• 负责人: Karsten Zengler
• 金额: $ 12.5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325172&HistoricalAwards=false
• 关键词: eMB; Collaborative; Research; ML; AI

Our world is dominated in many respects by communities of single celled organisms, e.g., bacteria, archaea and non-filamentous fungi. Such communities are key players in all geochemical cycles; they are present in all multicellular organisms, including humans, where in addition to possibly harmful effects, they also have essential beneficial roles. They are ubiquitous in engineered systems as well where, again, they can be beneficial (e.g., waste water treatment) or harmful (e.g., drinking water distribution). In large part, microbial communities interactions with their environments are metabolic through chemicals they take in, products they make with these inputs, and byproducts they excrete, so understanding these metabolic capabilities is essential for understanding how microbes effect their surroundings. Advances in genetic sequencing are making it easier and easier to determine microbial "machinery" (enzymes); researchers are becoming increasingly adept in predicting how this "machinery" combines into "assemblage lines" (metabolic pathways). Armed with this knowledge, the next step is to understand how these "assembly lines" fit into their environment into a sort of large scale "distribution system" that determines overall microbial community function. At large scale, this becomes a challenging computational problem, and current methods are not adequate. This project aims to accelerate these computations by introducing machine learning tools into key bottlenecks in the algorithms. The project is a collaboration between Temple University, Montana State University, and the University of California, San Diego and offers valuable educational, training, and outreach opportunities. Activity funded by this proposal would center on development of computational methods, based on machine learning and artificial intelligence assisted optimization, that are sufficiently efficient so as to make it possible to embed complex cell-scaled models of microbial behavior (metabolic and gene expression models, so-called ME modes) into environmental scale PDE-based models of microbial community activity. In support of this effort, ME models of several specific organisms (S. aureus, S. epidermidis, B. subtilis) will be adapted for use in specific environmental-scale models (basic biofilm communities and built-environment subaerial communities). In complement, environmental-scale continuum-mechanics-based (partial differential equation) models for these systems will be constructed and adapted for use with the new computational methods. AI will also be applied at this macroscale to attempt to identify key metabolic processes at the large scale.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.

我们的世界在许多方面都受到单细胞生物的群落的统治，例如细菌，古细菌和非丝虫真菌。这些社区是所有地球化学周期中的主要参与者。它们存在于包括人类在内的所有多细胞生物中，除了可能有害影响，它们还具有重要的有益作用。它们在工程系统中也无处不在，同样，它们可能是有益的（例如废水处理）或有害的（例如饮用水分配）。在很大程度上，微生物群落与环境的相互作用是通过所吸收的化学物质，与这些输入制造的产品以及它们排泄的副产品相互作用的，因此了解这些代谢能力对于了解微生物如何影响其周围环境至关重要。遗传测序的进步使确定微生物“机械”（酶）变得越来越容易。研究人员越来越擅长预测这种“机械”如何结合到“组合线”（代谢途径）中。有了这些知识，下一步是了解这些“组装线”如何适应其环境中的大规模“分配系统”，从而决定了整体微生物社区功能。在大规模的情况下，这成为一个具有挑战性的计算问题，并且当前方法不足。该项目旨在通过将机器学习工具引入算法中的关键瓶颈来加速这些计算。该项目是坦普尔大学，蒙大拿州立大学和加利福尼亚大学圣地亚哥分校之间的合作，并提供有价值的教育，培训和外展机会。 该提案资助的活动将基于机器学习和人工智能辅助优化的计算方法的发展，这些优化非常有效，以便可以将微生物行为（代谢和基因表达模型，所谓的ME模式，所谓的ME模式）嵌入基于环境规模的Microbobial Commusity活动模型中。为了支持这项工作，将对几种特定生物（S. Aureus，S。epidermidis，B。枯草芽孢杆菌）的ME模型进行调整，以用于特定的环境规模模型（基本生物膜社区和内置环境下群社区）。在补充中，将构建并调整基于环境尺度的连续力学（部分微分方程）模型（部分微分方程）模型，以与新的计算方法一起使用。 AI还将在此宏观上应用，以试图在大规模上确定关键的代谢过程。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。