Mechanistic models for predicting the dynamics of microbial communities

预测微生物群落动态的机制模型

• 批准号: 10490833
• 负责人: Po-Yi Ho
• 金额: $ 7.18万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10490833
• 关键词: 16S ribosomal RNA sequencing; Acceleration; Address; Affect; Algorithms; Antibiotics; Bacterial Physiology; Behavior; Biological; Biomedical Engineering; Coculture Techniques; Collaborations; Collection; Communities; Complex; Consumption; Correlation Studies; Data; Data Set; Doctor of Philosophy; Engineering; Environment; Experimental Models; Feces; Gnotobiotic; Goals; Growth; Health; Human; In Vitro; Investigation; Knowledge; Laboratories; Laboratory culture; Laws; Link; Mass Spectrum Analysis; Measurable; Measures; Mentors; Metabolic; Methods; Microbe; Modeling; Modification; Motivation; Mus; Outcome; Parents; Periodicals; Phase; Phenotype; Physics; Production; Property; Research; Resources; Scientist; Series; Societies; Source; System; Techniques; Therapeutic; Time; Toxin; Training; career; community setting; diverse data; empowerment; experimental study; feeding; graduate student; gut microbiota; in vivo; in vivo Model; interest; mathematical model; member; metabolomics; microbial; microbial community; microbiota; next generation sequencing; novel; novel therapeutics; predictive modeling; reconstitution; response; single molecule; statistics; theories; therapeutic development; therapeutic target; undergraduate student

Project summary Microbial communities within the human gut broadly and significantly affect host health. Engineering the dynamics of microbial communities is therefore a promising direction for new therapeutics. However, microbes within a community affect one another’s growth through a wide variety of mechanisms whose relative importance remain unclear, hindering the predictive capability of existing models for community dynamics. To address this knowledge gap, I propose experimental and mathematical modeling methods to disentangle and measure the strengths of the various interaction mechanisms. Key to my proposal is our lab’s powerful set of communities and microbial isolates derived from mice stool that have similar compositions in laboratory cultures as in the gut of gnotobiotic mice. They enable me to assemble and perturb the communities in lab cultures while mimicking behaviors relevant to host health. Guided by mathematical models that represent microbes as consumers and producers of environmental resources, as well as agents of other potential interaction mechanisms, I will assemble different combinations of the isolates and measure their growth properties to quantify their interaction mechanisms. For example, the amount of growth of one species in the medium spent by the growth of another species reflects the amount of overlap in the resources consumed by these two species. I will infer interaction mechanisms from two additional perspectives by quantifying environmental metabolites during growth of the communities, and investigating the statistics of fluctuations in species abundances over time in vivo. These three approaches will integrate high throughput experiments with mathematical modeling to systematically measure the importance of various interaction mechanisms, and generate a framework to do so for any microbial community. Together, the outcomes will ground species interactions mechanistically, empowering the engineering of microbial communities. My interdisciplinary proposal leverages my PhD training in physics, particularly statistical physics and the modeling of complex systems, and bacterial physiology. It will also train me in high-throughput phenotyping (next-generation sequencing and mass spectrometry metabolomics) of microbial communities, which will help me achieve my career goal to lead a laboratory that engineer microbial communities to benefit society. My sponsoring scientist Dr. Kerwyn Casey Huang in the Stanford Department of Bioengineering is an excellent mentor for the plan. His interdisciplinary lab bridges phenomena from single molecules to the multi-species scale using physical and biological techniques, and collaborates intimately with leading labs in microbiota research at Stanford. Thus, it is the ideal environment to pursue the ideas in my proposal. I will also actively engage undergraduate and graduate students in my proposed projects.

项目概要 人类肠道内的微生物群落广泛而显着地影响宿主健康。工程设计 因此，微生物群落的动态是新疗法的一个有前途的方向。然而，微生物 一个社区内的人通过多种机制影响彼此的成长，这些机制的相对重要性 仍不清楚，阻碍了现有社区动态模型的预测能力。为了解决这个问题 知识差距，我提出实验和数学建模方法来理清和测量 各种互动机制的优势。 我的建议的关键是我们实验室强大的群落和源自小鼠粪便的微生物分离株 它们在实验室培养物中的成分与在限生小鼠肠道中的成分相似。他们使我能够 聚集并扰乱实验室文化中的社区，同时模仿与宿主健康相关的行为。引导 通过将微生物表示为环境资源的消费者和生产者的数学模型， 以及其他潜在相互作用机制的代理，我将组装分离株的不同组合 并测量它们的生长特性以量化它们的相互作用机制。例如，增长量 一个物种在另一物种生长所消耗的培养基中的消耗量反映了该物种的重叠量 这两个物种消耗的资源。我将从另外两个角度来推断交互机制 通过量化群落生长过程中的环境代谢物，并调查统计数据 体内物种丰度随时间的波动。这三种方法将集成高吞吐量 通过数学建模进行实验，系统地衡量各种相互作用的重要性 机制，并为任何微生物群落生成一个框架。共同努力，结果将 地面物种机械地相互作用，增强微生物群落的工程能力。 我的跨学科提案利用了我在物理学方面的博士学位训练，特别是统计物理学和 复杂系统建模和细菌生理学。它还将训练我进行高通量表型分析 微生物群落（下一代测序和质谱代谢组学），这将有助于 我实现了我的职业目标，领导一个改造微生物群落造福社会的实验室。我的 斯坦福大学生物工程系的资助科学家 Kerwyn Casey Huang 博士是一位优秀的科学家 该计划的导师。他的跨学科实验室将单分子现象与多物种规模联系起来 使用物理和生物技术，并与微生物群研究的领先实验室密切合作 斯坦福。因此，这是实现我提案中的想法的理想环境。我也会积极参与 本科生和研究生参与我提出的项目。