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.

项目摘要 人体肠道内的微生物群落广泛而显著地影响宿主健康。工程的 因此，微生物群落的动力学是新疗法的有希望的方向。然而，微生物 在一个社区内，通过各种各样的机制影响彼此的成长，这些机制的相对重要性 目前尚不清楚，阻碍了现有模型对社区动态的预测能力。为了解决这个 知识差距，我提出了实验和数学建模方法来解开和测量 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一套社区和微生物分离来自小鼠粪便 其在实验室培养物中具有与在gnotobiotic小鼠的肠道中相似的组成。它们使我能够 在实验室培养中聚集和扰乱群落，同时模仿与宿主健康相关的行为。指导 通过数学模型将微生物代表为环境资源的消费者和生产者， 以及其他潜在相互作用机制的代理人，我将组装不同的分离物组合， 并测量它们的生长特性以量化它们的相互作用机制。例如， 一个物种在另一个物种生长所消耗的培养基中的含量反映了 这两个物种消耗的资源。我将从另外两个角度推断相互作用机制 通过对群落生长过程中环境代谢物的定量分析， 体内物种丰度随时间的波动。这三种方法将整合高吞吐量 用数学模型进行实验，系统地测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。总之，结果将 地面物种的相互作用机制，赋予微生物群落的工程。 我的跨学科建议利用了我在物理学，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 （下一代测序和质谱代谢组学）的微生物群落，这将有助于 我实现了我的职业目标，领导一个实验室，工程微生物群落，造福社会。我 赞助科学家凯西黄博士在斯坦福大学生物工程系是一个优秀的 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，并与领先的微生物群研究实验室密切合作， 斯坦福大学。因此，这是一个理想的环境，以推行我的建议中的想法。我也会积极参与 本科生和研究生在我提出的项目。