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 人类肠道内的微生物群落广泛地影响宿主健康。工程 因此，微生物群落的动态是新疗法的承诺方向。但是，微生物 在一个社区内部，通过各种机制影响彼此的成长，这些机制相对重要 尚不清楚，阻碍了现有模型对社区动态的预测能力。解决这个问题 知识差距，我提出了实验和数学建模方法来分解和测量 各种相互作用机制的优势。 我的建议的关键是我们实验室的强大社区和来自小鼠凳子的微生物隔离株 在实验室培养物中具有与gnotobiotic小鼠的肠道相似的组成。他们使我能够 在实验室文化中组装和扰动社区，同时模仿与宿主健康相关的行为。指导 通过代表微生物作为消费者和环境资源生产者的数学模型， 以及其他潜在相互作用机制的试剂，我将组装分离株的不同组合 并测量其生长特性以量化其相互作用机制。例如，增长量 在培养基中，一个物种在另一个物种的生长中花费的一种物种反映了重叠的量 这两个物种消耗的资源。我将从两个其他角度推断互动机制 通过量化社区增长期间的环境代谢物，并研究 随着时间的流逝，物种丰富的体内波动。这三种方法将集成高通量 进行数学建模的实验，以系统地测量各种相互作用的重要性 机制，并为任何微生物社区生成一个框架。在一起的结果将 地面物种相互作用机械地赋予了微生物群落的工程能力。 我的跨学科建议利用了我在物理学方面的博士学位培训，尤其是统计物理学和 复杂系统和细菌生理学的建模。它还将在高通量表型中训练我 （下一代测序和质谱代谢组学）微生物群落，这将有助于 我实现了我的职业目标，以领导一个工程师微生物社区使社会受益的实验室。我的 赞助科学家Kerwyn Casey Huang博士在斯坦福大学生物工程系非常出色 计划的导师。他的跨学科实验室桥梁现象从单分子到多物种量表 使用物理和生物学技术，并与在Microbiota Research的领先实验室紧密合作 斯坦福大学。那是在我的提议中追求思想的理想环境。我也会积极参与 我提出的项目的本科生和研究生。