Predicting the assembly and function of microbial consortia: a systems biology approach

项目摘要/摘要 任务和背景：我是一名系统生物学家，我的研究项目专注于构建 微生物群落聚集的预测定量模型。微生物几乎栖息在地球上的每一个表面 它们很少单独被发现。相反，它们形成了复杂的生态群落， 组成和集体代谢对生物地球化学循环具有深远的影响，人类和 动物健康、农业和工业。我研究的最终目标是开发一种量化的， 预见性地了解微生物群落如何形成以及它们如何响应外部和 环保驱动因素。我们的研究将揭示合理的策略来设计合成微生物联合体 以生物技术为目的，并将提供对养分转移和其他生态过程的见解 可用于在外部操纵天然微生物群落的组成和功能。 我实验室的工作概述：我们的工作结合了计算、实验和生态学理论。 在实验上，我们监测了自然微生物群落的自组装，在受控良好的 定义的合成环境。这些环境的营养成分可以随意调节， 使我们能够定量地绘制出这导致的微生物分类结构和功能的变化 社区。我们的结果与我们已经采用的生态学理论的预测进行了比较。 微生物群落。最后，我们使用全基因组计算新陈代谢模型 每个物种的代谢网络作为输入，并预测群落的新陈代谢和动态。我们 已经能够证明社区集会在家庭和功能上遵循可预测的模式 合成环境中的水平。这些可以从第一代谢原理中得到理解和解释， 并与生态学理论的预测相一致。 未来方向：在未来五年，我们计划询问环境资源如何量化 影响微生物群落的组成和功能。我们能预测小说中的社区集会吗 新陈代谢环境？我们能从基因组中预测碳源利用的数量模式吗 信息?高阶相互作用、促进和非传递性竞争在 构建微生物群落的自发组装？计算和实验 我作为哈佛大学独立博士后在过去五年里开发的方法和系统 以及耶鲁大学的终身教职员工，这让我的实验室在解决这些问题方面具有独特的地位。我们的发现将 代表着开发微生物群落组装的量化、预测性模型的一个里程碑。

Project Summary/Abstract Mission and background: I am a systems biologist, and my research program is focused on building predictive quantitative models of microbial community assembly. Microbes inhabit virtually every surface on earth, and they are rarely found alone. Rather, they form complex ecological communities whose composition and collective metabolism has profound implications for biogeochemical cycles, human and animal health, agriculture, and industry. The ultimate goal of my research is to develop a quantitative, predictive understanding of how microbial communities form and how they respond to external and environmental drivers. Our research will reveal rational strategies to design synthetic microbial consortia for biotechnology purposes, and will provide insights into how nutrient shifts and other ecological processes may be used to externally manipulate the composition and function of natural microbial communities. Overview of work in my laboratory: Our work combines computation, experiment, and ecological theory. Experimentally, we monitor the self-assembly of natural microbial communities in well-controlled and defined synthetic environments. The nutrient composition of these environments can be modulated at will, allowing us to quantitatively map the shifts this induces on the taxonomic structure and function of microbial communities. Our results are compared with the predictions of ecological theory, which we have adapted for microbial communities. Finally we use genome-wide computational metabolic models that take the metabolic networks from each species as inputs and predict community metabolism and dynamics. We have been able to show that community assembly follows predictable patterns at the family and functional levels in synthetic environments. These can be understood and interpreted from first metabolic principles, and are consistent with the predictions of ecological theory. Future directions: Over the next five years, we plan to ask how environmental resources quantitatively affect the composition and function of microbial communities. Can we predict community assembly on novel metabolic environments? Can we predict quantitative patterns of carbon source utilization from genomic information? How prevalent are high-order interactions, facilitation, and non-transitive competition in structuring the spontaneous assembly of microbial communities? The computational and experimental methods and systems that I have developed over the past five year as an independent postdoc at Harvard and a tenure track faculty at Yale, uniquely position my lab to address these questions. Our findings will represent a milestone towards developing quantitative, predictive models of microbial community assembly.