Automated, model-guided phenotyping to identify metabolite/gene/microbe interactions

项目摘要/摘要 DNA测序催生了“微生物组革命”--数以千计的微生物和数量惊人的 与人类健康和疾病相关的微生物相互作用。不幸的是，世界上的大多数物种 微生物组只为(部分)基因组所知。新发现的细菌的有限表型数据 揭示出行为与我们的任何模式生物都不同的物种。虽然基因组规模的建模扮演着 在理解微生物组中的重要作用，大多数物种的表型数据的缺乏阻碍了 详细模拟影响我们健康的微生物群落。 该项目将开发一个用于分析、合成和模拟微生物群落的自动化系统。 我们方法的中心是深度表型，这是一个执行复杂增长的自动化机器人平台 按需进行实验。来自深度表型的数据将用于训练新陈代谢和统计模型 口腔病原体变形链球菌和白色念珠菌预测保持这两种微生物的条件 处于非致病状态。

Project Summary/Abstract DNA sequencing has spawned the “microbiome revolution” -- thousands of microbes and a dizzying number of microbial interactions that are associated with human health and disease. Unfortunately, most species in the microbiome are known only by a (partial) genome. The limited phenotypic data on newly discovered bacteria reveal species that behave unlike any of our model organisms. While genome-scale modeling plays an important role in understanding the microbiome, the paucity of phenotypic data for most species prevents detailed simulation of the microbial communities that affect our health. This project will develop an automated system for profiling, synthesizing, and modeling microbial communities. The center of our approach is Deep Phenotyping, an automated robotic platform that performs complex growth experiments on demand. Data from Deep Phenotyping will be used to train metabolic and statistical models of the oral pathogens Streptococcus mutans and Candida albicans to predict conditions that keep both microbes in a nonpathogenic state.