论文标题
微生物相互作用的高通量表征的统计数据
Statistics of High-Throughput Characterization of Microbial Interactions
论文作者
论文摘要
研究感兴趣的积极领域是对复杂微生物群落的生态模型的推断。推断这种生态模型需要了解微生物之间的相互作用以及它们如何影响彼此的成长。该论文采用统计观点来进一步促进当前解决此问题的知识。第一部分解释了如何使用高通量液滴的微流体技术来筛选微生物相互作用。明确的统计框架是动机和开发的，可以指导来自此类实验的数据分析。第二部分解释了如何根据实验设置预测如何生成多少数据来推断微生物相互作用。在不孵育液滴的情况下运行实验是做出这样的预测所必需的。第三部分证明了从这些实验产生的数据中推断微生物相互作用的可行性。微生物学和生态文献中的相关思想被重塑为明确的统计框架。
An active area of research interest is the inference of ecological models of complex microbial communities. Inferring such ecological models entails understanding the interactions between microbes and how they affect each other's growth. This dissertation employs a statistical perspective to contribute further to the knowledge currently addressing this problem. Part I explains how high-throughput droplet-based microfluidics technology can be used to screen for microbial interactions. An explicit, statistical framework is motivated and developed that can guide the analysis of data from such experiments. Part II explains how it might be possible to predict, based on the experimental setup, how much data will be produced to infer given microbial interactions. Running the experiment once without incubating the droplets turns out to be necessary to make such predictions. Part III demonstrates the feasibility of inferring microbial interactions from the data produced by these experiments. Relevant ideas from the microbiological and ecological literature are recast into an explicit, statistical framework.
