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.
翻译:研究感兴趣的一个积极领域是复杂微生物群落的生态模型的推论。这种生态模型的推论要求了解微生物之间的相互作用以及它们如何影响彼此的生长。这一论文从统计角度出发,为目前解决这一问题的知识作出进一步贡献。第一部分解释了如何利用高通量滴子微氟化物技术来筛选微生物相互作用。一个明确的统计框架可以激励和开发,从而指导对此类实验数据的分析。第二部分解释了如何在实验设置的基础上预测将产生多少数据来推断给微生物相互作用。在不孵化滴子的情况下进行一次试验对于作出这种预测是有必要的。第三部分说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。微生物和生态文献的有关想法被重新编成一个明确的统计框架。