Statistical moments to infer microbial phenotypes in communities despite unquantified variables.

定量预测群落动态对于设计具有所需性质的微生物群落非常重要，例如降解废物或生产益生菌。对于相互作用很清楚的群落，尽管可以建立机械模型，但它们与微生物表型对应的参数，如释放或消耗化学品的速度，是出了名的难以测量。例如，如果代谢物被其他物种迅速消耗，则很难测量释放速率；在单一培养中测量的释放速率通常不能捕捉群落环境中的表型。传统上，确定性模型适用于数据，但由于没有足够的信息来约束它们，推断的参数往往是不正确的或不唯一的，这种情况因快速消耗的化学品或酶中间体等未量化的变量而恶化。在这里，为了在没有量化变量的情况下推断力学模型的参数，我将利用实验重复的统计信息来开发SMIP“统计矩推断参数”。我的初步工作表明，基本真实值可以通过以方程的形式施加额外的约束来推断，该方程描述了变量的“统计矩”(例如，均值、方差、协方差)的动力学。在这项提案中，我将1)将SMIP正规化，导出统计矩来推断参数，而不考虑未量化的变量；2)根据硅胶社区的地面事实验证SMIP；以及3)在我将在实验中测量的两个微生物系统的时间序列中测试SMIP。我的研究不仅将帮助实验者克服在相关群落环境中量化微生物表型的挑战，而且还将促进预测合成生态学。这一行动将帮助我整合我对理论和实验研究的兴趣，培养成功的跨学科独立职业生涯所需的技能。

Quantitatively predicting community dynamics is important for engineering microbial communities with desired properties, e.g. degrading waste or producing probiotics. For communities with well-understood interactions, although mechanistic models can be constructed, their parameters which correspond to microbial phenotypes such as rates of release or consumption of chemicals, are notoriously difficult to measure. For example, release rates are difficult to measure if metabolites are rapidly consumed by another species; and rates measured in monocultures often do not capture phenotypes in the community setting. Traditionally, deterministic models are fitted to data, but the inferred parameters are often incorrect or not unique due to insufficient information to constrain them, a situation worsened by unquantified variables such as rapidly consumed chemicals or enzyme intermediaries. Here, to infer parameters of mechanistic models despite unquantified variables, I will develop SMIP "Statistical Moments to Infer Parameters" by taking advantage of the statistical information of experimental replicates. My preliminary work indicates that ground truth values can be inferred by imposing additional constraints in the form of equations that describe the dynamics of "statistical moments" (e.g. mean, variance, covariance) of variables. In this proposal, I will 1) formalize SMIP, deriving statistical moments to infer parameters despite unquantified variables; 2) validate SMIP against ground truth in silico communities; and 3) test SMIP in time series of two microbial systems that I will measure in experiments. My research will not only help experimentalists overcome the challenge of quantifying microbial phenotypes in the relevant community environment but also facilitate predictive synthetic ecology. This action will help me to integrate my interests in theoretical and experimental research, developing the skills necessary for a successful interdisciplinary independent career.