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

尽管存在未量化的变量，但仍可推断群落中微生物表型的统计时刻。

• 批准号: EP/Z001048/1
• 负责人: Wenying Shou
• 金额: $ 26.26万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FZ001048%2F1
• 关键词: Statistical; moments; infer; microbial; phenotypes

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.

定量预测群落动态对于工程化具有期望特性的微生物群落（例如降解废物或产生益生菌）是重要的。对于具有良好相互作用的群落，尽管可以构建机械模型，但其对应于微生物表型的参数，如化学品的释放或消耗速率，是非常难以测量的。例如，如果代谢物被另一个物种迅速消耗，释放率就很难测量;在单一栽培中测量的释放率往往不能反映群落环境中的表型。传统上，确定性模型是拟合数据，但推断的参数往往是不正确的或不唯一的，由于信息不足，以约束他们，这种情况恶化了不可量化的变量，如快速消耗的化学品或酶的中间体。在这里，尽管未量化的变量，我将开发SMIP“统计矩推断参数”，利用实验重复的统计信息来推断机械模型的参数。我的初步工作表明，地面真值可以通过以方程的形式施加额外的约束来推断，这些方程描述了变量的“统计矩”（例如均值，方差，协方差）的动态。在这个提案中，我将1）形式化SMIP，推导统计矩来推断参数，尽管变量未量化; 2）验证SMIP对地面真实的silico社区;和3）测试SMIP在时间序列的两个微生物系统，我将在实验中测量。我的研究不仅将帮助实验学家克服在相关社区环境中量化微生物表型的挑战，还将促进预测性合成生态学。这一行动将帮助我整合我在理论和实验研究的兴趣，发展必要的技能，一个成功的跨学科的独立职业生涯。