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

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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.