Inferring Microbial Biomass Yield and Cell Weight using Probabilistic Macrochemical Modeling

TL;DR

This paper introduces a probabilistic macrochemical modeling approach to accurately estimate microbial biomass yield and cell weight, accounting for measurement noise and uncertainties, thereby improving robustness and providing uncertainty quantification.

Contribution

It presents a novel probabilistic modeling methodology that relaxes assumptions and enhances robustness in estimating microbial growth parameters from noisy experimental data.

Findings

Model improves robustness to cell weight assumptions

Provides uncertainty estimates for key parameters

Validated with synthetic microbial growth data

Abstract

Growth rates and biomass yields are key descriptors used in microbiology studies to understand how microbial species respond to changes in the environment. Of these, biomass yield estimates are typically obtained using cell counts and measurements of the feed substrate. These quantities are perturbed with measurement noise however. Perhaps most crucially, estimating biomass from cell counts, as needed to assess yields, relies on an assumed cell weight. Noise and discrepancies on these assumptions can lead to significant changes in conclusions regarding the microbes' response. This article proposes a methodology to address these challenges using probabilistic macrochemical models of microbial growth. It is shown that a model can be developed to fully use the experimental data, relax assumptions and greatly improve robustness to a priori estimates of the cell weight, and provides uncertainty estimates of key parameters. This methodology is demonstrated in the context of a specific case study and the estimation characteristics are validated in several scenarios using synthetically generated microbial growth data.