Inferring Microbial Biomass Yield and Cell Weight using Probabilistic Macrochemical Modeling
This work addresses a specific challenge in microbiology for researchers studying microbial responses to environmental changes, but it is incremental as it builds on existing modeling techniques.
The paper tackles the problem of estimating microbial biomass yield and cell weight, which are affected by measurement noise and uncertain assumptions, by proposing a probabilistic macrochemical modeling approach. The result is a method that improves robustness to prior cell weight estimates and provides uncertainty estimates, as validated with synthetic data.
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.