Deterministic and Semi-Mechanistic Approaches in Predictive Fermentation Microbiology

Abstract

Predictive fermentation microbiology utilizes deterministic and stochastic mathematical models to study the growth dynamics of microorganisms. If the components of such models represent known or hypothesized biological growth processes then these models can be used to refine existing hypotheses or generate new hypotheses about the factors controlling growth.

Special techniques must be used when fitting such models to experimental data. Methods are suggested for model re-parameterization and model fitting which improve the estimation of model parameters. Once estimates of model parameters have been made, temporal and multivariate sensitivity analyses can assess important relationships among the model parameters.

A deterministic dynamic model of batch growth by a homofermentative lactic acid bacterium growing in a variable temperature environment was derived. This model predicts cell growth as well as changes in the chemical composition of the medium. This model was fit to experimental data. Analysis of the model revealed a quantitative reversal in parameter sensitivities across temperatures.

Although mechanistic, this model neglected the effects of pH, organic acid dissociation and ionic strength of the medium. It is shown that these chemical dynamics are important and can be modeled through a convenient semi-mechanistic approach. The ability to model these chemical dynamics appropriately allows for a modeling framework in which the acid tolerance strategies commonly exhibited by bacteria can be studied.