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United States Department of Agriculture

Agricultural Research Service

Title: Energy-Based Dynamic Model for Variable Temperature Batch Fermentation by Lactococcus Lactis

Authors
item Dougherty, Daniel
item Breidt, Frederick
item McFeeters, Roger
item Lubkin, S - NC STATE UNIVERSITY

Submitted to: Applied and Environmental Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 14, 2002
Publication Date: May 1, 2002
Citation: Dougherty, D.P., Breidt, F., McFeeters, R.F., Lubkin, S.R. 2002. Energy-based dynamic model for variable temperature batch fermentation by Lactococcus lactis. Applied and Environmental Microbiology. 68:2468-2478.

Interpretive Summary: This research article describes a mathematical model for bacterial growth in foods. The model is unique in that the growth (and death) of bacterial cells was based on the internal energy levels of the cells. The model consisted of a system of differential equations that were solved using a computer simulation program. Predictions included changes in bacterial cell counts, energy levels, and end product (acid) inhibition of growth. The predictions of the model were validated by experimental studies. We found that the model could accurately predict the delay (lag time) in bacterial growth when the culture was shifted to a colder temperature. The model was developed as a research tool to understand the behavior of bacterial cells during temperature changes. The model may also have application in the development of risk assessment models for minimally processed, refrigerated foods.

Technical Abstract: We developed a mechanistic mathematical model for predicting the progression of batch fermentation of cucumber juice by Lactococcus lactis under variable environmental conditions. In order to overcome the deficiencies of currently available models, we use a dynamic energy budget (DEB) approach to model the dependence of growth on current as well as past environmental conditions. Using parameter estimates from independent experimental data, our model is able to predict the outcomes of three different temperature shift scenarios, including lag effects, during batch growth. Sensitivity analyses elucidate how temperature affects the metabolism and growth of cells through lag, log, stationary, and death phases, and reveal that there is a qualitative reversal in the factors limiting growth between low and high temperatures. Our model has an applied use as a predictive tool in batch culture growth. It has the added advantage of being able to suggest plausible and testable mechanistic assumptions about the interplay between cellular energetics and the modes of inhibition by temperature and end-product accumulation.

Last Modified: 4/18/2014
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