Individual-based modelling: a mechanistic complement underpinning macroscopic models in predictive microbiology

Area of Science:

• Predictive microbiology
• Mathematical modeling of microbial growth
• Food safety science

Background:

• Macroscopic models dominate predictive microbiology, focusing on total cell numbers.
• These macroscopic models have inherent limitations in describing microbial dynamics.
• A need exists for complementary approaches to improve microbial behavior prediction in food.

Purpose of the Study:

• To propose individual-based modeling (IBM) as a complementary methodology in predictive microbiology.
• To introduce a novel bacterial growth model specifically for the stationary phase.
• To implement and explore the results of this new model within an IBM framework.

Main Methods:

• Development of an individual-based modeling framework.
• Implementation of a new mathematical model for bacterial stationary phase growth.
• Exploratory analysis of simulation results from the individual-based model.

Main Results:

• The individual-based modeling approach was successfully implemented.
• A new model for the bacterial stationary phase was developed and integrated.
• Exploratory results demonstrate the potential of the proposed methodology.

Conclusions:

• Individual-based modeling offers a valuable complement to macroscopic models in predictive microbiology.
• The new stationary phase model shows promise for more detailed microbial behavior prediction.
• Further research and validation are warranted for broader application in food safety.