Predictive modeling of microbial single cells: A review.

Area of Science:

• Food microbiology
• Mathematical modeling
• Predictive analytics

Background:

• Food products are often contaminated with low levels of food-borne pathogens.
• Microbial growth during the food chain poses significant risks.
• Accurate prediction of microbial behavior is essential for food safety.

Purpose of the Study:

• To review key aspects of microbial single-cell modeling.
• To present techniques for microbial single-cell data acquisition and growth data collection.
• To summarize sources of microbial single-cell variability.

Main Methods:

• Detailed presentation of microbial single-cell data acquisition techniques.
• Summary of microbial growth data collection methods.
• Analysis of traditional deterministic and modern stochastic modeling approaches.

Main Results:

• Traditional deterministic models struggle with low cell numbers and high heterogeneity.
• Stochastic models offer improved prediction accuracy by accounting for cell-to-cell variability.
• Understanding variability sources is key to enhancing predictive models.

Conclusions:

• Stochastic modeling is a promising approach for accurate microbial single-cell prediction.
• Addressing cell-to-cell variability is critical for robust food safety models.
• Further research into single-cell microbial dynamics can improve food safety predictions.