Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis

Area of Science:

• Food microbiology
• Quantitative microbial ecology
• Computational biology

Background:

• Predictive microbiology forecasts microbial population dynamics using mathematical models.
• Traditional models include continuous, empirical, theoretical, and tiered approaches.
• Recent advances favor spatially explicit Individual-based Models (IbMs) due to technological and computational progress.

Purpose of the Study:

• To conduct a SWOT (Strength, Weaknesses, Opportunities, Threats) analysis comparing population continuous modeling and Individual-based Modeling (IbM) in predictive microbiology.
• To provide insights for researchers on the advantages and disadvantages of different modeling approaches.
• To guide the selection of appropriate models for specific research questions in microbial population dynamics.

Main Methods:

• Comparative analysis of existing modeling approaches in predictive microbiology.
• SWOT analysis framework applied to population continuous models and Individual-based Models (IbMs).
• Literature review and synthesis of current research on microbial modeling techniques.

Main Results:

• Population continuous models offer macroscopic system overviews but may lack mechanistic detail.
• Individual-based Models (IbMs) provide cell-level insights and bridge micro- to macro-scale phenomena.
• SWOT analysis highlights distinct strengths, weaknesses, opportunities, and threats for each modeling paradigm.

Conclusions:

• Both population continuous models and IbMs have unique roles and applications in predictive microbiology.
• Understanding the strengths and limitations of each approach is crucial for effective model selection.
• The advancement of IbMs offers new possibilities for understanding microbial communities from the individual to population level.