Bayesian inference on fermentation kinetics: Comparative analysis with frequentist approach.

Area of Science:

• Biotechnology
• Chemical Engineering
• Computational Biology

Background:

• Fermentation kinetic modeling is crucial for optimizing bioprocesses.
• Traditional methods like nonlinear least squares fitting have limitations in parameter interpretability and data efficiency.
• Bayesian inference presents a promising alternative for robust kinetic modeling.

Purpose of the Study:

• To compare the performance of Bayesian hierarchical models against frequentist nonlinear least squares fitting for fermentation kinetic modeling.
• To evaluate the application of these methods in glycerol-glucose co-fermentation for 1,3-propanediol production and dark fermentation for biohydrogen production.
• To assess the advantages of Bayesian methods in parameter interpretability, robustness, and uncertainty quantification.

Main Methods:

• Utilized modified hyperbolic secant functions to model metabolite concentrations.
• Implemented Bayesian hierarchical models for kinetic analysis.
• Compared Bayesian approach with frequentist nonlinear least squares fitting.

Main Results:

• Frequentist methods showed high computational efficiency and suitability for short-term predictions.
• Bayesian methods demonstrated superior parameter interpretability, robustness with limited data, and enhanced uncertainty quantification.
• Bayesian hierarchical models provided more stable and data-efficient fermentation kinetic models.

Conclusions:

• Bayesian inference offers significant advantages for developing interpretable, stable, and data-efficient fermentation kinetic models.
• The Bayesian approach is particularly beneficial when dealing with limited experimental data.
• This study highlights the potential of Bayesian methods to advance fermentation process optimization and understanding.