Robust parameter estimation during logistic modeling of batch and fed-batch culture kinetics

Area of Science:

• Biotechnology
• Bioprocess Engineering
• Mathematical Modeling

Background:

• Mammalian cell culture is crucial for biopharmaceutical production.
• Accurate modeling of cell growth dynamics is essential for process optimization.
• Existing logistic models can be sensitive to parameter variations.

Purpose of the Study:

• To develop a robust method for logistic modeling of batch and fed-batch mammalian cell cultures.
• To improve the accuracy and reliability of parameter estimation in cell culture models.
• To provide a simple, implementable approach for data analysis.

Main Methods:

• Derived linearized forms of logistic growth, decline, and generalized logistic equations.
• Utilized linear least squares for initial parameter estimation.
• Employed nonlinear optimization with three algorithms for refined parameter determination.
• Tested the approach on BHK, CHO, and hybridoma cells across various culture volumes (100 mL-300 L).

Main Results:

• Achieved solution convergence for all nonlinear optimization algorithms across all tested data sets.
• Demonstrated robust estimation of logistic parameters through linearization and nonlinear optimization.
• The method proved effective for diverse mammalian cell types and culture scales.
• Successfully modeled both batch and fed-batch culture data.

Conclusions:

• The combined linearization and nonlinear optimization approach offers robust parameter estimation for mammalian cell culture models.
• This method is simple and can be implemented in spreadsheets for practical application.
• The findings facilitate improved bioprocess monitoring and control for mammalian cell cultures.