Optimizing bioprocessing efficiency with OptFed: Dynamic nonlinear modeling improves product-to-biomass yield.

Area of Science:

• Biotechnology
• Biochemical Engineering
• Process Optimization

Background:

• Fed-batch processes are crucial for recombinant molecule production but often operate sub-optimally due to unclear kinetics.
• Current process design relies on simplified models and operator experience, limiting production potential.

Purpose of the Study:

• To develop a general modeling framework, OptFed, for predicting optimal fed-batch conditions using experimental data.
• To enhance the efficiency and yield of biotechnological production processes.

Main Methods:

• Developed OptFed, a framework utilizing ordinary differential equations and regression models to fit kinetic constants from experimental data.
• Employed optimal control problem-solving techniques, including orthogonal collocation and nonlinear programming, to predict optimal process parameters.
• Applied the framework to a recombinant protein L fed-batch production case study.

Main Results:

• OptFed successfully predicted optimal feed rate and reactor temperature for recombinant protein L production.
• The framework outperformed Response Surface Methodology (RSM) in both simulations and experiments.
• Achieved a 19% improvement in the experimental product-to-biomass ratio, identifying previously missed optimal conditions.

Conclusions:

• OptFed provides a robust and effective approach for optimizing fed-batch processes in biotechnology.
• The framework demonstrates significant potential for enhancing bioprocess efficiency and maximizing product yield.
• This methodology offers a powerful tool for overcoming limitations of traditional process design and optimization strategies.