Constrained optimization of L-lysine production based on metabolic flux using a mathematical programming method

Area of Science:

• Biotechnology
• Biochemical Engineering
• Metabolic Engineering

Background:

• Microbial fermentation requires complex optimization to maximize product yield.
• Traditional methods like the maximum principle are insufficient for intricate metabolic networks.
• L-lysine production is a key target for bioprocess optimization.

Purpose of the Study:

• To develop a constrained nonlinear programming (NLP) method for optimizing microbial fermentation.
• To formulate L-lysine production as an NLP problem, incorporating metabolic reactions and empirical data.
• To enable efficient and accurate calculation of optimal fermentation conditions.

Main Methods:

• Formulated the L-lysine fermentation optimization as a constrained nonlinear programming problem.
• Discretized time-dependent state equations based on material balances into a time-independent vector.
• Developed a computer program utilizing the sequential quadratic programming (SQP) method to solve the NLP problem.

Main Results:

• The SQP method successfully solved the constrained NLP problem.
• Calculated the maximum L-lysine production and optimal L-threonine feeding rate.
• Demonstrated the ease of incorporating equality and inequality constraints into the model.
• Achieved a maximum L-lysine concentration of 75.3 g/l under optimized conditions.

Conclusions:

• Constrained nonlinear programming, specifically using the SQP method, is effective for microbial fermentation optimization.
• The developed method allows for precise control and maximization of L-lysine production.
• This approach offers a robust framework for optimizing other bioprocesses with complex metabolic pathways.