Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation

Area of Science:

• Biochemistry
• Computational Biology
• Systems Biology

Background:

• Biochemical kinetic system simulations are crucial for model validation, 'what if' analyses, and exploring model behaviors.
• These simulations are vital for metabolic engineering and solving the inverse problem of parameter estimation from experimental data.

Purpose of the Study:

• To develop a generic approach combining numerical optimization with biochemical kinetic simulations.
• To facilitate rational design of improved metabolic pathways and parameter estimation in metabolic pathways.

Main Methods:

• Integration of diverse numerical optimization methods with biochemical kinetic simulations.
• Implementation of a simulation-optimization strategy within the Gepasi biochemical kinetics simulator.

Main Results:

• Evaluation of various optimization methods, emphasizing their ability to find global optima.
• Recommendation for incorporating a suite of diverse optimization methods in simulation software for broader applicability.
• Demonstration of the simulation-optimization strategy's application through examples.

Conclusions:

• A robust simulation-optimization strategy enhances the capabilities of biochemical kinetic simulators.
• The Gepasi software (version 3.20) now incorporates this advanced methodology.
• This approach supports advancements in metabolic engineering and systems biology research.