Evaluation of an S-system root-finding method for estimating parameters in a metabolic reaction model

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Parameter estimation in mathematical models of cellular metabolism from time-series data is a significant challenge.
• Existing approaches for metabolic modeling lack established, robust methods for parameter estimation.

Purpose of the Study:

• To evaluate the S-system root-finding method for parameter estimation in metabolic models.
• To compare the S-system method's performance against the Newton-Raphson method.
• To explore enhancements for practical application of the S-system method.

Main Methods:

• Utilized Biochemical Systems Theory (BST) to construct power-law models.
• Applied an S-system root-finding method for parameter estimation.
• Investigated translocation techniques and complex-step differentiation for practical application.

Main Results:

• The S-system method demonstrated superior convergence region and fewer iterations compared to the Newton-Raphson method.
• The translocation technique and complex-step differentiation were found to be useful for practical implementation.
• The S-system method proved effective for modeling diverse metabolic reaction networks.

Conclusions:

• The S-system method is a powerful and efficient tool for parameter estimation in metabolic modeling.
• This approach enhances the construction of accurate mathematical models for cellular metabolism.
• The study highlights the S-system method's potential for advancing systems biology research.