A scalable method for parameter identification in kinetic models of metabolism using steady-state data

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Kinetic models of metabolism rely on parameter values to predict dynamic behavior.
• Estimating these parameters requires structural identifiability and informative in vivo experimental data.
• Current methods for assessing structural identifiability are computationally complex and limited to small metabolic networks.

Purpose of the Study:

• To develop a scalable methodology for structurally identifying parameters in kinetic metabolic models using steady-state data.
• To address the challenge of designing experiments that generate informative steady-state data for parameter estimation.
• To enable more accurate and efficient analysis of metabolic networks.

Main Methods:

• A novel, scalable methodology for structural identifiability analysis based on steady-state data availability.
• Integration of experimental design principles to determine optimal experiments for parameter estimation.
• Application to a small metabolic network to demonstrate feasibility and effectiveness.

Main Results:

• The proposed methodology successfully identifies parameters for fluxes in kinetic metabolic models using steady-state data.
• Demonstrated that steady-state data can be effectively generated through selective substrate and enzyme level perturbations.
• Showcased that most parameters in fluxes described by mechanistic enzyme kinetics are identifiable.

Conclusions:

• The developed methodology offers a computationally scalable solution for parameter identifiability in metabolic models.
• It provides a framework for designing experiments that yield informative steady-state data, crucial for parameter estimation.
• This approach can be combined with dynamic data methods for comprehensive experimental design, optimizing resource allocation.