Parameterization of cell-free systems with time-series data using KETCHUP.

Area of Science:

• Biochemical Engineering
• Systems Biology
• Metabolic Engineering

Background:

• Kinetic models link enzyme levels, metabolite concentrations, and allosteric regulation to metabolic fluxes.
• Current models often rely on steady-state data, limiting confidence in dynamic predictions.
• Simulating enzymatic cascades requires dynamic descriptions as steady-states are often inapplicable.

Purpose of the Study:

• To demonstrate that kinetic parameters from single-enzyme assays are accurate for simulating multi-enzyme cell-free systems.
• To introduce extensions to the KETCHUP software for parameterizing kinetic models using time-course data.
• To improve the accuracy of dynamic simulations in metabolic systems.

Main Methods:

• Utilized time-course data from single-enzyme assays of formate dehydrogenase (FDH) and 2,3-butanediol dehydrogenase (BDH).
• Employed two extensions of the KETCHUP software for kinetic model parameterization.
• Implemented an extension to reconcile measurement time-lag errors in datasets for improved parameterization.

Main Results:

• Kinetic parameters fitted to single-enzyme assay dynamics remained accurate for simulating multi-enzyme cell-free systems.
• Accurate simulation of a binary FDH-BDH system was achieved by combining identified kinetic parameters.
• KETCHUP extensions enabled robust parameterization using time-course data across various initial conditions.

Conclusions:

• Kinetic parameters derived from single-enzyme time-course data can reliably predict the dynamics of multi-enzyme systems.
• The enhanced KETCHUP software provides a powerful tool for kinetic model parameterization with time-series data.
• This approach enhances the predictive power of kinetic models for metabolic engineering and synthetic biology.