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

Mengqi Hu1, Syed Bilal Jilani2, Daniel G Olson2

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Kinetic models can now accurately predict metabolic dynamics using time-course data from single-enzyme assays. This approach, implemented in the KETCHUP software, improves simulations of multi-enzyme systems, enhancing metabolic engineering and synthetic biology applications.

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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.