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KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states.

Mengqi Hu1, Patrick F Suthers1, Costas D Maranas1

  • 1Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.

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|February 9, 2024
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Summary
This summary is machine-generated.

This study introduces KETCHUP, a new computational tool for parameterizing large-scale metabolic models. KETCHUP efficiently estimates kinetic parameters, overcoming challenges in data acquisition and computational complexity for robust model development.

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Large-scale kinetic models link metabolic fluxes, metabolite concentrations, and enzyme levels.
  • Developing broadly applicable, efficient, and robust frameworks for model parameterization remains a significant challenge.
  • Data heterogeneity, scarcity, and computational difficulties hinder the adoption of kinetic models.

Purpose of the Study:

  • To introduce KETCHUP (Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo), a flexible parameter estimation tool.
  • To address the challenges in parameterizing large-scale kinetic metabolic models.
  • To enable robust kinetic model construction and parameterization using diverse datasets and conditions.

Main Methods:

  • Utilizes a primal-dual interior-point algorithm to solve a nonlinear programming problem.
  • Identifies parameters to recapitulate (non)steady-state fluxes and concentrations in metabolic networks.
  • Accepts various kinetic descriptions, metabolic fluxes, enzyme levels, and metabolite concentrations under steady-state or instationary conditions.

Main Results:

  • KETCHUP demonstrates at least an order of magnitude faster convergence compared to the K-FIT tool.
  • Achieves better data fits than existing methods.
  • Successfully parameterized wild-type and perturbed metabolic networks.

Conclusions:

  • KETCHUP provides a versatile and efficient solution for kinetic model parameterization.
  • Overcomes limitations of previous methods in terms of speed and accuracy.
  • Facilitates wider adoption of large-scale kinetic models in systems biology and metabolic engineering.