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Dorothee Childs1, Sergio Grimbs2, Joachim Selbig2

  • 1Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.

Bioinformatics (Oxford, England)
|June 15, 2015
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Summary

Structural kinetic modelling (SKM) assesses metabolic stability by analyzing network structures and elasticities. This study introduces a kinetic feasibility criterion to improve model reliability, identifying key enzymes like alpha-ketoglutarate dehydrogenase in TCA cycle stability.

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Biochemical Kinetics

Background:

  • Structural kinetic modelling (SKM) analyzes metabolic steady-state stability without detailed rate equations.
  • SKM relies on network structure, steady-state measurements, and elasticity values.
  • Statistical analysis of sampled elasticities can identify key network positions influencing perturbation response.

Purpose of the Study:

  • To examine the kinetic feasibility of elasticity combinations generated during Monte Carlo sampling in SKM.
  • To develop a criterion to mitigate infeasible kinetic models.
  • To apply the enhanced SKM approach to understand the stability of the neuronal TCA cycle.

Main Methods:

  • Monte Carlo sampling of elasticity values within the SKM framework.
  • Formulation of a kinetic feasibility criterion for sampled elasticity sets.
  • Statistical analysis of Jacobian matrices derived from feasible SKMs.
  • Application to two steady states of the neuronal TCA cycle.

Main Results:

  • A majority of randomly sampled SKMs yield kinetically infeasible models with negative parameters.
  • A simple criterion was formulated to filter out infeasible models, improving reliability.
  • Analysis of the neuronal TCA cycle identified coordinated elasticity control of stability.
  • Mutations in alpha-ketoglutarate dehydrogenase were identified as a primary source of instability.

Conclusions:

  • Ensuring kinetic feasibility is crucial for reliable structural kinetic modelling.
  • The developed criterion enhances the validity of SKM by excluding infeasible models.
  • The neuronal TCA cycle's stability is dynamically regulated by specific elasticities, with alpha-ketoglutarate dehydrogenase being a critical factor.