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Related Experiment Videos

Response to temporal parameter fluctuations in biochemical networks.

Wolfram Liebermeister1

  • 1Berlin Center for Genome Based Bioinformatics, Max Planck Institute for Molecular Genetics, Ihnestr 73, 14195 Berlin, Germany. lieberme@molgen.mpg.de

Journal of Theoretical Biology
|March 24, 2005
PubMed
Summary

We introduce spectral response coefficients to analyze how metabolic systems respond to fluctuating parameters over time. This method accurately predicts temporal responses, even for significant parameter changes, using Fourier analysis.

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Biochemical Kinetics

Background:

  • Metabolic control theory quantifies responses to static parameter changes.
  • Extending this to dynamic parameter fluctuations is crucial for understanding biological systems.
  • Existing methods struggle with temporal parameter variability.

Purpose of the Study:

  • To define and apply spectral response coefficients for analyzing metabolic system dynamics.
  • To generalize metabolic control theory concepts to temporal parameter fluctuations.
  • To assess the accuracy of these coefficients for predicting metabolic responses.

Main Methods:

  • Definition of spectral response coefficients relating Fourier components of concentrations/fluxes to parameter fluctuations.

Related Experiment Videos

  • Generalization of metabolic control theory concepts, including control coefficients and their theorems.
  • Analysis of first-order response coefficients for harmonic parameter oscillations and second-order coefficients for mode coupling.
  • Application of Fourier synthesis for computing temporal responses.
  • Validation using a model of glycolysis.

    • Main Results:

    • Spectral response coefficients effectively relate temporal parameter fluctuations to metabolic variable responses.
    • First-order coefficients capture forced oscillations, dependent on frequency, phase, and amplitude.
    • Resonance phenomena near Hopf bifurcations and spectral densities of fluctuations were studied.
    • Second-order coefficients describe frequency interactions and harmonic generation.
    • Fourier synthesis approximation proved accurate even for large relative parameter fluctuations in the glycolysis model.

    Conclusions:

    • Spectral response coefficients provide a powerful framework for analyzing metabolic system dynamics under temporal parameter variations.
    • The developed methods extend metabolic control theory to dynamic conditions.
    • The approach offers accurate predictions of metabolic responses, applicable to complex biological systems like glycolysis.