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A second-order approach to metabolic control analysis

T Höfer1, R Heinrich

  • 1Humboldt-Universität zu Berlin, FB Biologie, Institut für Biophysik, Germany.

Journal of Theoretical Biology
|September 7, 1993
PubMed
Summary
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This study extends metabolic control analysis by incorporating second-order terms for larger parameter perturbations. This enhanced approach improves accuracy but faces experimental limitations for higher-order coefficients.

Area of Science:

  • Systems Biology
  • Biochemical Engineering
  • Metabolic Engineering

Background:

  • Metabolic Control Analysis (MCA) traditionally uses linear approximations for metabolite and flux changes.
  • Linear approximations limit analysis to small parameter perturbations, restricting accuracy for larger biological system changes.

Purpose of the Study:

  • To develop and derive the mathematical formalism for second-order approximations in Metabolic Control Analysis.
  • To enhance the accuracy of predicting system responses to larger parameter perturbations in metabolic networks.

Main Methods:

  • Derived basic equations incorporating second-order terms into the existing MCA framework.
  • Defined parameter-independent second-order control coefficients for linearly dependent reaction rates.

Related Experiment Videos

  • Applied the second-order formalism to an unbranched reaction chain and a glycolysis model.

    • Main Results:

    • Second-order terms improve accuracy for a wider range of rate perturbations compared to linear MCA.
    • Second-order effects depend on higher-order elasticity coefficients and linear theory's control coefficients/pi-elasticities.
    • Summation theorems for second-order coefficients are analogous to those in linear MCA.

    Conclusions:

    • Second-order MCA provides a more accurate description of metabolic system responses to larger perturbations.
    • Experimental determination of higher-order elasticity coefficients presents significant challenges, limiting practical application.
    • Combining second-order MCA with kinetic modeling may overcome limitations of local analysis and experimental data acquisition.