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A transfer-function representation for the input-output relation in consecutive Michaelis-Menten-type reactions

N Sakamoto1

  • 1Institute of Information Sciences and Electronics, University of Tsukauba, Ibaraki, Japan.

Bio Systems
|January 1, 1994
PubMed
Summary
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A new transfer-function model analytically approximates reaction velocity for single and consecutive Michaelis-Menten reactions. This model accurately predicts system responses around steady states, aiding biochemical systems analysis.

Area of Science:

  • Biochemical Engineering
  • Chemical Kinetics
  • Systems Biology

Background:

  • Michaelis-Menten kinetics describe enzyme-catalyzed reactions.
  • Modeling open systems requires understanding substrate influx and reaction velocity.
  • Analytical methods are needed for efficient system analysis.

Purpose of the Study:

  • To develop an analytical transfer-function representation for Michaelis-Menten reactions.
  • To describe the reaction velocity in response to substrate influx in open systems.
  • To validate the model for single and consecutive reaction systems.

Main Methods:

  • Devised a transfer-function representation for reaction kinetics.
  • Analyzed single and two consecutive Michaelis-Menten-type reactions.

Related Experiment Videos

  • Compared transfer-function output with computer simulations (numerical integration).
  • Examined validity by varying kinetic parameters and flow rate.

    • Main Results:

    • Developed a first-order system transfer function for single reactions, with a time constant dependent on kinetic parameters and flow rate.
    • Showed that the transfer function for two consecutive reactions is the product of individual transfer functions.
    • Validated the model's accuracy for responses around steady states.

    Conclusions:

    • The transfer-function representation provides a valid analytical approximation for Michaelis-Menten reaction systems.
    • The model is effective for predicting system behavior in response to substrate changes.
    • This approach simplifies the analysis of complex reaction dynamics in open systems.