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Time Dependent Differential Yield as a Scale-up Parameter in Enzyme and Fermentation Reactors.

P S Crooke1, R D Tanner, D H Park

  • 1Department of Mathematics Vanderbilt University Nashville, TN 37235.

Biotechnology Progress
|June 23, 2010
PubMed
Summary
This summary is machine-generated.

This study analyzes enzyme kinetics, revealing how yield functions relate to substrate concentration. The findings offer insights into enzyme and fermentation systems, improving data consistency analysis.

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Area of Science:

  • Biochemical Engineering
  • Enzyme Kinetics
  • Bioprocess Modeling

Background:

  • Enzyme-substrate kinetics are fundamental to understanding biological and industrial processes.
  • Yield functions are critical for quantifying the efficiency of enzyme and fermentation systems.
  • Existing models often simplify the complex relationship between enzyme concentration and substrate availability.

Purpose of the Study:

  • To investigate the differential yield function in enzyme-substrate kinetic models.
  • To analyze the asymptotic behavior of the yield function as substrate concentration approaches zero.
  • To explore the relationship between the differential yield function and commonly used yield constants.

Main Methods:

  • Mathematical analysis of the yield function under pseudo-steady-state conditions.
  • Application of Michaelis-Menten and Briggs-Haldane kinetics to bound the differential yield function.
  • Investigation of the dimensionless parameter epsilon (k(m)/E*)'s role in asymptotic yield.
  • Demonstration of mathematical results using experimental data from horseradish peroxidase and gluconic acid fermentation.

Main Results:

  • The differential yield function is bounded by yield functions derived from Michaelis-Menten and Briggs-Haldane models.
  • The common yield constant is an integral average of the differential yield function.
  • The average yield constant provides a method for assessing data consistency.
  • The parameter epsilon influences the asymptotic yield.

Conclusions:

  • The study provides a rigorous mathematical framework for understanding enzyme yield functions.
  • The findings enhance the interpretation and consistency analysis of data from enzyme and fermentation systems.
  • The research offers improved methods for modeling and optimizing bioprocesses.