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Related Concept Videos

Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
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Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Determination of Michaelis Constant and Maximum Elimination Rate01:20

Determination of Michaelis Constant and Maximum Elimination Rate

140
The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Operon Model01:23

Operon Model

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The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
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Updated: Jul 23, 2025

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
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Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover

Xingcun Fan1, Lingfeng Cao1, Xuefeng Yan1,2

  • 1Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.

Biotechnology and Bioengineering
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

Optimizing enzyme constraints in genome-scale metabolic network models (GSMMs) improves metabolic predictions. An adaptive differential evolution algorithm effectively refines key enzyme parameters for enhanced accuracy.

Keywords:
Saccharomyces cerevisiaeadaptive mutation strategyenzyme-constrained genome-scale metabolic network modelsensitivity analysis

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Area of Science:

  • Metabolic Engineering
  • Computational Biology
  • Systems Biology

Background:

  • Genome-scale metabolic network models (GSMMs) are crucial for understanding cellular metabolism.
  • Incorporating enzyme constraints, specifically enzyme turnover numbers, significantly enhances GSMM predictive power.
  • Accurate parameterization of enzyme constraints is vital for reliable metabolic predictions.

Purpose of the Study:

  • To develop and evaluate a novel parameter optimization method for enzyme-constrained GSMMs.
  • To identify key enzyme parameters influencing specific growth rate predictions.
  • To improve the accuracy and predictive capabilities of GSMMs through systematic optimization.

Main Methods:

  • Sensitivity analysis to identify influential enzyme parameters.
  • Differential evolution (DE) algorithm with an adaptive mutation strategy for parameter optimization.
  • Application and evaluation of the method on the Saccharomyces cerevisiae metabolic model (ecYeast8.3.4).

Main Results:

  • Sensitivity analysis classified parameters into most sensitive, highly sensitive, and nonsensitive groups.
  • The adaptive DE algorithm successfully optimized highly sensitive parameters, improving model predictions.
  • Recommended strategies include retaining all parameters or optimizing only the highly sensitive ones.

Conclusions:

  • Enzyme-constrained GSMM parameter optimization using adaptive DE is effective.
  • Focusing optimization on highly sensitive parameters offers an efficient strategy for improving metabolic models.
  • The developed method enhances the predictive accuracy of GSMMs for applications in metabolic engineering and synthetic biology.