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Related Concept Videos

Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

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Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
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Enzyme Kinetics01:19

Enzyme Kinetics

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Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
Scientists typically study enzyme kinetics with a fixed amount of enzyme in the controlled environment of a test tube. When more reactant, or substrate, is...
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Nonlinear Pharmacokinetics: Michaelis-Menten Equation01:18

Nonlinear Pharmacokinetics: Michaelis-Menten Equation

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The Michaelis–Menten equation is a fundamental model for describing capacity-limited kinetics in drug metabolism. It offers insights into the rate of decline of plasma drug concentration Cp over time, with Vmax and KM as pivotal parameters.
Vmax represents the maximum achievable process rate, while KM, known as the Michaelis constant, signifies the drug concentration at which the process rate reaches half its maximum. This relationship between Vmax, KM, and Cp gives rise to three distinct...
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Determination of Michaelis Constant and Maximum Elimination Rate01:20

Determination of Michaelis Constant and Maximum Elimination Rate

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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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Induced-fit Model01:13

Induced-fit Model

85.0K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
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Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
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Updated: Oct 10, 2025

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
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Beyond the Michaelis-Menten: Bayesian Inference for Enzyme Kinetic Analysis.

Hyukpyo Hong1,2, Boseung Choi2,3, Jae Kyoung Kim4,5

  • 1Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

Methods in Molecular Biology (Clifton, N.J.)
|December 10, 2021
PubMed
Summary

This study introduces a Bayesian approach to enzyme kinetics, overcoming Michaelis-Menten limitations for accurate parameter estimation. An R package and optimal experimental design enhance precision with minimal data.

Keywords:
Bayesian inferenceEnzyme kineticsMichaelis–Menten rate lawProgress curve assayTotal quasi-steady state approximation

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Area of Science:

  • Biochemistry
  • Enzyme Kinetics
  • Computational Biology

Background:

  • Michaelis-Menten (MM) kinetics is widely used but limited to low enzyme concentrations and suffers from parameter identifiability issues.
  • Accurate enzyme kinetic parameter estimation is crucial for understanding biological processes.

Purpose of the Study:

  • To develop a robust Bayesian approach for enzyme kinetics that overcomes the limitations of the traditional Michaelis-Menten model.
  • To provide a user-friendly R package for implementing Bayesian inference in progress curve assays.
  • To define an optimal experimental design for precise enzyme kinetic parameter estimation.

Main Methods:

  • Developed a modified Michaelis-Menten rate law using the total quasi-steady state approximation.
  • Implemented a Bayesian inference framework for progress curve analysis.
  • Utilized a computational R package for practical application and experimental design.

Main Results:

  • The Bayesian approach accurately estimates enzyme kinetic parameters, even under conditions where MM kinetics fails.
  • The proposed optimal experimental design enables precise parameter estimation from minimal progress curve measurements.
  • The R package facilitates the application of these advanced methods.

Conclusions:

  • The Bayesian method offers a significant improvement over traditional Michaelis-Menten kinetics for enzyme parameter estimation.
  • Accurate and precise enzyme kinetic analysis is achievable with the developed computational tools and experimental design strategies.