Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Nonlinear Pharmacokinetics: Michaelis-Menten Equation01:18

Nonlinear Pharmacokinetics: Michaelis-Menten Equation

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...
Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Enzyme Kinetics01:19

Enzyme Kinetics

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...
Pharmacodynamic Models: Emax Drug–Concentration Effect Model01:18

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
The Kinetic Model of Gases01:24

The Kinetic Model of Gases

The kinetic model of gases explains the properties of a perfect gas using three main assumptions: molecules move in ceaseless random motion, their size is negligible compared to the distances between them, and they do not interact except during perfectly elastic collisions. The total energy of a gas is the sum of the kinetic energies of all its constituent molecules. The pressure exerted by the gas arises from the continual bombardment of the container walls by billions of colliding molecules.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

The journal of physical chemistry. B·2023
Same author

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

ArXiv·2023
Same author

Folding@home: achievements from over twenty years of citizen science herald the exascale era.

ArXiv·2023
Same author

Folding@home: Achievements from over 20 years of citizen science herald the exascale era.

Biophysical journal·2023
Same author

Improvement in ADMET Prediction with Multitask Deep Featurization.

Journal of medicinal chemistry·2020
Same author

Dynamical model of the CLC-2 ion channel reveals conformational changes associated with selectivity-filter gating.

PLoS computational biology·2020

Related Experiment Video

Updated: Jun 21, 2026

Single-Molecule Measurement of Protein Interaction Dynamics Within Biomolecular Condensates
06:48

Single-Molecule Measurement of Protein Interaction Dynamics Within Biomolecular Condensates

Published on: January 5, 2024

Bayesian single-exponential kinetics in single-molecule experiments and simulations.

Daniel L Ensign1, Vijay S Pande

  • 1Department of Chemistry, Stanford University, Stanford, California 94305, USA.

The Journal of Physical Chemistry. B
|August 18, 2009
PubMed
Summary

We present a Bayesian method to calculate single-exponential rates for molecular processes, even without observed transitions. This approach enables robust statistical comparisons between different datasets for molecular dynamics and experimental studies.

More Related Videos

Steady-state, Pre-steady-state, and Single-turnover Kinetic Measurement for DNA Glycosylase Activity
14:27

Steady-state, Pre-steady-state, and Single-turnover Kinetic Measurement for DNA Glycosylase Activity

Published on: August 19, 2013

Parallel High Throughput Single Molecule Kinetic Assay for Site-Specific DNA Cleavage
06:51

Parallel High Throughput Single Molecule Kinetic Assay for Site-Specific DNA Cleavage

Published on: May 6, 2020

Related Experiment Videos

Last Updated: Jun 21, 2026

Single-Molecule Measurement of Protein Interaction Dynamics Within Biomolecular Condensates
06:48

Single-Molecule Measurement of Protein Interaction Dynamics Within Biomolecular Condensates

Published on: January 5, 2024

Steady-state, Pre-steady-state, and Single-turnover Kinetic Measurement for DNA Glycosylase Activity
14:27

Steady-state, Pre-steady-state, and Single-turnover Kinetic Measurement for DNA Glycosylase Activity

Published on: August 19, 2013

Parallel High Throughput Single Molecule Kinetic Assay for Site-Specific DNA Cleavage
06:51

Parallel High Throughput Single Molecule Kinetic Assay for Site-Specific DNA Cleavage

Published on: May 6, 2020

Area of Science:

  • Computational Biology
  • Statistical Physics
  • Biophysics

Background:

  • Single-molecule processes often exhibit single-exponential kinetics.
  • Accurate rate determination is crucial for understanding molecular dynamics.
  • Existing methods may struggle with limited or no observed transitions.

Purpose of the Study:

  • To develop a fully Bayesian method for calculating probability distributions of single-exponential rates.
  • To enable rate estimation even when no state transitions are observed.
  • To provide a framework for comparing rates between datasets using Bayesian hypothesis testing.

Main Methods:

  • A fully Bayesian approach for calculating probability distributions of single-exponential rates.
  • Estimation of lower bounds on rates when no transitions are observed.
  • Bayesian hypothesis testing for comparing rates across two datasets.

Main Results:

  • The method provides probability distributions for single-exponential rates in molecular processes.
  • It can estimate lower bounds for rates even with zero observed transitions.
  • Bayesian hypothesis testing allows for straightforward comparison of rates between datasets.

Conclusions:

  • The developed Bayesian method offers a robust framework for analyzing single-exponential rates in molecular systems.
  • It is applicable to both simulation and experimental data suitable for two-state models.
  • The approach enhances the statistical power for rate comparisons and analysis, particularly in challenging scenarios with limited data.