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

Randomized Experiments01:13

Randomized Experiments

9.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.3K
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

1.4K
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...
1.4K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.1K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.7K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.7K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.6K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.6K
Bootstrapping01:24

Bootstrapping

931
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
931

You might also read

Related Articles

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

Sort by
Same author

Extrapolating molecular dynamics simulations to zero time step and across thermodynamic space.

The Journal of chemical physics·2026
Same author

Arc mediates intercellular tau transmission via extracellular vesicles.

Cell·2026
Same author

pH-dependent activation of the Na<sup>+</sup>/H<sup>+</sup> antiporter NhaA and conformational dynamics of its N-terminus.

Nature communications·2026
Same author

Structural flexibility of the human vault particle revealed by high-resolution cryo-EM and molecular dynamics simulations.

Nature communications·2026
Same author

Discovery and Development of a Potent LIMK2 Isoform-Specific Degrader.

ACS chemical biology·2026
Same author

The vault associates with membranes in situ.

Nature communications·2026
Same journal

Revisiting crossed-correlated baths in open quantum systems simulated by HEOM or T-TEDOPA.

The Journal of chemical physics·2026
Same journal

Vesicle size and membrane composition control monomer transfer pathways in multicomponent lipid vesicles.

The Journal of chemical physics·2026
Same journal

Polaron-mediated exciton dynamics of P(NDI2OD-T2) unveiled by transient absorption spectroscopy under electrochemical conditions.

The Journal of chemical physics·2026
Same journal

Green-Kubo relation in a mesoscale odd fluid model.

The Journal of chemical physics·2026
Same journal

Nitrogenation of microscopic MoS2 surfaces by oxidation scanning probe lithography.

The Journal of chemical physics·2026
Same journal

Molecular structure, binding, and disorder in TDBC-Ag plexcitonic assemblies.

The Journal of chemical physics·2026
See all related articles

Related Experiment Video

Updated: Mar 28, 2026

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency
06:41

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency

Published on: May 10, 2024

2.8K

Bayesian ensemble refinement by replica simulations and reweighting.

Gerhard Hummer1, Jürgen Köfinger1

  • 1Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany.

The Journal of Chemical Physics
|January 3, 2016
PubMed
Summary
This summary is machine-generated.

Bayesian ensemble refinement methods characterize dynamic biomolecular structures using experimental data. This study unifies these methods, offering practical applications for analyzing complex molecular ensembles.

More Related Videos

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)
09:38

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)

Published on: September 5, 2025

1.1K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Related Experiment Videos

Last Updated: Mar 28, 2026

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency
06:41

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency

Published on: May 10, 2024

2.8K
Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)
09:38

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)

Published on: September 5, 2025

1.1K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Area of Science:

  • Biophysics
  • Computational Biology
  • Structural Biology

Background:

  • Dynamic and partially disordered biomolecular structures present challenges in characterization.
  • Integrating diverse experimental data, including ensemble-averaged observables, is crucial for understanding these structures.

Purpose of the Study:

  • To describe and interrelate various Bayesian ensemble refinement methods.
  • To discuss the practical applications of these methods in analyzing biomolecular structures.
  • To derive an optimal Bayesian ensemble distribution.

Main Methods:

  • Bayesian formulation to rank configuration space distributions.
  • Maximizing the posterior to derive the optimal Bayesian ensemble distribution.
  • Bayesian replica ensemble refinement and Bayesian inference of ensembles.

Main Results:

  • The optimal Bayesian ensemble distribution is derived.
  • Bayesian replica ensemble refinement enhances configuration sampling via replica molecular dynamics simulations.
  • Convergence to the optimal Bayesian result requires restraints to scale linearly with the number of replicas.

Conclusions:

  • Different Bayesian ensemble refinement methods are unified and their interrelations clarified.
  • The study provides a theoretical basis for practical applications in characterizing dynamic biomolecular structures.
  • Future investigations can build upon this framework, incorporating single-molecule and dynamic observables.