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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

You might also read

Related Articles

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

Sort by
Same author

Scalable Boltzmann generators for equilibrium sampling of large-scale materials.

Nature communications·2026
Same author

Assessing generative modeling approaches for free energy estimates in condensed matter.

The Journal of chemical physics·2026
Same author

Learning data-efficient coarse-grained molecular dynamics from forces and noise.

Nature communications·2026
Same author

Extending the range of graph neural networks with global encodings.

Nature communications·2026
Same author

Model-Free Learning of Probability Flows: Elucidating the Nonequilibrium Dynamics of Flocking.

Physical review letters·2025
Same author

Secondary structure transitions and dual PIP2 binding define cardiac KCNQ1-KCNE1 channel gating.

Cell research·2025

Related Experiment Video

Updated: Jun 1, 2026

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
12:38

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

Markov state models based on milestoning.

Christof Schütte1, Frank Noé, Jianfeng Lu

  • 1Institute of Mathematics, Freie Universitaet Berlin, D-14195 Berlin, Germany. Christof.Schuette@fu-berlin.de

The Journal of Chemical Physics
|June 7, 2011
PubMed
Summary

Core Set Markov state models (MSMs) offer a new way to analyze molecular dynamics by using core sets as milestones. This approach effectively traces rare event kinetics and extracts rate constants, improving upon standard MSMs.

More Related Videos

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 1, 2026

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
12:38

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Molecular Dynamics

Background:

  • Markov state models (MSMs) are essential for analyzing large molecular dynamics datasets.
  • MSMs approximate complex dynamics as Markov jump processes between predefined states.
  • Analyzing rare event kinetics in molecular systems remains a challenge.

Purpose of the Study:

  • To introduce and analyze "Core Set MSMs," a novel MSM approach.
  • To leverage metastable core sets as milestones for kinetic analysis.
  • To compare the performance of Core Set MSMs against standard MSMs.

Main Methods:

  • Utilized the milestoning framework for analysis.
  • Employed Bayesian estimation methods.
  • Applied Transition Path Theory (TPT).

Main Results:

  • Core Set MSMs successfully extract phenomenological rate constants between metastable sets.
  • Demonstrated the ability to approximate the evolution of key observables.
  • Evaluated performance on a toy example and alanine dipeptide torsion dynamics.

Conclusions:

  • Core Set MSMs provide a robust framework for analyzing rare event kinetics.
  • This method enhances the capabilities of standard MSMs for molecular dynamics analysis.
  • Core Set MSMs are effective for systems with metastable states and rare transitions.