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: 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...
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...
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...
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,...
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:

You might also read

Related Articles

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

Sort by
Same author

AE-PocketMiner Uses Attention to Simultaneously Predict Cryptic Pockets and Their Allosteric Coupling.

bioRxiv : the preprint server for biology·2026
Same author

Deep mining of the human antibody repertoire identifies frequent and genetically diverse CDRH3 topologies targetable by vaccination.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

How Well Can AI and Physics-Based Simulations Predict the Probability a Cryptic Pocket Is Open?

Journal of chemical theory and computation·2026
Same author

Introduction to Markov State Modeling of Conformational Dynamics.

Journal of chemical theory and computation·2026
Same author

How Well Can AI and Physics-Based Simulations Predict the Probability a Cryptic Pocket Is Open?

bioRxiv : the preprint server for biology·2026
Same author

Decrypting cryptic pockets with physics-based simulations and artificial intelligence.

Current opinion in structural biology·2026

Related Experiment Video

Updated: May 7, 2026

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

A tutorial on building markov state models with MSMBuilder and coarse-graining them with BACE.

Gregory R Bowman1

  • 1Departments of Molecular & Cell Biology and Chemistry, University of California, Berkeley, Berkeley, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|September 25, 2013
PubMed
Summary

Markov state models (MSMs) provide a map of molecular conformational space, connecting simulations to experiments. This tutorial explains how to build MSMs and perform analyses using MSMBuilder software.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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

Related Experiment Videos

Last Updated: May 7, 2026

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

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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

Area of Science:

  • Computational chemistry
  • Biophysics
  • Molecular dynamics

Background:

  • Markov state models (MSMs) are essential for interpreting complex molecular simulation data.
  • Understanding molecular conformational landscapes is crucial for drug discovery and materials science.
  • Connecting simulation results with experimental observations remains a challenge.

Purpose of the Study:

  • To provide a tutorial on constructing Markov state models (MSMs).
  • To demonstrate basic analytical techniques applicable to MSMs.
  • To introduce the capabilities of the MSMBuilder software package.

Main Methods:

  • Building Markov state models from molecular simulation trajectories.
  • Defining conformational states and transition probabilities.
  • Utilizing the MSMBuilder software for model construction and analysis.

Main Results:

  • MSMs effectively represent molecular conformational space as a network of states and transitions.
  • Quantitative links between simulations and experimental data can be established.
  • Efficient simulation strategies can be designed based on MSM insights.

Conclusions:

  • MSMs offer a powerful framework for analyzing molecular dynamics simulations.
  • MSMBuilder facilitates the creation and analysis of these models.
  • This approach enhances the predictive power of molecular simulations.