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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

314
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
314
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

534
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...
534
Types of Damping01:20

Types of Damping

8.0K
If the amount of damping in a system is gradually increased, the period and frequency start to become affected because damping opposes, and hence slows, the back and forth motion (the net force is smaller in both directions). If there is a very large amount of damping, the system does not even oscillate; instead, it slowly moves toward equilibrium. In brief, an overdamped system moves slowly towards equilibrium, whereas an underdamped system moves quickly to equilibrium but will oscillate about...
8.0K
Damped Oscillations01:07

Damped Oscillations

7.6K
In the real world, oscillations seldom follow true simple harmonic motion. A system that continues its motion indefinitely without losing its amplitude is termed undamped. However, friction of some sort usually dampens the motion, so it fades away or needs more force to continue. For example, a guitar string stops oscillating a few seconds after being plucked. Similarly, one must continually push a swing to keep a child swinging on a playground.
Although friction and other non-conservative...
7.6K
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

84
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
84
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

335
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...
335

You might also read

Related Articles

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

Sort by
Same author

Coarse-Grained Modeling of Drug Absorption into Plasticized PVC.

Journal of chemical theory and computation·2026
Same author

Shear-induced bubble nucleation in magmas.

Science (New York, N.Y.)·2025
Same author

Coarse-Grained Insights into Insulin Aspart Adsorption on Plasticized Poly(vinyl chloride) (PVC) Surfaces.

The journal of physical chemistry. B·2025
Same author

Interactions of insulin aspart hexamer and excipients with plasticized polyvinyl chloride surfaces: A comprehensive investigation combining molecular simulations and experiments.

International journal of biological macromolecules·2025
Same author

Drug Delivery Mechanisms of Poly(glycerol sebacate): An In-Depth Study of the Energetics at the Molecular Scale.

Molecular pharmaceutics·2025
Same author

Molecular and Energetic Descriptions of the Plasma Protein Adsorption onto the PVC Surface: Implications for Biocompatibility in Medical Devices.

ACS omega·2024

Related Experiment Video

Updated: Apr 4, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

9.1K

Bayesian parametrization of coarse-grain dissipative dynamics models.

Alain Dequidt1, Jose G Solano Canchaya1

  • 1Université Clermont Auvergne, Université Blaise Pascal, Institut de Chimie de Clermont-Ferrand, BP 10448, Clermont-Ferrand F-63000, France.

The Journal of Chemical Physics
|September 3, 2015
PubMed
Summary

We developed a new Bayesian optimization method to create accurate dissipative coarse-grain models. This approach optimizes models by matching coarse-grained trajectories, improving molecular dynamics simulations.

More Related Videos

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

3.6K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.5K

Related Experiment Videos

Last Updated: Apr 4, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

9.1K
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

3.6K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.5K

Area of Science:

  • Computational chemistry
  • Materials science
  • Statistical mechanics

Background:

  • Coarse-grained (CG) models are essential for simulating large molecular systems.
  • Optimizing CG models, especially dissipative ones, remains a challenge.
  • Existing methods like force matching have limitations.

Purpose of the Study:

  • Introduce a novel bottom-up method for optimizing dissipative coarse-grain models.
  • Provide a practical framework with an analytical solution for parameter optimization.
  • Validate and demonstrate the method's effectiveness on model systems.

Main Methods:

  • Bayesian optimization of the likelihood to reproduce a reference CG trajectory.
  • Utilizes averaged forces over coarse time steps, not instantaneous forces.
  • Estimates friction parameters alongside force field parameters.

Main Results:

  • Successfully optimized a system with a known optimum.
  • Achieved excellent agreement for local molecular structure and equilibrium density of n-pentane.
  • Obtained satisfactory dynamic properties, though sensitive to CG representation.

Conclusions:

  • The new method offers a robust alternative to existing techniques like iterative Boltzmann inversion and force matching.
  • The quality of the optimized force field depends on the definition of CG degrees of freedom and interactions.
  • This approach enhances the accuracy and applicability of coarse-grained simulations.