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

360
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...
360
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

290
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...
290
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.8K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.8K
Bias01:22

Bias

7.4K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.4K
Newton’s Method01:30

Newton’s Method

76
Newton’s Method is a powerful iterative technique for approximating the roots of real-valued, differentiable functions, particularly when analytical solutions are impractical. This approach is widely used in scientific computing, engineering, and finance, where equations may be too complex for traditional algebraic methods to handle. The method relies on an iterative process that refines an initial estimate using the function’s derivative to approach the true solution progressively.
76
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

352
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
352

You might also read

Related Articles

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

Sort by
Same author

Direct nonequilibrium molecular dynamics simulation of diffusio-osmotic flow in nanopores.

Journal of colloid and interface science·2026
Same author

Predicting Mobility Acceleration in Two-Bead Coarse-Grained Models Using an Augmented RoughMob Framework.

The journal of physical chemistry. B·2026
Same author

Molecular architecture of block-polymer brushes for underwater oil droplet catch-and-release: A constant-pH hybrid Monte Carlo molecular-dynamics study.

The Journal of chemical physics·2026
Same author

Molecular Mechanisms behind Nonmonotonic Surface Tensions of Binary Aqueous <i>n</i>-Diol Mixtures.

The journal of physical chemistry. B·2026
Same author

Plasticization by Water Governs the Hydration-Adhesion Relationship of Cellulose Mucilage.

Biomacromolecules·2026
Same author

Catch and Release of Oil Droplets by Block-Copolymer-Grafted Surfaces: Coarse-Grained Molecular Dynamics Simulations.

ACS macro letters·2025

Related Experiment Video

Updated: Feb 22, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.2K

Adaptive-numerical-bias metadynamics.

Neda Khanjari1, Hossein Eslami1, Florian Müller-Plathe2

  • 1Department of Chemistry, College of Sciences, Persian Gulf University, Boushehr, 75168, Iran.

Journal of Computational Chemistry
|September 27, 2017
PubMed
Summary

This study introduces an adaptive metadynamics method that uses flexible biasing potentials for more accurate free energy calculations. This approach improves convergence and avoids common issues found in conventional metadynamics simulations.

Keywords:
adaptive biasing potentialsfree energy calculationsmetadynamicspotential of mean forcesampling rare events

More Related Videos

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

10.4K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.2K

Related Experiment Videos

Last Updated: Feb 22, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.2K
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

10.4K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.2K

Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Molecular Dynamics

Background:

  • Conventional metadynamics methods can suffer from trapping in local minima due to fixed Gaussian biases.
  • Accurate free energy calculations are crucial for understanding molecular processes and designing new materials.

Purpose of the Study:

  • To develop a novel metadynamics scheme employing adaptive biasing potentials.
  • To improve the accuracy and convergence rate of free energy surface calculations.

Main Methods:

  • Implemented an adaptive biasing potential that adjusts its shape based on sampled collective variable distributions.
  • Applied the new method to calculate free energy profiles for three test systems.
  • Compared performance against conventional and well-tempered metadynamics.

Main Results:

  • The adaptive metadynamics method demonstrated higher accuracy and faster convergence compared to conventional metadynamics.
  • The adaptive potentials effectively mitigate the issue of system trapping in local energy wells.
  • Performance was comparable to the well-tempered metadynamics approach.

Conclusions:

  • The proposed adaptive metadynamics scheme offers a more robust and efficient alternative for free energy calculations.
  • This method enhances the reliability of molecular simulations for complex systems.
  • Further applications in various computational chemistry problems are warranted.