Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by

Same author

Toward Automated Cell Model Development through Information Theory<sup>†</sup>.

The journal of physical chemistry. A·2024

Same author

Omniligase-1-Mediated Phage-Peptide Library Modification and Insulin Engineering.

ACS chemical biology·2024

Same author

B-cell epitope discovery: The first protein flexibility-based algorithm-Zika virus conserved epitope demonstration.

PloS one·2023

Same author

Temporally Coarse-Grained All-Atom Molecular Dynamics Achieved via Stochastic Padé Approximants.

The journal of physical chemistry. B·2020

Same author

Amphiphile self-assembly dynamics at the solution-solid interface reveal asymmetry in head/tail desorption.

Chemical communications (Cambridge, England)·2018

Same author

Multiscale time-dependent density functional theory: Demonstration for plasmons.

The Journal of chemical physics·2017

Same journal

Nuclear Gradients from Auxiliary-Field Quantum Monte Carlo and Their Applications in ML-Driven Geometry Optimization and Transition State Search.

Journal of chemical theory and computation·2026

Same journal

Correction to "Cluster-in-Molecule Local Correlation Method with an Accurate Distant Pair Correction for Large Systems".

Journal of chemical theory and computation·2026

Same journal

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy.

Journal of chemical theory and computation·2026

Same journal

Systematic Molecularity-Dependent Entropy Errors in Continuum/RRHO Solution Thermochemistry: Origin and Correction.

Journal of chemical theory and computation·2026

Same journal

After 100 Years of Quantum Mechanics: Toward a Constructive Observation-Centered Perspective.

Journal of chemical theory and computation·2026

Same journal

Sample-Based Quantum Diagonalization Methods for Modeling the Photochemistry of Diazirine and Diazo Compounds.

Journal of chemical theory and computation·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Multiscale Factorization Method for Simulating Mesoscopic Systems with Atomic Precision.

Andrew Abi Mansour¹, Peter J Ortoleva¹

¹Department of Chemistry, Indiana University , Bloomington, Indiana 47405, United States.

Journal of Chemical Theory and Computation

|May 8, 2014

Summary

This summary is machine-generated.

A new multiscale simulation method accurately models large N-atom systems by integrating multiple scales and stationary process hypotheses. This approach enhances understanding of complex molecular dynamics in systems like viruses and proteins.

More Related Videos

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

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

Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry

Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry

Published on: September 6, 2016

Related Experiment Videos

Last Updated: Apr 30, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

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

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

Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry

Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry

Published on: September 6, 2016

Area of Science:

Multiscale modeling
Computational biophysics
Statistical mechanics

Background:

Mesoscopic N-atom systems exhibit complex behavior driven by multi-scale interactions.
Simulating these systems requires methods that bridge atomic and coarse-grained levels.
Understanding friction-dominated dynamics is crucial for biological systems.

Purpose of the Study:

To present a novel multiscale simulation method for N-atom systems.
To accurately model systems in the friction-dominated regime.
To validate the method's accuracy using biological examples.

Main Methods:

Integration of multiscale analysis and Trotter factorization.
Hypothesis of stationary processes for momenta conjugate to coarse-grained variables.
Application to specific N-atom systems including proteins and viruses.

Main Results:

The developed multiscale method provides accurate simulations for N-atom systems.
Demonstrated efficacy on lactoferrin, nudaurelia capensis omega virus, and human papillomavirus.
Successful modeling of friction-dominated dynamics across scales.

Conclusions:

The presented multiscale method is a robust tool for simulating complex N-atom systems.
The approach offers insights into the dynamics of biological macromolecules.
This method advances computational strategies in biophysics and materials science.