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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

701
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,...
701
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

311
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...
311
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

397
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...
397

You might also read

Related Articles

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

Sort by
Same author

Reaction-aware molecular representation learning: Toward generalizable artificial intelligence for enzymatic catalysis.

Acta pharmaceutica Sinica. B·2026
Same author

Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.

Protein science : a publication of the Protein Society·2026
Same author

Correlated clustering and projection for dimensionality reduction.

Machine learning: science and technology·2026
Same author

VARIANT: Web Server for Decoding and Analyzing Viral Mutations at Genome and Protein Levels.

ArXiv·2026
Same author

Manifold topological deep learning for biomedical data.

Nature communications·2026
Same author

A review of recent advances in generative artificial intelligence models for biomolecular sciences.

Acta pharmaceutica Sinica. B·2026

Related Experiment Video

Updated: Mar 29, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K

Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM).

Kelin Xia1, Kristopher Opron2, Guo-Wei Wei3

  • 1Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, USA.

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

We introduce generalized Gaussian network models (gGNM) and anisotropic network models (gANM) for protein flexibility analysis. Our new multiscale methods (mGNM, mANM) significantly improve B-factor predictions by capturing intrinsic multiscale protein behaviors.

More Related Videos

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
10:45

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

13.8K

Related Experiment Videos

Last Updated: Mar 29, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K
Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
10:45

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

13.8K

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Dynamics

Background:

  • Gaussian network model (GNM) and anisotropic network model (ANM) are widely used for studying protein flexibility.
  • These models analyze protein dynamics and functions based on network representations.
  • Existing methods have limitations in accurately predicting protein B-factors and capturing complex dynamics.

Purpose of the Study:

  • To develop generalized Gaussian network model (gGNM) and anisotropic network model (gANM) methods.
  • To introduce multiscale versions of these models (mGNM and mANM) to incorporate varying scales.
  • To enhance the accuracy of B-factor predictions and analyze protein domain separations and collective motions.

Main Methods:

  • Developed a unified framework to construct generalized Kirchhoff matrices from correlation functions.
  • Introduced multiscale elastic network models (mGNM and mANM) by incorporating different scales into generalized matrices.
  • Validated methods through extensive numerical experiments and B-factor predictions on 364 proteins.

Main Results:

  • gGNMs demonstrated improved B-factor prediction accuracy compared to the original GNM.
  • Multiscale GNM (mGNM) and multiscale ANM (mANM) achieved over 11% improvement in B-factor predictions.
  • mGNM successfully predicted B-factors for proteins that failed the original GNM, and analyzed domain separations.
  • mANM effectively analyzed protein collective motions.

Conclusions:

  • The proposed gGNM, gANM, mGNM, and mANM methods offer significant advancements in protein flexibility analysis.
  • Multiscale models effectively capture intrinsic multiscale behaviors in protein structures, leading to superior predictive power.
  • These novel methods provide powerful tools for understanding protein dynamics, function, and structural properties.