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

Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
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...
64
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

573
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
573
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
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...
96
Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

9.2K
According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
9.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
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...
81

You might also read

Related Articles

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

Sort by
Same author

zelll: a fast, framework-free, and flexible implementation of the cell lists algorithm for the Rust programming language.

Bioinformatics advances·2026
Same author

Architecture of the flexible tail tube of bacteriophage SPP1.

Nature communications·2020
Same author

Bayesian inference of chromatin structure ensembles from population-averaged contact data.

Proceedings of the National Academy of Sciences of the United States of America·2020
Same author

A probabilistic network model for structural transitions in biomolecules.

Proteins·2018
Same author

Data-driven coarse graining of large biomolecular structures.

PloS one·2017
Same author

Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data.

PLoS computational biology·2016
Same journal

Neuronal membrane organization by the submembranous spectrin-ankyrin scaffold: evolution, specialization and disease.

Biological chemistry·2026
Same journal

Golgi-associated membrane scaffolds: roles in health and disease.

Biological chemistry·2026
Same journal

Mechanistic insights on spatiotemporal control of Ras-signaling.

Biological chemistry·2026
Same journal

Cysteine cathepsin proteases in apicomplexan parasites.

Biological chemistry·2026
Same journal

Electron donating and withdrawing groups affect the antioxidant activity of 4'-aminochalcones on gentamicin-induced kidney cell injury.

Biological chemistry·2026
Same journal

CNKSR2 scaffold function in the mammalian nervous system.

Biological chemistry·2026
See all related articles

Related Experiment Video

Updated: Jul 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Bayesian methods in integrative structure modeling.

Michael Habeck1,2

  • 1Microscopic Image Analysis Group, Jena University Hospital, D-07743 Jena, Germany.

Biological Chemistry
|July 28, 2023
PubMed
Summary
This summary is machine-generated.

This review explores Bayesian approaches for integrative structure modeling of large biomolecular assemblies. These methods combine diverse experimental data and AI predictions to understand cellular structures and dynamics.

Keywords:
Bayesian inferenceMarkov chain Monte Carlobiomolecular structureintegrative modelingmacromolecular assemblies

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.4K

Related Experiment Videos

Last Updated: Jul 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.4K

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Growing interest in characterizing large biomolecular assemblies and their cellular interactions.
  • Diverse experimental techniques (imaging, spectroscopy, MS, genomics) and AI methods exist to study biomolecular systems.
  • A key challenge is integrating multi-scale data into comprehensive models.

Purpose of the Study:

  • To review Bayesian approaches for integrative structure modeling of biomolecular systems.
  • To highlight recent applications of these methods in structural biology.
  • To discuss current challenges and future directions in the field.

Main Methods:

  • Focus on Bayesian inference principles for data integration.
  • Review of experimental techniques including imaging, spectroscopy, cross-linking mass spectrometry, and functional genomics.
  • Incorporation of AI-assisted protein structure prediction.

Main Results:

  • Bayesian methods provide a powerful framework for integrating diverse data types.
  • Recent applications demonstrate success in modeling complex biomolecular assemblies.
  • The review outlines the utility of these approaches for understanding cellular dynamics.

Conclusions:

  • Integrative structure modeling using Bayesian approaches is crucial for understanding biomolecular systems.
  • Further development is needed to address current challenges in data integration and model refinement.
  • Future perspectives include enhanced computational tools and broader application across biological scales.