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

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Fluid Mosaic Model01:34

Fluid Mosaic Model

The fluid mosaic model was first proposed as a visual representation of research observations. The model comprises the composition and dynamics of membranes and serves as a foundation for future membrane-related studies. The model depicts the structure of the plasma membrane with a variety of components, which include phospholipids, proteins, and carbohydrates. These integral molecules are loosely bound, defining the cell’s border and providing fluidity for optimal function.LipidsThe most...
Induced-fit Model01:13

Induced-fit Model

Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical characteristics of...
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

You might also read

Related Articles

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

Sort by
Same author

Toward a unifying mechanistic model of eukaryotic translation initiation through integrative single-molecule, structural, and computational insights.

Current opinion in structural biology·2026
Same author

RNA localization to nuclear speckles follows splicing logic.

Nucleic acids research·2026
Same author

Elevator mechanism dynamics in a sodium-coupled dicarboxylate transporter.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Structural dependence of ProQ-mRNA interactions in live bacterial cells.

Communications biology·2025
Same author

Transcriptome-wide mRNP condensation precedes stress granule formation and excludes new mRNAs.

Molecular cell·2025
Same author

A cascade of structural rearrangements positions peptide release factor 2 for polypeptide hydrolysis on the ribosome.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Graphical models for inferring single molecule dynamics.

Jonathan E Bronson1, Jake M Hofman, Jingyi Fei

  • 1Department of Chemistry, Columbia University, New York, NY 10027, USA. jeb2126@columbia.edu

BMC Bioinformatics
|November 2, 2010
PubMed
Summary
This summary is machine-generated.

Graphical modeling with variational Bayesian expectation maximization (VBEM) offers new computational tools for analyzing single-molecule biophysics time series data. This approach provides robust model selection and parameter inference for dynamic processes.

More Related Videos

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy
11:26

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Published on: September 8, 2009

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

Related Experiment Videos

Last Updated: Jun 7, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy
11:26

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Published on: September 8, 2009

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

Area of Science:

  • Single-molecule biophysics
  • Computational biophysics
  • Statistical inference

Background:

  • Advancements in single-molecule biophysics yield complex time series data.
  • Novel computational tools are essential for analyzing this data.
  • Graphical modeling presents a powerful framework for such analyses.

Purpose of the Study:

  • To describe the application of graphical modeling for learning from biophysical time series data.
  • To introduce the variational Bayesian expectation maximization (VBEM) algorithm for this purpose.
  • To illustrate the methodology using single-molecule fluorescence resonance energy transfer (smFRET) data.

Main Methods:

  • Modeling smFRET time series data using a hidden Markov model (HMM) with Gaussian observables.
  • Employing the variational Bayesian expectation maximization (VBEM) algorithm for parameter inference and model selection.
  • Comparing VBEM with maximum likelihood (ML) optimized by expectation maximization (EM).

Main Results:

  • The VBEM algorithm yields model evidence for model selection (maximum evidence, ME).
  • It provides an approximating posterior parameter distribution, describing learned parameters.
  • VBEM offers advantages over ML/EM, including natural model selection and well-posed optimization.

Conclusions:

  • Graphical modeling is a valuable tool for inferring dynamic processes in single-molecule biophysics.
  • The VBEM algorithm provides a robust and effective method for analyzing complex biophysical time series.
  • This approach enhances the understanding of molecular dynamics from experimental data.