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: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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...
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.
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...

You might also read

Related Articles

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

Sort by
Same author

NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.

Bioinformatics (Oxford, England)·2026
Same author

Discrete turn strategies emerge in information-limited navigation.

ArXiv·2026
Same author

Large-scale, spatially resolved panoramic CRISPR screening in native tissue environments using Perturb-DBiT.

Nature biotechnology·2026
Same author

Making invisible excited-state structures of pro-interleukin-18 visible by combining NMR and machine learning.

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

Growth-dependent sensory bet-hedging enhances collective navigation.

bioRxiv : the preprint server for biology·2026
Same author

<i>E. coli</i> chemosensing accuracy is not limited by stochastic molecule arrivals.

Nature physics·2026

Related Experiment Video

Updated: Jun 21, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Understanding modularity in molecular networks requires dynamics.

Roger P Alexander1, Philip M Kim, Thierry Emonet

  • 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.

Science Signaling
|July 30, 2009
PubMed
Summary
This summary is machine-generated.

Understanding cellular behavior requires analyzing molecular networks. Recent advances highlight the importance of dynamic perspectives for defining modules and their functions in biological systems.

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

Related Experiment Videos

Last Updated: Jun 21, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

Area of Science:

  • Systems Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Genome sequencing provides extensive molecular part data.
  • Understanding cellular behavior necessitates studying molecular interactions.
  • Molecular networks offer a framework for data organization and analysis.

Purpose of the Study:

  • To review recent advances in biological network modularity analysis.
  • To emphasize the necessity of a dynamic perspective in network analysis.
  • To connect network structure and dynamics to cellular behavior.

Main Methods:

  • Analysis of molecular network structures.
  • Application of systems biology principles.
  • Focus on modularity in biological networks.

Main Results:

  • Molecular networks are crucial for understanding cellular function.
  • Bridging network structure to dynamics remains a challenge.
  • Modularity is a key concept in biological network analysis.

Conclusions:

  • A dynamic perspective is essential for defining molecular modules.
  • Understanding collective function requires dynamic analysis of modules.
  • Integrating dynamic and structural network properties is key for systems biology.