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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

Protein-Protein Interfaces

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.

You might also read

Related Articles

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

Sort by

Same author

Predictive modeling in biology and medicine: Digital twins and multi-scale modeling.

PLoS computational biology·2026

Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026

Same author

Dynamics of attractor transitions in Boolean networks under noise.

Chaos (Woodbury, N.Y.)·2026

Same author

Digital Twins are a Key Enabling Technology for Personalized Medicine.

Bulletin of mathematical biology·2026

Same author

The role of digital twins in P4 medicine: A paradigm for modern healthcare.

NPJ digital medicine·2025

Same author

Upper bound for the stability of Boolean networks.

Physical review. E·2025

Same journal

An integrative model of FGF2-induced signaling and muscle cell proliferation.

Journal of theoretical biology·2026

Same journal

A Hybrid Reaction-Diffusion and Mechanical Stimulus Model for Mandibular Bone Remodeling under Chewing and Vibratory Loading.

Journal of theoretical biology·2026

Same journal

Integrated tick management strategies in fragmented peridomestic environments.

Journal of theoretical biology·2026

Same journal

Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and their selection coefficients in an evolving population.

Journal of theoretical biology·2026

Same journal

Misspecification of the generation time distribution and its impact on R<sub>t</sub> estimates in structured populations.

Journal of theoretical biology·2026

Same journal

Stability-driven assembly meets Prigoginian informational dissipation. A mean-field ODE comment of entropy reduction and emergent proto-self.

Journal of theoretical biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 29, 2026

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

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

Published on: October 19, 2021

Regulatory patterns in molecular interaction networks.

David Murrugarra¹, Reinhard Laubenbacher

¹Virginia Bioinformatics Institute and Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA. davidmur@vt.edu

Journal of Theoretical Biology

|August 30, 2011

Summary

This summary is machine-generated.

Molecular systems biology reveals that nested canalyzing functions in gene regulatory networks lead to more robust dynamics. This regulatory pattern, common in published models, results in fewer attractors and shorter limit cycles.

More Related Videos

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Related Experiment Videos

Last Updated: May 29, 2026

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

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

Published on: October 19, 2021

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

Molecular Systems Biology
Computational Biology
Biochemistry

Background:

Understanding molecular interaction networks is key in systems biology.
Graph theory has identified topological patterns influencing network dynamics.
Biochemical mechanisms, including additive and synergistic inputs, control network nodes.

Purpose of the Study:

To identify patterns in the mechanisms governing molecular network dynamics.
To introduce a specific type of logical rule within the multistate discrete model paradigm.
To analyze the impact of this rule on network robustness and dynamics.

Main Methods:

Introduction of a novel logical rule type within the multistate discrete model.
Reduction of this rule to nested canalyzing functions in the Boolean network context.
Comparative analysis of network dynamics between networks employing this logic and random networks.

Main Results:

Networks utilizing this multivalued logic demonstrate enhanced robustness compared to random networks.
These networks exhibit a reduced number of attractors and shorter limit cycles.
The majority of regulatory functions in published gene regulatory and signaling network models are identified as nested canalyzing.

Conclusions:

Nested canalyzing functions represent a prevalent and robust regulatory pattern in biological networks.
This finding contributes to understanding the design principles of molecular interaction networks.
The identified pattern offers insights into the stability and dynamics of gene regulatory and signaling pathways.