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

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...

Mutual Inductance

Mutual Inductance

Inductance is the property of a device that tells us how effectively it induces an emf in another device. In other words, it is a physical quantity that expresses the effectiveness of a given device.
When two circuits carrying time-varying currents are close to one another, the magnetic flux through each circuit varies because of the changing current in the other circuit. Consequently, an emf is induced in each circuit by the changing current in the other. Therefore, this type of emf is called...

Noncovalent Attractions in Biomolecules

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

Noncovalent Attractions in Biomolecules

Noncovalent Attractions in Biomolecules

You might also read

Related Articles

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

Sort by

Same author

Can ephapticity contribute to brain complexity?

PloS one·2024

Same author

Applying a cytochrome c oxidase I barcode for Leishmania species typing.

PloS one·2024

Same author

Cycling reduces the entropy of neuronal activity in the human adult cortex.

PloS one·2024

Same author

Recurrence microstates for machine learning classification.

Chaos (Woodbury, N.Y.)·2024

Same author

Molecular and morphometric study of Brazilian populations of Psychodopygus davisi.

Medical and veterinary entomology·2023

Same author

HRM Accuracy and Limitations as a Species Typing Tool for <i>Leishmania</i> Parasites.

International journal of molecular sciences·2023

Search research articles

Related Experiment Video

Updated: Nov 27, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Mutual Information as a General Measure of Structure in Interaction Networks.

Gilberto Corso¹, Gabriel M F Ferreira², Thomas M Lewinsohn^2,3

¹Departamento de Biofísica e Farmacologia, Centro de Biociências, Universidade Federal do Rio Grande do Norte (UFRN), Natal-RN 59072-970, Brazil.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

Mutual information (MI) quantifies interaction structure in ecological networks, offering a general measure applicable across diverse network types and spatial scales. This approach reveals that network topology differences depend more on size and occupancy than inherent patterns.

Keywords:

community ecology interaction diversity specialization

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

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: Nov 27, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

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:

Ecology
Network Analysis
Information Theory

Background:

Biological diversity is traditionally measured using entropy-based indices.
Ecological interactions are often represented as bipartite networks or interaction matrices.
Previous research focused on specific network patterns like nestedness and modularity.

Purpose of the Study:

To investigate mutual information (MI) as a general measure of structure in ecological interaction networks.
To assess MI's applicability across different network topologies and parameters.
To explore MI's utility in comparative ecological network analyses.

Main Methods:

Developed analytical solutions for MI in various ecological network models.
Analyzed MI's response to network parameters such as size and occupancy.
Evaluated MI across nested, modular, and compound network structures.

Main Results:

Mutual information (MI) is sensitive to fundamental matrix parameters like dimension and occupancy, but can be normalized.
Observed differences in network topologies are largely explained by variations in dimension and occupancy.
MI proves to be a versatile metric for analyzing interaction structures.

Conclusions:

Mutual information (MI) serves as a robust, generalizable measure for ecological interaction network structure.
MI's adaptability allows for meaningful comparisons of ecological networks across geographical gradients or interaction types.
This approach enhances the conceptual and empirical analysis of ecological communities.