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

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Applications Of NMR In Biology

Applications Of NMR In Biology

Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.

You might also read

Related Articles

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

Sort by

Same author

Accuracy of the smartphone blood pressure measurement solution OptiBP to track blood pressure changes in pregnant women.

Journal of hypertension·2025

Same author

Accuracy of blood pressure measurement across BMI categories using the OptiBP™ mobile application.

Blood pressure·2022

Same author

Smartphone based blood pressure measurement: accuracy of the OptiBP mobile application according to the AAMI/ESH/ISO universal validation protocol.

Blood pressure monitoring·2021

Same author

Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements.

Scientific reports·2020

Same author

Bisimilar Booleanization of multivalued networks.

Bio Systems·2020

Same author

Proceedings of the XXXVIIIth Seminar of the French-Speaking Society for Theoretical Biology; Saint-Flour (Cantal), France, 11-13 June, 2018.

Acta biotheoretica·2020

Search research articles

Related Experiment Video

Updated: Jan 20, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

On Computing Structural and Behavioral Complexities of Threshold Boolean Networks : Application to Biological

Urvan Christen¹, Sergiu Ivanov², Rémi Segretain¹

¹CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAG, Univ. Grenoble Alpes, 38000, Grenoble, France.

Acta Biotheoretica

|August 26, 2019

Summary

This summary is machine-generated.

Threshold Boolean networks (TBNs) can exhibit simple behaviors despite complex structures. Our study reveals most TBNs are structurally complex, even when their dynamics are simple, using a novel complexity computation method.

Keywords:

Biological regulation Complexity Threshold Boolean networks

More Related Videos

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

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jan 20, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Systems biology
Computational biology
Network science

Background:

Threshold Boolean networks (TBNs) are used to model biological systems, such as gene regulatory networks.
Different TBNs can exhibit identical dynamic behaviors, making it challenging to infer network structure from function.
The complexity of these networks, both structural and behavioral, is a key area of investigation.

Purpose of the Study:

To investigate the relationship between structural and behavioral complexity in Threshold Boolean networks.
To determine if TBNs with simple dynamics are also structurally simple.
To develop a method for quantifying the structural complexity of TBNs.

Main Methods:

Developed a novel method to compute the structural complexity of TBNs.
Estimated the sizes of equivalence classes for the threshold Boolean functions within TBNs.
Computed both structural and behavioral complexities for various TBN models.

Main Results:

Most Threshold Boolean networks are found to be structurally complex.
This structural complexity persists even in TBNs that exhibit simple, predictable behaviors.
The developed method provides a quantitative measure for TBN structural complexity.

Conclusions:

A significant disconnect exists between the structural complexity and behavioral simplicity in many TBNs.
Understanding this structural complexity is crucial for accurate modeling of biological networks.
The new method offers a valuable tool for analyzing the complexity of TBNs in systems biology.