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

Local Attraction01:22

Local Attraction

130
Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...
130
Detection of Black Holes01:10

Detection of Black Holes

2.3K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.3K
Factors Influencing Attraction III: Similarity01:23

Factors Influencing Attraction III: Similarity

5
The similarity hypothesis suggests that individuals are more likely to form relationships with others who share similar attitudes, beliefs, values, and interests. This concept has been widely studied in social psychology, demonstrating that perceived similarity fosters interpersonal attraction. In an experiment supporting this hypothesis, participants were presented with fabricated information indicating that strangers held attitudes similar to their own. The results showed that participants...
5
Factors Influencing Attraction I: Proximity01:22

Factors Influencing Attraction I: Proximity

8
Proximity plays a fundamental role in shaping interpersonal attraction by increasing opportunities for interaction and fostering familiarity. Research consistently demonstrates that individuals are more likely to form social bonds with those who are physically closer to them, whether in residential settings, workplaces, or educational institutions. This effect is largely driven by the increased frequency of encounters, which facilitates the development of friendships and romantic...
8
Block Diagram Reduction01:22

Block Diagram Reduction

300
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
300
Network Function of a Circuit01:25

Network Function of a Circuit

408
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.
408

You might also read

Related Articles

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

Sort by
Same author

On the Number of Control Nodes in Boolean Networks With Degree Constraints.

IEEE transactions on cybernetics·2026
Same author

DiCleavePlus: A Transformer-Based Model to Detect Human Dicer Cleavage Sites Within Cleavage Patterns.

Genes to cells : devoted to molecular & cellular mechanisms·2025
Same author

Toward Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming.

ACS omega·2025
Same author

Enhancing epidemic forecasting with a physics-informed spatial identity neural network.

PloS one·2025
Same author

Cycle-configuration descriptors: a novel graph-theoretic approach to enhancing molecular inference.

Journal of cheminformatics·2025
Same author

Identification of Keratin 5-Expressing Fibroblasts for Regenerating Keratinocytes in the Necrotic Skin Graft.

JID innovations : skin science from molecules to population health·2025

Related Experiment Video

Updated: Sep 20, 2025

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.2K

Attractor detection and enumeration algorithms for Boolean networks.

Tomoya Mori1, Tatsuya Akutsu1

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan.

Computational and Structural Biotechnology Journal
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

This review presents efficient algorithms for detecting attractors in Boolean networks (BNs), overcoming the NP-hard complexity of previous methods. These algorithms offer guaranteed computational time limits for specific BN classes, aiding biological systems analysis.

Keywords:
Boolean networkComputational complexityNested canalyzing functionPeriodic attractorSATSingleton attractor

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

652

Related Experiment Videos

Last Updated: Sep 20, 2025

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.2K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

652

Area of Science:

  • Computational Biology
  • Systems Biology
  • Network Science

Background:

  • Boolean networks (BNs) model complex biological systems like gene regulatory networks.
  • Attractors in BNs represent stable system states crucial for understanding overall system behavior.
  • Detecting attractors in BNs is computationally challenging (NP-hard), hindering analysis.

Purpose of the Study:

  • To review algorithms for singleton/periodic attractor detection in Boolean networks.
  • To highlight algorithms with guaranteed computational complexities below a specific threshold ().
  • To address the computational difficulty in analyzing BNs.

Main Methods:

  • Focus on algorithms with guaranteed computational complexities for specific BN classes.
  • Consider synchronous update models with constraints on indegree, Boolean functions (AND/OR, nested canalyzing).
  • Briefly review practically efficient algorithms for attractor detection.

Main Results:

  • Identified algorithms with proven computational complexities less than for specific BN classes.
  • Demonstrated that attractor detection can be tractable under certain constraints.
  • Provided an overview of efficient practical algorithms.

Conclusions:

  • Efficient attractor detection algorithms exist for restricted classes of Boolean networks.
  • These algorithms mitigate the computational burden of BN analysis.
  • Further research can explore practical applications and broader BN classes.