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

Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
Construction of Root Locus01:15

Construction of Root Locus

The construction of a root locus involves several key steps to analyze and visualize the behavior of a system's poles with varying gain. The number of branches in the root locus equals the number of closed-loop poles and is symmetrical about the real axis.
For positive gain values, the root locus exists on the real axis to the left of an odd number of finite open-loop poles or zeros. The root locus starts at the open-loop poles and traces the paths of the closed-loop poles as the gain increases.
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Plotting and Calibrating the Root Locus01:19

Plotting and Calibrating the Root Locus

Root loci often diverge as system poles shift from the real axis to the complex plane. Key points in this transition are the breakaway and break-in points, indicating where the root locus leaves and reenters the real axis. The branches of the root locus form an angle of 180/n degrees with the real axis, where n is the number of branches at a breakaway or break-in point.
The maximum gain occurs at the breakaway points between open-loop poles on the real axis, while the minimum gain is observed...
Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
Control System Problem01:21

Control System Problem

In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...

You might also read

Related Articles

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

Sort by
Same author

SpecEStop: Self-Supervised Hyperspectral Mixed Noise Removal via Deep Spectral Prior.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis.

NPJ digital medicine·2026
Same author

Local and High-Order Consistency Coding and Adaptation for Cross-Hypergraph Node Classification.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Separable Decomposition for Ragged Tensors.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Nonlinear Transformed Low-Rank Quaternion Tensor Total Variation for Multidimensional Color Image Completion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

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

IEEE transactions on cybernetics·2026

Related Experiment Video

Updated: Jul 7, 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

Algorithms for finding small attractors in Boolean networks.

Shu-Qin Zhang1, Morihiro Hayashida, Tatsuya Akutsu

  • 1Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong.

EURASIP Journal on Bioinformatics & Systems Biology
|February 7, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces efficient algorithms for finding attractors in Boolean networks, significantly improving computational speed for genetic regulatory network analysis. These methods accelerate the identification of key network states compared to traditional approaches.

Related Experiment Videos

Last Updated: Jul 7, 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

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Boolean networks model gene interactions in genetic regulatory networks.
  • Identifying attractors is crucial for understanding network dynamics.
  • Existing methods for attractor identification can be computationally intensive.

Purpose of the Study:

  • To develop novel algorithms for identifying singleton and small attractors in Boolean networks.
  • To analyze the time complexity of these new algorithms.
  • To provide a rigorous proof for the NP-hardness of finding shortest period attractors.

Main Methods:

  • Utilized gene ordering and feedback vertex sets for attractor identification.
  • Developed and analyzed average-case time complexities of proposed algorithms.
  • Conducted extensive computational experiments to validate theoretical results.

Main Results:

  • An outdegree-based ordering algorithm achieves O(1.19(n)) time complexity for finding singleton attractors (K=2).
  • This represents a significant speed improvement over the naive O(2(n)) algorithm.
  • Proved that finding an attractor with the shortest period is NP-hard.

Conclusions:

  • The proposed algorithms offer efficient solutions for analyzing Boolean networks.
  • Computational experiments confirm the theoretical efficiency gains.
  • The NP-hardness result highlights the inherent complexity of certain attractor-finding problems.