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

Circuit Terminology01:14

Circuit Terminology

3.2K
An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.
3.2K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

517
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...
517
Network Function of a Circuit01:25

Network Function of a Circuit

983
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.
983
Equivalent Resistance01:16

Equivalent Resistance

1.2K
In circuit analysis, situations often arise where resistors are neither in series nor parallel configurations. To tackle such scenarios, three-terminal equivalent networks like the wye (Y) (Figure 1 (a)) or tee (T) and delta (Δ) (Figure 1 (b)) or pi (π) networks come into play. These networks offer versatile solutions and are frequently encountered in various applications, including three-phase electrical systems, electrical filters, and matching networks.
1.2K
Neural Circuits01:25

Neural Circuits

3.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.1K
Multimachine Stability01:25

Multimachine Stability

620
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
620

You might also read

Related Articles

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

Sort by
Same author

Anlotinib-containing regimens in HR+ advanced breast cancer after prior CDK4/6 inhibitor progression.

NPJ breast cancer·2026
Same author

Clinicopathological discordance and survival outcomes in 154 breast cancer patients with pulmonary metastasis in a real-world setting.

Discover oncology·2026
Same author

Temperature-Dependent Microstructure Evolution and Superplastic Deformation Behavior of Cold-Deformed Cr4Mo4Ni4V Martensitic Steel: From Continuous to Discontinuous Dynamic Recrystallization.

Materials (Basel, Switzerland)·2026
Same author

Real-World Study on the Efficacy and Safety of Incadronate Disodium in Treating Bone Metastases of Advanced Breast Cancer.

Current pharmaceutical design·2026
Same author

Targeting tumor-specific T cells with LAG3-directed interleukin-2 prevents T-cell exhaustion and reinvigorates antitumor immunity.

Signal transduction and targeted therapy·2026
Same author

PlasmidGPT: A generative framework for plasmid analysis and generation.

Science advances·2026

Related Experiment Video

Updated: Mar 16, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K

Identifying network topologies that can generate turing pattern.

M Mocarlo Zheng1, Bin Shao2, Qi Ouyang2

  • 1The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, China.

Journal of Theoretical Biology
|August 14, 2016
PubMed
Summary
This summary is machine-generated.

This study identifies genetic network designs that create Turing patterns, essential for biological self-organization. Pure activator-inhibitor systems are most effective for robust pattern formation.

Keywords:
Network enumerationNonlinear dynamic analysisRobustnessTuring pattern

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
Plasma Lithography Surface Patterning for Creation of Cell Networks
05:58

Plasma Lithography Surface Patterning for Creation of Cell Networks

Published on: June 14, 2011

13.2K

Related Experiment Videos

Last Updated: Mar 16, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
Plasma Lithography Surface Patterning for Creation of Cell Networks
05:58

Plasma Lithography Surface Patterning for Creation of Cell Networks

Published on: June 14, 2011

13.2K

Area of Science:

  • Systems Biology
  • Developmental Biology
  • Non-equilibrium Thermodynamics

Background:

  • Turing patterns are fundamental to non-equilibrium self-organization in reaction-diffusion systems.
  • Biological development processes are hypothesized to utilize Turing instability for generating periodic patterns.

Purpose of the Study:

  • To systematically identify network topologies capable of Turing pattern formation.
  • To analyze the robustness of different network configurations for generating Turing instability.

Main Methods:

  • Enumeration of all possible 2- and 3-node genetic regulatory networks.
  • Application of linear stability analysis to assess Turing instability generation.
  • Investigation of network modifications, including additional linkages and fixed nodes.

Main Results:

  • All 3-node networks capable of Turing patterns map to pure or cross activator-inhibitor mechanisms.
  • Pure activator-inhibitor systems demonstrate greater robustness for Turing pattern formation.
  • Additional network linkages enhance performance by introducing new topologies or complementing regulations.
  • A fixed node enables Turing pattern formation even with similar diffusion coefficients for morphogens.

Conclusions:

  • Provides design principles for robust genetic circuits that generate Turing patterns.
  • Offers a framework for systematically exploring other bifurcation phenomena in biological systems.