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...
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Feedback control systems01:26

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Positive and Negative Feedback Loops01:18

Positive and Negative Feedback Loops

Animal organs and organ systems constantly adjust to internal and external changes through a process called homeostasis ("steady state"). Examples of these changes include regulation of the level of glucose or calcium in the blood or internal responses to external temperatures. Homeostasis requires  maintaining an internal dynamic equilibrium:
Effects of feedback01:24

Effects of feedback

Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
Most signaling systems have negative feedback loops that can perform different functions such as output limiter, and adaptation.
Output limiter
Upon receiving an input signal, the cellular response rapidly increases until a threshold is reached. Beyond this threshold, a negative feedback loop...

You might also read

Related Articles

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

Sort by
Same author

NetCF: A Network Control-based Framework to Reveal the Molecular Mechanism of Phenotype Switching in Lung Cancer.

Genomics, proteomics & bioinformatics·2026
Same author

A Realistic Control Approach for Set Stabilization of Complex Biological Networks With Logical Models.

IEEE transactions on computational biology and bioinformatics·2026
Same author

A Rho GTPase-effector ensemble governs cell migration behavior.

Nature communications·2025
Same author

Identifying an optimal perturbation to induce a desired cell state by generative deep learning.

Cell systems·2025
Same author

Reverse control of biological networks to restore phenotype landscapes.

Science advances·2025
Same author

Identification of a unique subpopulation of mucosal fibroblasts in colorectal cancer with tumor-restraining characteristics.

Molecules and cells·2025

Related Experiment Video

Updated: Jul 10, 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

Analysis of feedback loops and robustness in network evolution based on Boolean models.

Yung-Keun Kwon1, Kwang-Hyun Cho

  • 1Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea. kwon@soar.snu.ac.kr

BMC Bioinformatics
|November 9, 2007
PubMed
Summary
This summary is machine-generated.

Biological networks evolve robustly through coupled feedback loops, not just preferential attachment. This finding explains the prevalence of feedback loops in biological systems, enhancing network resilience.

Related Experiment Videos

Last Updated: Jul 10, 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:

  • Systems biology
  • Network science
  • Evolutionary biology

Background:

  • Biological networks often exhibit scale-free degree distributions.
  • Preferential attachment is a leading model for network evolution, but its dynamical properties are less studied.

Purpose of the Study:

  • To investigate the evolutionary process of network robustness.
  • To compare the robustness of networks grown via preferential attachment versus those prioritizing coupled feedback loops.

Main Methods:

  • Simulations using Boolean network models.
  • Analysis of network evolution under different growth mechanisms.

Main Results:

  • Preferential attachment increases coupled feedback loops during network evolution.
  • Networks prioritizing coupled feedback loops are more robust than those grown by preferential attachment, despite similar degree distributions.

Conclusions:

  • Coupled feedback loops are crucial for acquiring robustness during network evolution.
  • This highlights a potential reason for the abundance of coupled feedback loops in biological networks.