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

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:
What is Homeostasis?01:16

What is Homeostasis?

Maintaining homeostasis requires that the body continuously maintain its internal conditions. Each physiological condition has a particular set point, from body temperature to blood pressure to levels of certain nutrients. A set point is the physiological value around which the normal range fluctuates. A normal range is a restricted set of values that is optimally healthful and stable. For example, the set point for normal human body temperature is approximately 37°C (98.6°F). Physiological...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

You might also read

Related Articles

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

Sort by
Same author

SARS-CoV-2 reshapes m<sup>6</sup>A methylation in long noncoding RNAs of human lung cells.

NAR molecular medicine·2025
Same author

Homeostasis in input-output networks: Structure, Classification and Applications.

Mathematical biosciences·2025
Same author

Statistical Distributions of Genome Assemblies Reveal Random Effects in Ancient Viral DNA Reconstructions.

Viruses·2025
Same author

The Relationship between HERV, Interleukin, and Transcription Factor Expression in ZIKV Infected versus Uninfected Trophoblastic Cells.

Cells·2024
Same author

Reconstructing Prehistoric Viral Genomes from Neanderthal Sequencing Data.

Viruses·2024
Same author

Homeostasis in networks with multiple inputs.

Journal of mathematical biology·2024
Same journal

Slow Evolution Towards Generalism in a Model of Variable Dietary Range.

Bulletin of mathematical biology·2026
Same journal

CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Bulletin of mathematical biology·2026
Same journal

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Bulletin of mathematical biology·2026
Same journal

Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.

Bulletin of mathematical biology·2026
Same journal

The Exact Hypergeometric Posterior Method for Accurate Inference of Population Size from Mark-Recapture Data.

Bulletin of mathematical biology·2026
Same journal

Modeling, Analysis, and Optimal Control of Leukemic Cell Population Dynamics Under Therapy.

Bulletin of mathematical biology·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 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

Automated Classification of Homeostasis Structure in Input-Output Networks.

Xinni Lin1, Fernando Antoneli2, Yangyang Wang3

  • 1Department of Mathematics, University of Southern California, Los Angeles, California, 90007, USA.

Bulletin of Mathematical Biology
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Python algorithm to identify biological homeostasis mechanisms by analyzing network topology. The tool simplifies complex graph theory, making homeostasis analysis scalable and accessible for diverse biological systems.

Keywords:
Coupled dynamical systemsInfinitesimal homeostasisInput-output networkRobust perfect adaptation

More Related Videos

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors
08:33

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

Published on: July 28, 2023

Related Experiment Videos

Last Updated: Jun 12, 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

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors
08:33

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

Published on: July 28, 2023

Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Theory

Background:

  • Homeostasis is crucial for biological systems, maintaining stable outputs despite external changes.
  • Mathematical models describe homeostasis using input-output functions and singularity theory.
  • Current methods for identifying homeostatic mechanisms are computationally intensive and complex.

Purpose of the Study:

  • To develop a scalable and accessible computational tool for identifying homeostasis subnetworks.
  • To automate the analysis of homeostatic mechanisms directly from biological network topology.
  • To extend existing theoretical frameworks to handle complex biological networks.

Main Methods:

  • Developed a Python-based algorithm to analyze network connectivity and identify homeostasis subnetworks.
  • Utilized graph-theoretical concepts and combinatorial enumeration for automated analysis.
  • Extended the theoretical framework to accommodate multiple input nodes via an augmented single-input-node representation.

Main Results:

  • The algorithm successfully identifies homeostasis subnetworks and their conditions from network topology.
  • Demonstrated applicability across various biological network sizes and complexities, including multi-input scenarios.
  • Provided a scalable and systematic computational framework for classifying homeostatic mechanisms.

Conclusions:

  • The developed algorithm overcomes limitations of previous methods, enhancing accessibility and scalability.
  • This computational framework facilitates the application of mathematical theories to complex biological systems.
  • Enables efficient classification of homeostatic mechanisms in diverse biological networks.