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

Graphs of Functions01:30

Graphs of Functions

Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
Multiple Bar Graph01:07

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all points...
Types of Limits II01:24

Types of Limits II

When observing how a curve behaves near a specific point along the horizontal axis, there are cases where the curve’s height increases or decreases without limit as the position draws closer to that point. The curve does not settle at any particular value; instead, the values grow more extreme—upward or downward—the nearer they get. No defined value exists exactly at that location, yet the surrounding behavior becomes more dramatic, indicating a sharp change in direction.The values may rise...
Graphs of Trigonometric Functions01:30

Graphs of Trigonometric Functions

Trigonometric functions exhibit periodic and symmetrical behavior, deeply rooted in the unit circle. The sine and cosine functions correspond to the vertical and horizontal projections, respectively, of a point rotating counterclockwise around the circle. These functions trace smooth, repeating waveforms with identical periods and bounded ranges. The tangent function is defined as the ratio of sine to cosine and produces an unbounded curve that repeats every units, with vertical asymptotes...

You might also read

Related Articles

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

Sort by
Same author

Remarks on feedforward circuits, adaptation, and pulse memory.

IET systems biology·2009
Same author

Proximity of intracellular regulatory networks to monotone systems.

IET systems biology·2008
Same author

Some new directions in control theory inspired by systems biology.

Systems biology·2006
Same author

Methods of robustness analysis for Boolean models of gene control networks.

Systems biology·2006
Same author

Parameter estimation in models combining signal transduction and metabolic pathways: the dependent input approach.

Systems biology·2006
Same author

Neural systems as nonlinear filters.

Neural computation·2000
Same journal

Hepatitis B virus spreading via Beddington-DeAngelis incidence function and feed-forward neural network with optimal control.

Journal of biological dynamics·2026
Same journal

Optimal pest management in Moringa (<i>Moringa oleifera</i>): a mathematical model incorporating integrated pesticide use.

Journal of biological dynamics·2026
Same journal

The behavioural spillover effect: modelling behavioural interdependencies in multi-pathogen dynamics.

Journal of biological dynamics·2026
Same journal

Bistable wave speed of a diffusive three-species Lotka-Volterra competition model.

Journal of biological dynamics·2026
Same journal

A general analytic approach to predicting the best antibiotic dosing regimen.

Journal of biological dynamics·2026
Same journal

Dynamics of virus infection under the influence of antibody and cytokine.

Journal of biological dynamics·2026
See all related articles

Related Experiment Video

Updated: May 19, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Monotone bifurcation graphs.

G A Enciso1, E D Sontag

  • 1Mathematical Biosciences Institute, Ohio State University, Columbus, Ohio, USA. genciso@mbi.osu.edu

Journal of Biological Dynamics
|August 14, 2012
PubMed
Summary
This summary is machine-generated.

This study expands feedback decomposition methods to analyze complex dynamical systems, enhancing stability analysis for larger networks like gene regulatory networks. The approach is also extended to systems with delays and diffusion.

More Related Videos

4D Printed Bifurcated Stents with Kirigami-Inspired Structures
06:52

4D Printed Bifurcated Stents with Kirigami-Inspired Structures

Published on: July 25, 2019

Related Experiment Videos

Last Updated: May 19, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

4D Printed Bifurcated Stents with Kirigami-Inspired Structures
06:52

4D Printed Bifurcated Stents with Kirigami-Inspired Structures

Published on: July 25, 2019

Area of Science:

  • Dynamical Systems Theory
  • Systems Biology
  • Control Theory

Background:

  • Previous work utilized feedback decomposition for strongly monotone systems.
  • Analyzing complex dynamical systems often requires advanced theoretical tools.
  • Understanding the global behavior and stability of such systems is crucial.

Purpose of the Study:

  • To generalize feedback decomposition techniques to a broader class of dynamical systems.
  • To apply these generalized methods to analyze a nine-variable autoregulatory transcription network.
  • To explore extensions of the methodology to delay and reaction diffusion systems.

Main Methods:

  • Generalization of feedback decomposition into well-behaved subsystems.
  • Application of the technique to a specific biological network model.
  • Theoretical development for incorporating delays and diffusion.

Main Results:

  • The generalized approach successfully applies to a wider range of systems.
  • The analysis of the transcription network provided insights into its stability and behavior.
  • Feasibility of extending the method to delay and reaction diffusion systems was demonstrated.

Conclusions:

  • Feedback decomposition is a powerful and generalizable technique for analyzing complex dynamical systems.
  • This enhanced methodology offers new avenues for studying biological networks and other complex systems.
  • The work paves the way for analyzing systems with more intricate dynamics, including time delays and spatial effects.