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

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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.
Second Order systems II01:18

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
If  ζ...
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,
First Order Systems01:21

First Order Systems

First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
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

Epstein-Barr Virus-Induced Upregulation of GPR183: A Potential Upstream Mechanism in IgG4-Related Ophthalmic Disease.

Investigative ophthalmology & visual science·2026
Same author

Optical coherence tomography features and visual prognosis in vitreoretinal lymphoma: a structured phenotyping study.

Frontiers in medicine·2026
Same author

Clinical Application of the Modified Scleral Tunnel Intraocular Lens Ciliary Sulcus Suture-Fixation Technique.

Journal of ophthalmology·2026
Same author

Temperature-dependent dielectric function in plasmonic nanobubble formation.

Physical chemistry chemical physics : PCCP·2026
Same author

Topical application of low-concentration IL-2 enhances Treg's function and plays anti-inflammatory roles in experimental dry eye disease.

The ocular surface·2026
Same author

Combined exposure to 6PPD and 6PPD-Q induced neurotoxic responses in zebrafish and SH-SY5Y cells: Evidence from neurotransmitter disruption, oxidative damage, and apoptosis.

Neurotoxicology·2026

Related Experiment Video

Updated: Jul 10, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Incoherent Input as a Key Strategy for Robust Time-Dependent Biphasic Dynamics.

Qinlin Li1, Yuzhi Hu1, Hong Qi2

  • 1Anhui Key Laboratory for Control and Applications of Optoelectronic Information Materials, School of Physics and Electronic Information, Anhui Normal University, Wuhu, Anhui 241002, P. R. China.

ACS Synthetic Biology
|July 9, 2026
PubMed
Summary

Biological systems use time-dependent biphasic dynamics (TBD) for regulation. This study identifies core network motifs, like incoherent feedforward loops, essential for robust TBD generation and proposes a design paradigm for enhanced biological network robustness.

Keywords:
hypermotifsincoherent inputnetwork motifsnetwork searchrobustness

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses
08:34

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Published on: May 9, 2021

Related Experiment Videos

Last Updated: Jul 10, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses
08:34

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Published on: May 9, 2021

Area of Science:

  • Systems Biology
  • Network Biology
  • Computational Biology

Background:

  • Biological systems rely on precise, time-dependent responses for functional order.
  • Time-dependent biphasic dynamics (TBD) are crucial regulatory mechanisms.
  • Understanding the network topologies and robustness principles of TBDs is limited.

Purpose of the Study:

  • To systematically investigate network motifs capable of generating TBDs.
  • To identify core topological modules underlying TBD generation and robustness.
  • To propose a design paradigm for constructing robust biological networks.

Main Methods:

  • Systematic investigation of all one-, two-, and three-node network motifs.
  • Extensive random parameter sampling to evaluate TBD generation capacity.
  • Statistical analysis of network dynamics and topological landscape construction.

Main Results:

  • Incoherent feedforward loops and negative feedback loops identified as core modules for TBD generation.
  • 13 minimal core three-node motifs identified, forming a topological landscape of robustness.
  • Coupling motifs into feedback-enriched hypermotifs significantly enhances TBD robustness.

Conclusions:

  • Incoherent input, especially inhibitory regulation targeting the output node, is key for robust TBDs.
  • Biological networks exhibit modular principles for robust TBD generation.
  • A motif-to-hypermotif design paradigm offers insights into engineering robust biological functions.