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

Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

7.1K
The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
7.1K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

4.7K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
4.7K
Transient and Steady-state Response01:24

Transient and Steady-state Response

754
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
754
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

155
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
155
Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

5.8K
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...
5.8K
Overview of Cell Signaling01:23

Overview of Cell Signaling

16.5K
Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
Cells respond to many types of information, often through receptor proteins positioned on the membrane. For example, skin cells respond to and transmit touch...
16.5K

You might also read

Related Articles

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

Sort by
Same author

Methanol Clipping Modification on Liquid Metal Surface Enhances Photothermal Performance and Biocompatibility.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026
Same author

Total-Body Dynamic PET/CT Imaging of Proton-Induced Activity and Biologic Washout After Proton Therapy.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same author

Unlocking the Quality Potential of Liberoid Coffee: Advances in Composition, Processing, and Microbial Fermentation.

Comprehensive reviews in food science and food safety·2026
Same author

Prognostic value of the triglyceride-glucose (TyG) index for renal function progression in patients with CKD stages 3-4.

Frontiers in nutrition·2026
Same author

Non-Arrhenius threshold switching by field-driven dipolar ordering.

Nature communications·2026

Related Experiment Video

Updated: Apr 28, 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

1.7K

Predicting dynamic signaling network response under unseen perturbations.

Fan Zhu1, Yuanfang Guan2

  • 1Department of Computational Medicine and Bioinformatics, Department of Internal Medicine and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.

Bioinformatics (Oxford, England)
|June 13, 2014
PubMed
Summary
This summary is machine-generated.

A new computational model accurately predicts cell signaling protein responses to perturbations. This systems biology approach offers faster, genome-wide pathway analysis for drug design and treatment prediction.

More Related Videos

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

10.8K
Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

850

Related Experiment Videos

Last Updated: Apr 28, 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

1.7K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

10.8K
Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

850

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Predicting signaling network dynamics under perturbations is crucial for understanding biological pathways.
  • Accurate modeling has applications in drug design and predicting treatment outcomes.

Purpose of the Study:

  • To develop a novel computational model for predicting cell type-specific time-course responses of signaling proteins to unseen perturbations.

Main Methods:

  • The trajectory prediction problem is formulated as a regularization problem.
  • The algorithm involves denoising, estimating regression coefficients, and modeling trajectories.
  • The method was validated against simulation and experimental data.

Main Results:

  • The algorithm achieved top performance in the DREAM 8 time course prediction challenge.
  • It accurately predicts cell type-specific protein responses under novel perturbations.
  • The method significantly reduces computational time compared to existing approaches.

Conclusions:

  • This novel model provides an accurate and efficient tool for predicting signaling network dynamics.
  • It enables practical, genome-wide modeling of signaling pathways and time-course trajectories.
  • The approach has potential for advancing drug discovery and personalized medicine.