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

Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

6.4K
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...
6.4K
Overview of Cell Signaling01:23

Overview of Cell Signaling

20.4K
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...
20.4K
Cell-matrix's Response to Mechanical Forces01:13

Cell-matrix's Response to Mechanical Forces

2.7K
In animal cells, the extracellular matrix allows cells within tissues to withstand external stresses and transmits signals from the outside of the cell to the inside. The extracellular matrix is extensive, and its composition varies between different types of tissues. For example, the reticular fibers and ground substance make up the ECM in loose connective tissue, while collagen and bone minerals make up the ECM of bone tissue. 
Anchoring junctions mechanically attach a cell to the...
2.7K
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

6.5K
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...
6.5K
What is Cell Signaling?02:03

What is Cell Signaling?

119.1K
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 to respond to the environment.
119.1K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.5K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.5K

You might also read

Related Articles

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

Sort by
Same author

Prediction of Trends and Bioclimatic Factors Influencing the Monthly Incidence of Zoonotic Cutaneous Leishmaniasis Using Arima and Sarima Time Series Models in Maraveh Tappeh County, Golestan Province, Iran.

Journal of arthropod-borne diseases·2026
Same author

Recovering Reward Functions From Distributed Expert Demonstrations via Bi-Level Maximum-Likelihood Optimization.

IEEE transactions on neural networks and learning systems·2026
Same author

The crossroads between osteosarcopenia and intrinsic capacity-a narrative review.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

The interplay between osteosarcopenia and intrinsic capacity: insights and associations with all-cause mortality in the Toledo Study for Healthy Aging.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Bayesian Topology Inference of Regulatory Networks under Partial Observability.

Results in control and optimization·2026
Same author

Pareto-Optimal Interventions in Gene Regulatory Networks using Signal Temporal Logic.

Proceedings of the ... American Control Conference. American Control Conference·2026
Same journal

From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction.

... IEEE Conference on Artificial Intelligence·2025
Same journal

Structure-Based Inverse Reinforcement Learning for Quantification of Biological Knowledge.

... IEEE Conference on Artificial Intelligence·2023
See all related articles

Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila
05:27

Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila

Published on: July 26, 2024

533

Learning to Fight Against Cell Stimuli: A Game Theoretic Perspective.

Seyed Hamid Hosseini1, Mahdi Imani1

  • 1Department of Electrical and Computer Engineering at Northeastern University.

... IEEE Conference on Artificial Intelligence
|October 3, 2023
PubMed
Summary
This summary is machine-generated.

Current genomic interventions fail to address dynamic cellular responses, leading to treatment ineffectiveness. A new game-theoretic model explains this dynamic and explores AI-driven solutions for improved personalized medicine.

More Related Videos

Silicon Microchips for Manipulating Cell-cell Interaction
23:21

Silicon Microchips for Manipulating Cell-cell Interaction

Published on: August 30, 2007

10.8K
A New Approach that Eliminates Handling for Studying Aggression and the "Loser" Effect in Drosophila melanogaster
07:19

A New Approach that Eliminates Handling for Studying Aggression and the "Loser" Effect in Drosophila melanogaster

Published on: December 30, 2015

9.7K

Related Experiment Videos

Last Updated: Jul 15, 2025

Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila
05:27

Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila

Published on: July 26, 2024

533
Silicon Microchips for Manipulating Cell-cell Interaction
23:21

Silicon Microchips for Manipulating Cell-cell Interaction

Published on: August 30, 2007

10.8K
A New Approach that Eliminates Handling for Studying Aggression and the "Loser" Effect in Drosophila melanogaster
07:19

A New Approach that Eliminates Handling for Studying Aggression and the "Loser" Effect in Drosophila melanogaster

Published on: December 30, 2015

9.7K

Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Genomic sequencing advances personalized medicine but struggles with cellular complexity and dynamic responses.
  • Current interventions are limited by their inability to account for cell stimuli and dynamic responses.
  • These limitations contribute to chronic disease recurrence and suboptimal genomic interventions.

Purpose of the Study:

  • To analyze the dynamic interplay between cellular responses and genomic interventions.
  • To demonstrate why current genomic interventions become ineffective over time.
  • To explore the potential of artificial intelligence in developing more effective genomic solutions.

Main Methods:

  • Development of a game-theoretic model to simulate the interaction between cells and interventions.
  • Analytical and numerical demonstrations of the model's predictions.
  • Analysis of melanoma regulatory networks to assess intervention performance.

Main Results:

  • The game-theoretic model analytically and numerically demonstrates the progressive ineffectiveness of current interventions.
  • The study highlights the critical role of dynamic cellular responses in intervention outcomes.
  • Melanoma regulatory networks serve as a case study for performance analysis.

Conclusions:

  • Current genomic interventions are inherently limited by their static approach to dynamic biological systems.
  • A dynamic, game-theoretic perspective is crucial for understanding intervention failure.
  • Artificial intelligence offers a promising avenue for deriving adaptive and effective genomic interventions.