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

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
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
Positive and Negative Feedback Loops01:18

Positive and Negative Feedback Loops

14.9K
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:
14.9K
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
Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

15.2K
When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
15.2K
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

145
The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A...
145

You might also read

Related Articles

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

Sort by
Same author

Molecular Functions of Ubiquitin-like Modifiers in Bacterial Infection.

Cells·2026
Same author

Neuroprotective efficacy of Curcumae Rhizoma against ischemic stroke via attenuation of COX-2-mediated neuroinflammation: a systematic study predicting thrombin inhibition and highlighting alexandrin and hederagenin.

Metabolic brain disease·2026
Same author

Implementation of an AI-Based Clinical Decision Support System Predicting In-Hospital Cardiac Arrest in General Wards: A Multicenter Staggered-Implementation Study in Secondary Hospitals in Korea.

Diagnostics (Basel, Switzerland)·2026
Same author

Highly Flexible and Conformable ZnO/FeGa Magnetoelectric Heterostructures for Skin wound Healing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Effect of Optical Coherence Tomography Angiography Image Artifacts on Quantitation of Vessel Density and Perfusion.

Ophthalmic surgery, lasers & imaging retina·2026
Same author

Correction: Solar-powered bioelectrochemical system for efficient cadmium remediation and recovery of reusable solids.

RSC advances·2026

Related Experiment Video

Updated: Apr 28, 2026

Spatiotemporal Control of Protein Activity through Optogenetic Allosteric Regulation
08:00

Spatiotemporal Control of Protein Activity through Optogenetic Allosteric Regulation

Published on: October 4, 2024

1.2K

Simulating Protein Dynamics in Cell Signaling Pathways: A Mathematical Model Approach Incorporating Negative

Minsoo Kim1, Eunjung Kim2

  • 1Department of Electronics Communications Engineering, Major of Data Science, Korea Maritime & Ocean University, Busan, South Korea.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new mathematical model for cell signaling pathways that includes negative interactions, improving predictions of protein dynamics and compensatory responses. The model enhances understanding of targeted and combination therapies by simulating complex signaling behaviors.

Keywords:
extended Boolean networknegative interactionstochastic differential equationssynergistic combination effect

More Related Videos

Mimicking the Function of Signaling Proteins: Toward Artificial Signal Transduction Therapy
12:24

Mimicking the Function of Signaling Proteins: Toward Artificial Signal Transduction Therapy

Published on: September 29, 2016

6.4K
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

Related Experiment Videos

Last Updated: Apr 28, 2026

Spatiotemporal Control of Protein Activity through Optogenetic Allosteric Regulation
08:00

Spatiotemporal Control of Protein Activity through Optogenetic Allosteric Regulation

Published on: October 4, 2024

1.2K
Mimicking the Function of Signaling Proteins: Toward Artificial Signal Transduction Therapy
12:24

Mimicking the Function of Signaling Proteins: Toward Artificial Signal Transduction Therapy

Published on: September 29, 2016

6.4K
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

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Conventional cell signaling models often fail to capture complex biological phenomena like compensatory responses and oscillatory dynamics due to their focus on positive interactions.
  • Inhibitory interactions and feedback loops are crucial for biological realism in cell signaling pathway dynamics.
  • Understanding these dynamics is vital for developing effective targeted and combination therapies.

Purpose of the Study:

  • To develop an improved mathematical model for cell signaling pathways that incorporates negative interaction mechanisms.
  • To simulate and analyze protein dynamics in response to inhibition and gain-of-function mutations.
  • To evaluate the efficacy of combination therapies using the Bliss Independence Index.

Main Methods:

  • Utilized stochastic differential equations and the Euler-Maruyama method for simulations.
  • Incorporated a sign-changing characteristic function to model inhibitory interactions.
  • Employed a hyperbolic-tangent transfer function to ensure biologically plausible protein activity saturation.
  • Applied the Bliss Independence Index to assess synergistic effects of combination therapies.

Main Results:

  • The improved model accurately reproduces compensatory responses, such as increased SOS, RAS, and RAF activity upon MEK1/2 inhibition.
  • Demonstrated the potential for synergistic effects in combination therapies targeting multiple signaling pathways.
  • Investigated the impact of gain-of-function mutations on signaling balance and downstream protein activation.
  • The model successfully simulates signaling dynamics with inhibitory crosstalk, offering mechanistic insights.

Conclusions:

  • The enhanced mathematical model provides a more realistic representation of cell signaling dynamics by including negative interactions.
  • This model is a valuable tool for generating hypotheses for targeted and combination therapies in diseases driven by aberrant cell signaling.
  • The findings highlight the importance of considering inhibitory crosstalk for accurate pathway modeling and therapeutic strategy development.