Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Interactions Between Signaling Pathways

Interactions Between Signaling Pathways

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...

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...

Contact-dependent Signaling

Contact-dependent Signaling

Contact-dependent signaling, as the name suggests, requires that communicating cells be in direct contact with each other. This is achieved either through receptor-ligand interactions or by specialized cytoplasmic channels that allow the flow of small molecules between cells. In animal cells, channels called gap junctions facilitate contact-dependent signaling in certain tissues, whereas, plasmodesmata perform a similar function in plants.
Gap Junctions
In animal cells, gap junctions are formed...

Assembly of Signaling Complexes

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...

SFG Algebra

SFG Algebra

In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...

Overview of Cell Signaling

Overview of Cell Signaling

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...

You might also read

Related Articles

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

Sort by

Same author

Histopathology-inferred spatial transcriptomics characterizes the tumor microenvironment in 1,500 head and neck tumors and predicts clinical outcomes.

bioRxiv : the preprint server for biology·2026

Same author

Trials for Rare Cancers Are More Successful than those for Common Cancers.

ESMO rare cancers·2026

Same author

Liquid surrogates of spatial tumor ecosystems.

Cell research·2026

Same author

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

NPJ precision oncology·2026

Same author

Author Correction: The ubiquitin ligase RNF5 determines acute myeloid leukemia growth and susceptibility to histone deacetylase inhibitors.

Nature communications·2026

Same author

SYNTHESIS-Breast: A prospective early-phase trial of a genetic-interaction- focused computational algorithm in advanced metastatic breast cancer.

Research square·2026

Search research articles

Related Experiment Video

Updated: May 25, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

A linearized constraint-based approach for modeling signaling networks.

Liram Vardi¹, Eytan Ruppin, Roded Sharan

¹The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|February 4, 2012

Summary

This summary is machine-generated.

A new linear constraint-based modeling (CBM) approach offers improved scalability for analyzing complex biological signaling networks. This method enhances computational efficiency and sampling capabilities for studying cellular pathways like the epidermal growth factor receptor (EGFR).

Related Experiment Videos

Last Updated: May 25, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Computational Biology
Systems Biology
Biotechnology

Background:

The increasing volume of biological data necessitates accurate computational models for understanding complex biological phenomena.
Existing constraint-based modeling (CBM) and Boolean modeling approaches face limitations in scalability and sampling for large biological networks due to their reliance on mixed integer linear programming.

Purpose of the Study:

To develop a novel, fully linear constraint-based modeling (CBM) approach that overcomes the scalability and sampling limitations of existing methods.
To demonstrate the utility of this new CBM framework for modeling complex cellular signaling pathways.

Main Methods:

Proposed a novel optimization procedure for constructing a fully linear constraint-based model, avoiding integer variables.
Applied the developed CBM approach to reconstruct a model of the human epidermal growth factor receptor (EGFR) signaling pathway, comprising 322 species and 211 connections.
Compared the model's predictions against experimental phosphorylation data and a Boolean-based EGFR signaling model.

Main Results:

The new linear CBM approach demonstrated significant computational advantages in scalability and sampling utilization.
The reconstructed EGFR pathway model achieved a high prediction accuracy of 75%.
The model's predictions showed high similarity to those inferred by a Boolean-based EGFR signaling model.

Conclusions:

The developed fully linear CBM framework offers a computationally efficient and scalable alternative for modeling biological signaling networks.
This approach holds significant promise for advancing the study of cellular signaling by enabling more comprehensive analysis and sampling of solution spaces.
The high prediction accuracy and similarity to existing models validate the utility and potential of this novel computational framework.