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

Diversity in Cell Signaling Responses

Diversity in Cell Signaling Responses

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...

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...

What is Cell Signaling?

What is 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 to respond to the environment.

Cell Signaling Feedback Loops

Cell Signaling Feedback Loops

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...

You might also read

Related Articles

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

Sort by

Same author

Quantitative Spatiotemporal Analysis of Intracellular Kinase Activity in Metastatic Breast Cancer Cells Using a Microfluidic-Based Lateral Diffusion Assay.

Analytical chemistry·2026

Same author

Agent-based modeling demonstrates how target-independent processes supplement killing by antibody-drug conjugates in cancer therapy.

PLoS computational biology·2026

Same author

2025 Manuscript Reviewers: A Note of Thanks.

Radiology. Imaging cancer·2026

Same author

Editor's Recognition Awards.

Radiology. Imaging cancer·2026

Same author

On-Scanner Correction of Gradient Nonlinearity Bias for Accurate Assessment of Diffusion Heterogeneity Across Bone Sites in Myelofibrosis Patients.

Magnetic resonance in medicine·2026

Same author

In Vivo Auto-tuning of Antibody-Drug Conjugate Delivery to Maximize Efficacy Using High-Avidity, Low-Affinity Antibodies.

Molecular cancer therapeutics·2025

Search research articles

Related Experiment Video

Updated: Sep 21, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Computational methods for characterizing and learning from heterogeneous cell signaling data.

Patrick C Kinnunen¹, Kathryn E Luker², Gary D Luker^2,3,4

¹Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI, 48109-2800, USA.

Current Opinion in Systems Biology

|June 1, 2022

Summary

This summary is machine-generated.

Computational methods help interpret complex single-cell signaling data. Machine learning and mechanistic models reveal cell heterogeneity, crucial for understanding disease and developing treatments.

More Related Videos

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Published on: February 24, 2023

Related Experiment Videos

Last Updated: Sep 21, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Published on: February 24, 2023

Area of Science:

Cell Biology
Computational Biology
Systems Biology

Background:

Cell signaling heterogeneity is fundamental to biology and disease.
Understanding this heterogeneity is key for disease treatment.
Novel genetic engineering tools generate vast single-cell data.

Purpose of the Study:

To review computational methods for interpreting single-cell signaling data.
To highlight machine learning and mechanistic modeling approaches.
To bridge the gap between data and biological understanding.

Main Methods:

Review of machine learning techniques (dimensionality reduction, classification).
Discussion of integrating mechanistic models with heterogeneous data.
Evaluation of computational approaches for data interpretation.

Main Results:

Machine learning can identify structure in complex, dynamic single-cell data.
Mechanistic models combined with data can explain cell-to-cell differences.
Computational tools simplify the interpretation of signaling heterogeneity.

Conclusions:

Computational methods are essential for extracting meaning from single-cell signaling data.
These techniques facilitate the translation of data into biological insights.
Further development of these methods will advance disease treatment strategies.