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Signal Transduction: Overview

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Cells respond to many types of information, often through receptor proteins positioned on the membrane. They respond to chemical signals, such as hormones, neurotransmitters, and other signaling molecules, initiating a series of molecular reactions to produce an appropriate response. This is called signal transduction. Cells also coordinate different responses elicited by the same signaling molecule via mediators, allowing molecular cross-talk.
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Related Experiment Video

Updated: Apr 5, 2026

A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells

A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells

Published on: July 3, 2013

Studying Cellular Signal Transduction with OMIC Technologies.

Benjamin D Landry¹, David C Clarke², Michael J Lee³

¹Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.

Journal of Molecular Biology

|August 6, 2015

Summary

Systems and omics approaches are vital for understanding complex protein signal transduction. Integrating experimental and computational methods is key for novel insights into signaling biology.

Keywords:

OMIC technologies data-driven modeling signal transduction systems biology

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

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

Published on: September 29, 2016

Area of Science:

Cellular Biology
Systems Biology
Biochemistry

Background:

Protein signal transduction systems bridge genotype and phenotype, responding to diverse cues with complexity.
Signaling networks are dynamic, non-linear, and context-dependent, necessitating advanced study approaches.

Purpose of the Study:

To review common omics and modeling approaches for studying signal transduction.
To emphasize considerations for effectively merging experimental and computational methods.

Main Methods:

Systems and omics approaches are essential for analyzing complex signaling.
Signals can be encoded in various forms, including protein interactions, enzyme activities, localization, and post-translational modifications.
Dynamic features like duration and rates of change also encode signals.

Main Results:

Integrating multiple experimental and computational approaches is crucial for systems-level signaling analysis.
The non-triviality of integrating these analyses has become apparent.
Successful omics studies require careful consideration of both experimental design and modeling approaches.

Conclusions:

Merging omics and modeling requires careful planning for reliable and novel insights.
Effective integration maximizes the probability of understanding signaling biology.
Consideration of both experimental and computational strategies is critical from the project's outset.