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Related Concept Videos

Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

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 the...
Signal Transduction: Overview01:26

Signal Transduction: Overview

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.
Typically, signal transduction involves three...
Interactions Between Signaling Pathways01:19

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...
Diversity in Cell Signaling Responses01:22

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...
Assembly of Signaling Complexes01:30

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,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

Updated: Jul 19, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Data-driven modelling of signal-transduction networks.

Kevin A Janes1, Michael B Yaffe

  • 1Cell Decision Processes Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Nature Reviews. Molecular Cell Biology
|October 24, 2006
PubMed
Summary

Data-driven models simplify complex signal-transduction network data from large-scale studies. These computational approaches, including clustering and principal component analysis, are becoming essential for systems biology research.

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Last Updated: Jul 19, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: December 7, 2021

Area of Science:

  • Systems biology
  • Molecular biology
  • Computational biology

Background:

  • Large-scale quantitative studies of signal-transduction networks are enabled by new technologies.
  • Analyzing complex biological data from these studies often exceeds intuitive understanding.

Purpose of the Study:

  • To introduce data-driven modeling as a method for analyzing large biological datasets.
  • To highlight the utility of these models in deriving biological insights from signaling networks.

Main Methods:

  • Clustering
  • Principal Component Analysis (PCA)
  • Partial Least Squares (PLS)

Main Results:

  • Data-driven models simplify complex experimental measurements.
  • These methods facilitate the analysis of large-scale quantitative data.
  • Biological insights can be effectively derived from complex datasets using these approaches.

Conclusions:

  • Data-driven modeling is crucial for understanding complex signal-transduction networks.
  • These computational tools are becoming standard for systems-level research in molecular signaling.