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Interactions Between Signaling Pathways

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Once a ligand binds to a receptor, the signal is transmitted through the membrane and into the cytoplasm. The continuation of a signal in this manner is called signal transduction. Signal transduction only occurs with cell-surface receptors, which cannot interact with most components of the cell, such as DNA. Only internal receptors can interact directly with DNA in the nucleus to initiate protein synthesis. When a ligand binds to its receptor, conformational changes occur that affect the...

Intracellular Signaling Cascades

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Related Experiment Video

Updated: May 26, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA.

Ruiyan Luo¹, Hongyu Zhao

¹Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520, USA.

The Annals of Applied Statistics

|December 14, 2011

Summary

This summary is machine-generated.

This study introduces a Bayesian hierarchical model to infer protein signaling pathways from single-cell data. The method effectively identifies causal relationships between proteins using interventional experiments and accounts for noise.

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Published on: February 16, 2017

Area of Science:

Systems Biology
Computational Biology
Molecular Biology

Background:

Technological advances enable simultaneous measurement of multiple protein activities at the single-cell level.
Single-cell interventional data offers potential for inferring causal relationships among proteins.

Purpose of the Study:

To propose a Bayesian hierarchical modeling framework for inferring signaling pathways from single-cell interventional data.
To model network sparsity and protein associations at overall and experimental levels.
To explicitly account for intrinsic noise and measurement error.

Main Methods:

Bayesian hierarchical modeling framework.
Modeling network sparsity and protein associations.
Markov chain Monte Carlo (MCMC) for statistical inference.
Considering intrinsic noise and measurement error.

Main Results:

The framework infers pairs of proteins with associated causal relationships.
Effectively pools information across different interventional experiments.
Demonstrated effectiveness through simulation studies and real data analysis.

Conclusions:

The proposed Bayesian hierarchical model is effective for inferring protein signaling pathways from single-cell interventional data.
The approach successfully identifies causal protein relationships while accounting for noise and sparsity.