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Updated: Mar 31, 2026

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Reconstruction of Protein Networks Using Reverse-Phase Protein Array Data.

Silvia von der Heyde^1,2, Johanna Sonntag³, Frank Kramer⁴

¹Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany. v.d.heyde@indivutest.com.

Methods in Molecular Biology (Clifton, N.J.)

|November 1, 2015

Summary

This summary is machine-generated.

This study presents a method to reconstruct cellular signaling networks using reverse-phase protein arrays (RPPA) and dynamic deterministic effects propagation networks (ddepn). The approach integrates prior knowledge for accurate network analysis.

Keywords:

Boolean mode ling DDEPN Network reconstruction Protein signaling Proteomics Reverse-phase protein arrays

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Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

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Published on: August 2, 2015

Area of Science:

Systems Biology
Computational Biology
Molecular Biology

Background:

Cellular signaling networks are crucial for understanding biological processes.
Reconstructing these networks aids in deciphering complex cellular functions.
Existing methods may not fully leverage perturbation data and prior knowledge.

Purpose of the Study:

To describe a novel computational approach for reconstructing cellular signaling networks.
To utilize reverse-phase protein arrays (RPPA) for protein activation measurements.
To integrate prior signaling pathway knowledge for enhanced network inference.

Main Methods:

Employing reverse-phase protein arrays (RPPA) to measure protein and phosphoprotein activation across multiple samples.
Modeling protein interactions using a Boolean network approach, specifically dynamic deterministic effects propagation networks (ddepn).
Utilizing time-course perturbation data and integrating prior knowledge via the rBiopaxParser software for network reconstruction.

Main Results:

Demonstrated a practical application of the ddepn method for network reconstruction.
Provided a framework for interpreting the results of the reconstructed signaling networks.
Showcased the integration of public signaling pathway databases into the network analysis.

Conclusions:

The described approach offers a robust method for cellular signaling network reconstruction.
Open-source R software packages facilitate the practical application of these methods.
Combining RPPA data with ddepn and prior knowledge enhances the accuracy and interpretability of signaling networks.