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Updated: May 26, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Modeling signaling networks using high-throughput phospho-proteomics.

Camille Terfve¹, Julio Saez-Rodriguez

¹European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK. terfve@ebi.ac.uk

Advances in Experimental Medicine and Biology

|December 14, 2011

Summary

This summary is machine-generated.

Mathematical modeling aids understanding complex cellular signaling networks. This review covers data acquisition (antibody-based, mass spectrometry) and computational analysis methods for phosphoproteomics data.

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Phosphoproteomic Strategy for Profiling Osmotic Stress Signaling in Arabidopsis

Phosphoproteomic Strategy for Profiling Osmotic Stress Signaling in Arabidopsis

Published on: June 25, 2020

Area of Science:

Systems Biology
Computational Biology
Biochemistry

Background:

Cellular communication relies on complex signaling networks.
Mathematical modeling is crucial for deciphering network complexity.
Phosphoproteomics data provides key insights into signaling pathways.

Purpose of the Study:

To review recent advancements in data acquisition for cellular signaling.
To explore various computational model formalisms for analyzing phosphoproteomics data.
To bridge the gap between experimental data and mechanistic understanding of signaling networks.

Main Methods:

Antibody-based technologies and mass spectrometry (MS) for phosphoproteomics data acquisition.
Review of computational approaches including clustering, data mining, and mechanistic models.
Discussion of rule-based, logic-based, Bayesian network inference, and linear regression models.

Main Results:

Comparison of antibody-based and MS techniques, highlighting their strengths and limitations.
Overview of diverse modeling formalisms applicable to high-throughput phosphoproteomics data.
Demonstration of how different models capture various aspects of signaling network dynamics.

Conclusions:

Accurate data acquisition and appropriate model formalisms are essential for understanding cellular signaling.
A range of computational tools are available for analyzing complex phosphoproteomics datasets.
Integrating experimental data with mathematical models enhances our comprehension of cellular information processing.