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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data
Jason W Locasale1, Alejandro Wolf-Yadlin
1Division of Signal Transduction, Department of Systems Biology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. jlocasal@bidmc.harvard.edu
Plos One
|August 27, 2009
Summary
This study introduces a maximum entropy method to build signaling networks from quantitative proteomics data. The approach accurately infers phosphotyrosine interactions, revealing biological insights into cell signaling pathways.
Area of Science:
- Biochemistry
- Systems Biology
- Computational Biology
Background:
- Quantitative proteomics enables measurement of phosphorylation dynamics in signaling networks.
- Computational methods for inferring signaling networks from proteomics data are underdeveloped.
Purpose of the Study:
- To develop and validate a computational method for inferring phosphorylation-dependent interaction networks from quantitative proteomics data.
- To apply this method to a growth factor-mediated signaling network in human mammary epithelial cells.
Main Methods:
- Utilized the principle of maximum entropy to infer networks from pairwise correlations in mass spectrometry data.
- Validated the method using simulations of a model biochemical signaling network governed by differential equations.
- Analyzed mass spectrometry data from a human mammary epithelial cell line.
Main Results:
- The maximum entropy method accurately detected interactions in simulated signaling systems.
- Inferred network from cell signaling data exhibited a biologically interpretable small-world structure.
- Generated predictions for interactions involving uncharacterized phosphotyrosine sites, including a tumor suppressor pathway.
Conclusions:
- Maximum entropy-derived network models are effective for interpreting quantitative proteomics data.
- This approach provides a powerful tool for dissecting complex cellular signaling networks.
- The method aids in understanding signaling pathways and identifying novel interactions.
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