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The PathLinker app: Connect the dots in protein interaction networks.
Daniel P Gil1, Jeffrey N Law2, T M Murali1,3
1Department of Computer Science, Virginia Tech, Blacksburg, USA.
F1000Research
|April 18, 2017
Summary
PathLinker reconstructs signaling pathways by finding short interaction paths in protein networks. This graph-theoretic algorithm aids in understanding complex biological networks and drug perturbations, complementing manual pathway curation.
Area of Science:
- Systems Biology
- Bioinformatics
- Network Analysis
Background:
- Signaling pathway reconstruction is crucial for understanding cellular processes.
- Manual curation of pathways is time-consuming and labor-intensive.
- Computational methods can accelerate pathway analysis.
Purpose of the Study:
- To present PathLinker, a graph-theoretic algorithm for reconstructing signaling pathways.
- To provide a Cytoscape application for PathLinker functionality.
- To demonstrate PathLinker's utility in analyzing drug-perturbed networks.
Main Methods:
- PathLinker algorithm identifies multiple short paths between sources (receptors) and targets (transcription factors) in a background protein interaction network.
- The method is implemented as a Cytoscape application for user accessibility.
- Network analysis of proteins perturbed by lovastatin was performed using PathLinker.
Main Results:
- PathLinker efficiently computes interaction paths within large networks.
- The Cytoscape app facilitates the application of PathLinker to biological network reconstruction.
- Analysis of lovastatin-perturbed proteins revealed key interaction networks.
Conclusions:
- PathLinker offers an efficient computational approach to signaling pathway reconstruction.
- The Cytoscape application enhances the accessibility and usability of PathLinker for researchers.
- PathLinker is a valuable tool for analyzing complex biological networks and identifying drug targets.