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Centrality and the shortest path approach in the human interactome
Natalia Rubanova1,2,3, Nadya Morozova1,4,5
1Institut des Hautes Etudes Scientiques, Le Bois-Marie 35 rte de Chartres, Bures-sur-Yvette 91440, France.
Journal of Bioinformatics and Computational Biology
|October 17, 2019
Summary
Shortest paths in human molecular networks often differ from biologically meaningful pathways, especially for longer interactions. However, network centrality can effectively identify key proteins within these important molecular paths.
Area of Science:
- Systems Biology
- Bioinformatics
- Network Analysis
- Graph Theory
Background:
- Network analysis, including shortest path algorithms, is crucial in systems biology.
- Understanding molecular pathways requires analyzing interactions within biological networks.
- Human molecular interaction networks provide a framework for studying cellular processes.
Purpose of the Study:
- To investigate the relationship between shortest paths and biologically meaningful molecular paths in human interaction networks.
- To determine if shortest paths accurately represent canonical signaling pathways.
- To assess the utility of network centrality in identifying key components of molecular pathways.
Main Methods:
- Analysis of shortest paths in human interactome data (HPRD, HIPPIE).
- Comparison of shortest paths with canonical paths in eight KEGG signaling pathways.
- Evaluation of vertex and path centrality within shortest path-induced subnetworks.
Main Results:
- Shortest paths only match canonical paths of length 2-3 interactions.
- Longer canonical paths are matched by shortest paths with shortcuts or protein complex substitutions.
- High centrality vertices are frequently part of canonical paths (up to 80% accuracy).
Conclusions:
- Shortest path analysis alone is insufficient for representing longer biological pathways.
- Network centrality is a valuable metric for identifying functionally important proteins in molecular pathways.
- This study refines the application of graph theory concepts in systems biology research.
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