centrality
Definition
Centrality is a set of network metrics that quantify the importance or influence of individual nodes within a biological network. Different centrality measures capture distinct aspects of importance: degree centrality counts direct connections, betweenness centrality identifies nodes that bridge different network regions, closeness centrality measures average distance to all other nodes, and eigenvector centrality weights connections by the importance of neighbors. In biological networks, high-centrality nodes often represent essential genes, hub proteins, or key regulatory molecules whose disruption significantly impacts system function. Understanding centrality helps researchers identify therapeutic targets, predict gene essentiality, and uncover critical control points in cellular pathways.
Visualize centrality in Nodes Bio
Nodes Bio enables researchers to calculate and visualize multiple centrality metrics simultaneously, using color gradients or node sizing to highlight influential molecules. Users can filter networks to display only high-centrality nodes, compare centrality patterns across different conditions or disease states, and identify potential drug targets by finding nodes with high betweenness that control information flow between pathways.
Visualization Ideas:
- Protein-protein interaction networks with node size proportional to betweenness centrality
- Gene regulatory networks colored by eigenvector centrality to show transcriptional hierarchy
- Metabolic pathway networks highlighting high-closeness enzymes as potential drug targets
Example Use Case
A cancer researcher studying resistance mechanisms in melanoma builds a protein-protein interaction network from proteomic data. By calculating betweenness centrality, they identify a kinase with unexpectedly high scores despite moderate expression levels. This kinase bridges multiple survival pathways and proves essential for maintaining the resistant phenotype. Further investigation reveals it as a novel therapeutic target, with its inhibition synergizing with existing treatments by simultaneously disrupting multiple compensatory mechanisms that tumors use to evade therapy.