entity-relationship
Definition
Entity-relationship modeling represents biological systems as networks of entities (nodes) connected by defined relationships (edges). Entities can be genes, proteins, metabolites, diseases, or drugs, while relationships describe interactions like binding, regulation, phosphorylation, or association. This framework transforms complex biological data into structured networks that reveal patterns, pathways, and mechanisms. In life sciences, entity-relationship models enable systematic analysis of molecular interactions, disease mechanisms, and drug effects. The approach is fundamental to systems biology, allowing researchers to map how biological components interact across scales—from molecular pathways to cellular processes to organism-level phenotypes.
Visualize entity-relationship in Nodes Bio
Nodes Bio enables researchers to construct and explore entity-relationship networks by mapping biological entities as nodes and their interactions as edges. Users can visualize multi-layered relationships between genes, proteins, diseases, and compounds, applying filters to highlight specific interaction types. The platform supports pathway enrichment analysis and network topology metrics to identify key regulatory hubs, revealing how entities cluster and influence each other within biological systems.
Visualization Ideas:
- Multi-omics integration network showing genes, proteins, and metabolites with typed relationship edges
- Drug-target-disease network with color-coded relationship types (inhibition, activation, association)
- Gene regulatory network displaying transcription factors and target genes with directional regulatory relationships
Example Use Case
A cancer researcher investigating resistance mechanisms to targeted therapy constructs an entity-relationship network in Nodes Bio. Entities include mutated oncogenes, their protein products, downstream signaling molecules, and therapeutic compounds. Relationships capture phosphorylation events, protein-protein interactions, and drug-target bindings. By analyzing this network, the researcher identifies a compensatory pathway activated when the primary target is inhibited, explaining resistance. Network clustering reveals that three proteins form a hub mediating the bypass mechanism, suggesting combination therapy targets.