neo4jsbml

neo4jsbml imports Systems Biology Markup Language (SBML) models into Neo4j graph databases to represent biological entities and interactions as nodes and relationships for graph-based analysis.


Key Features:

  • Integration with Neo4j: Maps SBML model components to Neo4j graph elements, representing biological entities as nodes and interactions as relationships.
  • Graphical Data Representation: Translates hierarchical SBML organization into a graph format to reflect interdependencies within models.
  • User-Defined Schema: Supports user-defined schemas to control which SBML elements are imported into the Neo4j database.
  • Cypher Query Support: Enables traversal and analysis of imported models using Neo4j's Cypher query language.
  • Selective Loading and Performance: Allows selective import of relevant data from SBML to optimize database content and performance.

Scientific Applications:

  • Metabolic model analysis: Enables representation and analysis of metabolic models as graphs for exploration of reactions, metabolites, and their relationships.
  • Model sharing and interoperability: Facilitates exchange of SBML-encoded models by converting them into Neo4j graph representations that preserve structure and relationships.
  • Network visualization and traversal: Supports visualization-ready graph representations and graph traversal analyses of complex biological networks.

Methodology:

Imports SBML into Neo4j, maps SBML entities to nodes and interactions to relationships, translates SBML hierarchical structure into a graph format, supports user-defined schemas for selective loading, and enables traversal and analysis via Cypher.

Topics

Details

Cost:
Free of charge
Tool Type:
library
Programming Languages:
Python
Added:
5/14/2024
Last Updated:
5/14/2024

Operations

Publications

Gricourt G, Duigou T, Dérozier S, Faulon J. neo4jsbml: import systems biology markup language data into the graph database Neo4j. PeerJ. 2024;12:e16726. doi:10.7717/peerj.16726. PMID:38250720. PMCID:PMC10798154.

PMID: 38250720
Funding: - A French government grant managed by the Agence Nationale de la Recherche under the France 2030 program: ANR-22-PEBB-0008