MetaNetX

MetaNetX integrates and reconciles genome-scale metabolic networks, chemical compounds, reactions, cellular compartments, and proteins into the MNXref common namespace to enable standardized analysis and simulation of GSMNs and biochemical pathways.

Key Features:

• MNXref repository: Integrates chemical compounds, reactions, cellular compartments, and proteins into a common namespace for cross-resource reconciliation.
• Model mapping: Maps user-provided genome-scale metabolic networks (GSMNs) into the MNXref namespace and provides diagnostic messages about mapping issues.
• Pre-compiled reconciliations: Supplies reconciliations of major biochemical databases to harmonize identifiers across resources.
• Data holdings: Provides access to hundreds of GSMNs and biochemical pathways for comparative and analytical work.
• Network comparison: Enables comparison of multiple networks or pathways to identify shared and unique components.
• Dead-end detection: Detects dead-end metabolites and reactions within metabolic networks.
• Flux balance analysis: Performs flux balance analysis (FBA) on metabolic models.
• Knockout simulation: Simulates reaction and gene knockouts to assess impacts on network function.
• Data manipulation and export: Supports manipulation of model data and export of results in standardized formats.
• Model property preservation: Implements reconciliation procedures that preserve intrinsic properties of GSMN models.
• SPARQL endpoint: Exposes a SPARQL endpoint for programmatic queries of the database and online services.

Scientific Applications:

• Identifier reconciliation: Harmonizes identifiers across biochemical databases to support integrated analyses.
• Model reconstruction and curation: Accelerates development and refinement of high-quality GSMN reconstructions.
• Comparative pathway analysis: Facilitates comparison of metabolic pathways and networks across organisms or conditions.
• Constraint-based prediction: Uses flux balance analysis to predict metabolic flux distributions and phenotypes.
• Genetic and reaction perturbation studies: Assesses effects of gene and reaction knockouts on network viability and function.
• Programmatic data integration: Enables automated queries and integration of metabolic data via SPARQL for downstream analyses.

Methodology:

MNXref reconciles and maps compounds, reactions, compartments, and proteins into a common namespace; pre-compiles reconciliations of major biochemical databases; maps user GSMNs to MNXref with diagnostic messages; detects dead-end metabolites and reactions; performs flux balance analysis and reaction/gene knockout simulations; and exposes a SPARQL endpoint for queries.