ModelSEED

ModelSEED provides an integrated biochemical database and constructs draft genome-scale metabolic models from annotated genomes to support reconstruction, comparison, and simulation of metabolic networks.

Key Features:

• Comprehensive Biochemical Data: ModelSEED's biochemistry database contains 33,978 compounds and 36,645 reactions and encodes compartmentalization, transport reactions, charged species, and proton balancing.
• User-Extensible Database: The database is user-extensible and its data are stored in GitHub for community contributions and versioned updates.
• Integration and Comparison: It functions as a biochemical "Rosetta Stone" and supports ontologies to reconcile differences among annotation sources and metabolic reconstructions.
• Standardized Data Processing: ModelSEED amalgamates chemical data from multiple sources, applies standardized transformations, identifies redundancies, and computes thermodynamic properties for reactions.
• Simulation-Ready Network: The biochemical network is validated using flux balance analysis (FBA) to verify suitability for modeling and simulation of diverse phenotypes.

Scientific Applications:

• Genome-scale metabolic reconstruction: Reconstruction of draft genome-scale metabolic models from annotated genomes using the integrated biochemistry database.
• Comparative metabolic analysis: Comparison of metabolic capabilities across organisms and between different reconstructions using ontology mappings.
• Pathway analysis: Exploration and analysis of metabolic pathways, including stoichiometry and reaction thermodynamics.
• Metabolic engineering target identification: Identification and analysis of potential metabolic engineering targets.
• Evolutionary and strain optimization studies: Studies of evolutionary relationships and optimization of microbial strains for industrial applications.
• Phenotype prediction and simulation: Prediction of phenotypic outcomes under genetic and environmental perturbations using FBA-based simulations.

Methodology:

ModelSEED amalgamates chemical data from multiple sources, applies standardized transformations, identifies redundancies, computes reaction thermodynamic properties, constructs ontologies to reconcile representations, and validates networks with flux balance analysis (FBA).