FAME

FAME performs creation, editing, execution, and analysis of stoichiometric and genome-scale metabolic models to support systems biology and metabolic pathway investigation.

Key Features:

• Stoichiometric model workflow: Creation, editing, execution, and analysis of stoichiometric models are integrated into a single environment.
• Genome-scale model support: Management and analysis capabilities target genome-scale metabolic models.
• Linear solving (PySCeS-CBM): Leverages the Python-based PySCeS-CBM framework to perform linear solving for metabolic modeling tasks.
• Pathway visualization: Automatically superimposes analysis results onto KEGG-like pathway maps for interpretation of metabolic pathways and interactions.
• Implementation: Core implementation components include PHP.

Scientific Applications:

• Systems biology modeling: Construction and analysis of stoichiometric models to study metabolic network behavior in systems biology.
• Genome-scale metabolic analysis: Simulation and analysis of genome-scale metabolic networks to investigate metabolic capabilities and interactions.
• Metabolic pathway interpretation: Mapping model analysis results onto KEGG-like maps to interpret pathway-level changes and interactions.

Methodology:

Implemented in PHP and leveraging the Python-based PySCeS-CBM framework for linear solving, with automatic superposition of analysis results onto KEGG-like pathway maps.