SBMLmod

SBMLmod integrates gene expression, proteomics, and metabolomics datasets into Systems Biology Markup Language (SBML) models to enable data-driven modification and steady-state analysis of biochemical network models.

Key Features:

• Supported data types: Accepts gene expression, proteomics, and metabolomics datasets for incorporation into SBML models.
• SBML model integration: Automates incorporation of high-throughput biological data into existing SBML models.
• Steady-state analysis: Interfaces with the COPASIWS web service to perform steady-state analysis following data integration.
• Implementation: Implemented in Python.

Scientific Applications:

• Pathway-specific modeling: Integration of gene expression data from various healthy tissues and cancer datasets into a model of mammalian tryptophan metabolism.
• Data-driven simulation and analysis: Use of integrated datasets and COPASIWS-based steady-state analysis for simulation and hypothesis testing of biochemical networks.

Methodology:

Automated incorporation of gene expression, proteomics, and metabolomics data into SBML models and invocation of the COPASIWS web service to obtain steady-state analysis; implemented in Python.