Medusapy

Medusapy generates and analyzes ensembles of genome-scale metabolic network reconstructions to quantify and explore uncertainty in network structure and parameters.

Key Features:

• Ensemble Generation: Facilitates creation of ensembles from sets of metabolic network models and compresses them into a compact ensemble object for simultaneous management and analysis.
• Experimental Data Integration: Provides functions to incorporate experimental data during ensemble generation to ground reconstructions in empirical evidence.
• Constraint-Based Ensemble Analysis: Extends constraint-based reconstruction and analysis (COBRA) methodologies to enable simulations and analyses across multiple network models.
• Machine Learning Integration: Supports use of machine learning techniques to guide curation and refinement of genome-scale metabolic network reconstructions.
• COBRApy Extension: Extends the capabilities of the COBRApy package to enable efficient ensemble-scale analyses.

Scientific Applications:

• Metabolic engineering: Enables ensemble-based prediction of metabolic behaviors to inform strain design and engineering decisions under model uncertainty.
• Systems biology: Supports system-level studies that require exploration of alternative network structures and parameterizations.
• Model uncertainty analysis: Allows exploration of the range of possible network behaviors arising from uncertainty in structure and parameters.
• Organismal metabolism studies: Facilitates comparison of metabolic capabilities and responses across varied environmental or genetic conditions using ensembles.

Methodology:

Generates ensembles of genome-scale metabolic network reconstructions, performs simulations across these ensembles to evaluate ranges of metabolic behaviors, and integrates machine learning to guide model curation.