CoBAMP

CoBAMP performs pathway analysis for genome-scale constraint-based metabolic models, enabling enumeration and analysis of elementary flux modes (EFMs) and minimal cut sets to study metabolic pathways.

Key Features:

• K-shortest EFM algorithm: Implements the K-shortest EFM algorithm to identify a specified number of shortest elementary flux modes (EFMs).
• EFM enumeration and analysis: Enumerates and analyzes EFMs within constraint-based metabolic models.
• Minimal cut set identification: Identifies minimal cut sets to pinpoint reactions that control or disrupt specific metabolic functions.
• Modular framework: Provides a modular framework for pathway analysis and interoperability with other modeling tools.
• Integration with cobrapy, framed, and cameo: Integrates with cobrapy, framed, and cameo using common constraint-based modeling data structures.
• Python 3 implementation: Implemented in Python 3.
• optlang and MILP optimizer: Uses the optlang framework and requires a mixed-integer linear programming optimizer.
• Genome-scale model support: Applicable to genome-scale constraint-based metabolic models.

Scientific Applications:

• Alternative pathway exploration: Identifies alternative metabolic routes through enumeration of shortest EFMs.
• Network flexibility and robustness analysis: Assesses metabolic network flexibility and robustness under various conditions.
• Reaction control and disruption mapping: Uses minimal cut sets to pinpoint reactions that control or disrupt specific metabolic functions.
• Metabolic engineering target identification: Aids identification of potential targets for metabolic engineering.
• Systems-level metabolic insight: Supports elucidation of underlying mechanisms of metabolism and comprehension of cellular processes at a systems level.

Methodology:

Implements the K-shortest EFM algorithm, enumerates EFMs, identifies minimal cut sets, interfaces with cobrapy/framed/cameo data structures, and uses the optlang framework with a mixed-integer linear programming optimizer.