CASTLE

CASTLE identifies synthetic lethal interactions within genome-scale metabolic networks to predict single, double, and triple gene and reaction perturbations that can serve as combinatorial drug targets across pathogenic organisms.

Key Features:

• Genome-scale metabolic reconstructions: Includes reconstructed metabolic networks for over 130 pathogenic organisms for systemic analysis.
• Constraint-based modeling (FBA): Uses flux balance analysis (FBA) as a constraint-based approach to predict single and combinatorial perturbation effects.
• Fast-SL algorithm: Employs the Fast-SL algorithm to rapidly enumerate synthetic lethal interactions from metabolic networks.
• Synthetic lethal predictions (single/double/triple): Predicts sets of genes and reactions whose simultaneous perturbation results in lethality while individual disruptions do not.
• Enumerative capability: Generates comprehensive enumerations of possible synthetic lethal interactions from the metabolic reconstructions.
• Applicability to other models: Methodology is adaptable to additional pathogenic models beyond those included.

Scientific Applications:

• Drug discovery and development: Identifies novel therapeutic targets based on synthetic lethality predictions.
• Combinatorial target identification: Supports discovery of single, double, and triple gene/reaction target combinations for therapeutic strategies.
• Addressing drug resistance: Aids development of strategies against pathogens that have developed resistance to existing drugs.

Methodology:

Reconstructed genome-scale metabolic networks were analyzed using constraint-based approaches such as flux balance analysis (FBA) and the Fast-SL algorithm to enumerate single, double, and triple synthetic lethal gene and reaction sets.