Computational design of phage cocktails based on phage-bacteria infection networks

Area of Science:

• Computational biology
• Microbiology
• Bioinformatics

Background:

• Antibiotic misuse drives multidrug-resistant (MDR) bacteria, posing a significant public health threat.
• Phage therapy, using bacteriophages to target bacteria, offers a potential alternative to antibiotics for MDR infections.
• Phage cocktails, combinations of phages, are crucial for effective phage therapy against diverse bacterial strains.

Purpose of the Study:

• To develop and present two novel computational methods for designing effective phage cocktails.
• To integrate these methods into a user-friendly Cytoscape application for broader accessibility.
• To experimentally validate the biological efficacy and computational efficiency of the developed methods.

Main Methods:

• Exhaustive Search: An algorithm guaranteeing the optimal phage cocktail composition.
• Network Metrics: A computationally efficient algorithm for rapid phage cocktail design.
• Cytoscape application: A platform integrating both methods for practical use.

Main Results:

• The Exhaustive Search method consistently identifies the most effective phage cocktails.
• The Network Metrics method provides highly efficient, near-optimal solutions in milliseconds.
• Experimental validation confirmed the biological quality and performance of cocktails designed by both methods.

Conclusions:

• The developed computational methods offer powerful tools for designing targeted phage cocktails.
• These methods can significantly advance the application of phage therapy against MDR bacterial infections.
• The integrated Cytoscape application democratizes access to advanced phage cocktail design tools.