Determination of the Optimal Bacteriophage Dose to Control Pseudomonas aeruginosa using evolutionary programming and stochastic kinetics

Phage-therapy is a promising alternative against pathogenic, multiple drug resistant bacteria. In this work we propose an algorithm to determine the optimal bacteriophage dose able to minimize a population of Pseudomonas aeruginosa. Reverse engineering was used to determine the kinetic parameters; subsequently, a bi-level optimization platform was implemented for a model based on evolutionary programming. Our prediction of optimal dose was tested in vitro with planktonic cultures of P. aeruginosa. From the data obtained, we conclude that reverse engineering and stochastic simulations are a useful approach to find optimal phage doses against pathogenic bacteria, an important step for the implementation of phage-therapy.