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The use of phages as antibacterials is becoming more and more common in Western countries. However, a successful phage-derived antibacterial treatment needs to account for additional features such as the loss of infective virions and the multiplication of the hosts. The parameters critical inoculation size (VF) and failure threshold time (TF) have been introduced to assure that the viral dose (Vϕ) and administration time (Tϕ) would lead to the extinction of the targeted bacteria. The problem with the definition of VF and TF is that they are non-linear equations with two unknowns; thus, obtaining their explicit values is cumbersome and not unique. The current study used machine learning to determine VF and TF for an effective antibacterial treatment. Within these ranges, a Pareto optimal solution of a multi-criterial optimization problem (MCOP) provided a pair of Vϕ and Tϕ to facilitate the user’s work. The algorithm was tested on a series of in silico microbial consortia that described the outgrowth of a species at high cell density by another species initially present at low concentration. The results demonstrated that the MCOP-derived pairs of Vϕ and Tϕ could effectively wipe out the bacterial target within the context of the simulation. The present study also introduced the concept of mediated phage therapy, where targeting booster bacteria might decrease the virulence of a pathogen immune to phagial infection and highlighted the importance of microbial competition in attaining a successful antibacterial treatment. In summary, the present work developed a novel method for investigating phage/bacteria interactions that can help increase the effectiveness of the application of phages as antibacterials and ease the work of microbiologists.

No abstract

The rise in multidrug-resistant bacteria has sprung a renewed interest in applying phages as antibacterial, a procedure Western practitioners eventually abandoned due to several downfalls, including poor understanding of the dynamics between phages and bacteria. A successful phage therapy needs to account for the loss of infective virions and the multiplication of the hosts. The parameters critical inoculation size (VF) and failure threshold time (TF) have been introduced to assure that the viral dose (vΦ) and administration time (tΦ) would lead to an effective treatment. The problem with the definition of VF and TF is that they are non-linear equations with two unknowns; thus, their solution is cumbersome and not unique. The current study used machine learning in the form of a decision tree algorithm to determine ranges for the viral dose and administration times required to achieve an effective phage therapy. Within these ranges, a Pareto optimal solution of a multi-criterial optimization problem (MCOP) provides values leading to effective treatment. The algorithm was tested on a series of microbial consortia that described allochthonous invasions (the outgrowing of a species at high cell density by another species initially present at low concentration) to inhibit the growth of the invading species. The present study also introduced the concept of 'mediated phage therapy', where targeting a booster bacteria might decrease the virulence of a pathogen immune to phagial infection. The results demonstrated that the MCOP could provide pairs of vΦ and tΦ that could effectively wipe out the bacterial target from the considered micro-environment. In summary, the present work introduced a novel method for investigating the phage/bacteria interaction that could help increase the effectiveness of phage therapy.

We consider a particle system with uniform coupling between a macroscopic component and individual particles. The constraint for each particle is of full rank, which implies that each movement of the macroscopic component leads to a movement of all particles and vice versa. Skeletal muscle tissues share a similar property which motivates this work.We prove convergence of the mean-field limit, well-posedness and a stability estimate for the mean-field PDE. This work generalises our previous results from [20] to the case of nonlinear constraints.