title: The habits of highly effective phages: population dynamics as a framework for identifying therapeutic phages.
abstract: The use of bacteriophages as antibacterial agents is being actively researched on a global scale. Typically, the phages used are isolated from the wild by plating on the bacteria of interest, and a far larger set of candidate phages is often available than can be used in any application. When an excess of phages is available, how should the best phages be identified? Here we consider phage-bacterial population dynamics as a basis for evaluating and predicting phage success. A central question is whether the innate dynamical properties of phages are the determinants of success, or instead, whether extrinsic, indirect effects can be responsible. We address the dynamical perspective, motivated in part by the absence of dynamics in previously suggested principles of phage therapy. Current mathematical models of bacterial-phage dynamics do not capture the realities of in vivo dynamics, nor is this likely to change, but they do give insight to qualitative properties that may be generalizable. In particular, phage adsorption rate may be critical to treatment success, so understanding the effects of the in vivo environment on host availability may allow prediction of useful phages prior to in vivo experimentation. Principles for predicting efficacy may be derived by developing a greater understanding of the in vivo system, or such principles could be determined empirically by comparing phages with known differences in their dynamic properties. The comparative approach promises to be a powerful method of discovering the key to phage success. We offer five recommendations for future study: (i) compare phages differing in treatment efficacy to identify the phage properties associated with success, (ii) assay dynamics in vivo, (iii) understand mechanisms of bacterial escape from phages, (iv) test phages in model infections that are relevant to the intended clinical applications, and (v) develop new classes of models for phage growth in spatially heterogeneous environments.
authors.affiliation: Institute for Cellular and Molecular Biology, University of Texas, Austin, TX USA ; Center for Computational Biology and Bioinformatics, University of Texas, Austin, TX USA ; Department of Integrative Biology, University of Texas, Austin, TX USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: Bull
authors.affiliation: Department of Animal Science, Texas A&M University, College Station, TX USA ; Center for Phage Technology, Texas A&M University, College Station, TX USA.
authors.forename: Jason J
authors.initials: JJ
authors.lastname: Gill
keywords: bacterial infections; bacteriophage; mathematical model; phage therapy; population dynamics