Banuelos, Selenne

Horn, Mary Ann

Huang, Qimin

Luong, Tiffany

Roach, Dwayne

Ryu, Hwayeon

Segal, Rebecca

Sue, Andrew

Yu, Zhiyuan

English

ISU ReD: Research and eData

Presentation for the Symposium on BEER November 2023Modeling multiphage-bacteria kinetics to predictphage therapy outcomesRebecca Segal1,*, Zhiyuan Yu2, Tiffany Luong3, Selenne Banuelos4, Andrew Su3, MaryAnn Horn5, Hwayeon Ryu6, Qimin Huang7, Dwayne Roach31Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 232842Department of Bioinformatics, University of Michigan, Ann Arbor, MI 481093Department of Biology, San Diego State University, San Diego, CA 921824Department of Mathematics, California State University Channel Islands, Camarillo, CA 930305Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland,OH 441066Department of Mathematics and Statistics, Elon University, Elon, NC 272447Department of Mathematical & Computational Sciences, The College of Wooster, Wooster, OH 44691rasegal@vcu.eduAntibiotic resistant bacteria infections result in tens of thousands of deaths each year. Although several al-ternative therapies are currently under investigation, bacteriophage (phage) cocktail therapy appears poisedfor long-term success. Here, we investigate potency and longevity of individual Pseudomonas aeruginosaphages in cocktail to determine viral co-factors that promote optimal treatment efficacy. We combined bothin vitro and in silico phage-bacteria interaction models to explore treatment outcomes for various combi-nations of treatment. Three phages that adsorb symmetrically and asymmetrically were modeled to beadministered singly, double simultaneously, or double sequentially. We showed that simultaneously admin-istering two asymmetrically binding phages, each with high cell lysis efficiencies, improved overall cocktailpotency. The combined higher lysis potency and reduction in the net probability of independent gene muta-tions was correlated with prolonged and greater bacterial suppression. Nevertheless, we observe evolution ofmultiphage resistance in all two-phage cocktails in vitro. Our mathematical model framework identified themost sensitive parameters for phage selection when exploring the different treatment regimens. Collectively,our findings attempt to dissect the mechanisms of phage cocktails com-bating P. aeruginosa infections andhighlight the viral co-factors necessary for treatment efficacy.Keywords: Differential equation model, bacteriophages, cocktail, phage resistance, sequential, potency, time-kill kinetics, latent period, mutation

Modeling multiphage-bacteria kinetics to predict phage therapy outcomes

https://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1745&context=beer

Modeling multiphage-bacteria kinetics to predict phage therapy outcomes

Abstract

Similar works

Full text

Available Versions

ISU ReD: Research and eData