Modeling bacteriophage-induced inactivation of Escherichia coli utilizing particle aggregation kinetics.

TitleModeling bacteriophage-induced inactivation of Escherichia coli utilizing particle aggregation kinetics.
Publication TypeJournal Article
Year of Publication2020
AuthorsE Hicks, MR Wiesner, and CK Gunsch
JournalWater Research
Volume171
Start Page115438
Date Published03/2020
Abstract

Targeted inactivation of bacteria using bacteriophages has been proposed in applications ranging from bioengineering and biofuel production to medical treatments. The ability to differentiate between desirable and undesirable organisms, such as in targeting filamentous bacteria in activated sludge, is a potential advantage over conventional disinfectants. Like conventional disinfectants, bacteriophages exhibit non-linear concentration-time (Ct) dynamics in achieving bacterial inactivation. However, there is currently no workable model for predicting these observed non-linear inactivation rates. This work considers an approach to predicting bacteriophage-induced inactivation rates by utilizing classical particle aggregation theory. Bacteriophage-bacteria interactions are represented as a two-step process of transport by Brownian motion, differential settling, and shear, followed by attachment. Modifying classical expressions for particle-particle aggregation to include bacterial growth, death, and bacteriophage reproduction, the model was calibrated and validated using literature data. The calibrated model captures much of the observed non-linearity in inactivation rates and reasonably predicts the final host concentration. This model was shown to be most useful in systems more likely to reflect an industrial setting, where the initial multiplicity of infection, or MOI (the ratio of bacteriophage to host organisms), was 1 or greater. For systems of an initial MOI of less than 1 the model showed increased sensitivity to changes in input parameters and a less pronounced ability to reasonably predict inactivation rates.

DOI10.1016/j.watres.2019.115438
Short TitleWater Research