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A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions

Overview
Journal Front Microbiol
Specialty Microbiology
Date 2021 Oct 8
PMID 34621252
Citations 11
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Abstract

Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the 'post antibiotic era.' Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in versus environments. Realistic computer or mathematical models of these complex environments are a potential route to improve the predictive power of studies for the environment, and to streamline lab work. Here, we introduce and review the current status of mathematical modeling and highlight that data on genetic heterogeneity and mutational stochasticity, time delays and population densities could be critical in the development of realistic phage therapy models in the future. With this in mind, we aim to inform and encourage the collaboration and sharing of knowledge and expertise between microbiologists and theoretical modelers, synergising skills and smoothing the road to regulatory approval and widespread use of phage therapy.

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References
1.
Keen E, Dantas G . Close Encounters of Three Kinds: Bacteriophages, Commensal Bacteria, and Host Immunity. Trends Microbiol. 2018; 26(11):943-954. PMC: 6436384. DOI: 10.1016/j.tim.2018.05.009. View

2.
Lashari A, Trapman P . Branching process approach for epidemics in dynamic partnership network. J Math Biol. 2017; 76(1-2):265-294. PMC: 5754507. DOI: 10.1007/s00285-017-1147-0. View

3.
Cieslewicz M, Vimr E . Reduced polysialic acid capsule expression in Escherichia coli K1 mutants with chromosomal defects in kpsF. Mol Microbiol. 1998; 26(2):237-49. DOI: 10.1046/j.1365-2958.1997.5651942.x. View

4.
Payne R, Phil D, Jansen V . Phage therapy: the peculiar kinetics of self-replicating pharmaceuticals. Clin Pharmacol Ther. 2000; 68(3):225-30. DOI: 10.1067/mcp.2000.109520. View

5.
Sturm A, Heinemann M, Arnoldini M, Benecke A, Ackermann M, Benz M . The cost of virulence: retarded growth of Salmonella Typhimurium cells expressing type III secretion system 1. PLoS Pathog. 2011; 7(7):e1002143. PMC: 3145796. DOI: 10.1371/journal.ppat.1002143. View