MATHEMATICAL MODELING OF PHAGE-BACTERIA POPULATION DYNAMICS

Author

John Lawrence D. Palacios, Virginia Commonwealth UniversityFollow

DOI

https://doi.org/10.25772/XRXV-9E06

Defense Date

2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Systems Modeling and Analysis

First Advisor

Rebecca Segal

Second Advisor

Angela Reynolds

Third Advisor

Punit Gandhi

Fourth Advisor

Allison Johnson

Abstract

Bacteriophages are viruses that infect and replicate within bacteria. Lytic phages cause the bacterial cell to burst, killing the bacteria. These types of phages can be used to treat patients with antibiotic-resistant bacterial infections. As a step in developing successful treatment protocols, we aim to understand the population dynamics of phages and bacteria using an in vitro model. We model the dynamics using the Campbell model, which consists of a delay differential equation (DDE), as a base model. We extended the model by including the emergence of phage resistance. We then compared the DDE model with a parallel ordinary differential equation (ODE) model. We applied several parameter estimation methods to the ODE model, such as local sensitivity analysis, sensitivity-based identifiability, and a local optimization method to fit experimental data. Finally, we compared the ODE model to the DDE model and found that the ODE model better captures the data, allowing us to move forward with the ODE formulation. We also conducted global sensitivity analyses by computing partial rank correlation coefficients (PRCC), first-order sensitivities, and total-order sensitivities.

In addition to ODE modeling, we used an agent-based model (ABM) to simulate biofilm growth in the presence of phages. The ODE model assumes a well-mixed population, while the ABM incorporates spatial dynamics, which are important in therapeutic settings. The ABM is used as a benchmark to understand a biofilm flow experiment.

This dissertation aims to provide insights into phage-bacteria population dynamics using various modeling techniques to explore the potential of phage therapy. Our models can potentially be extended to demonstrate the efficacy of phages in treating infections caused by cystic fibrosis or infective endocarditis.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

9-6-2024