Author ORCID Identifier

https://orcid.org/0000-0002-0014-9329

Defense Date

2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Systems Modeling and Analysis

First Advisor

Angela Reynolds

Abstract

Lung insults, such as respiratory infections and lung injuries, can damage the pulmonary epithelium, with the most severe cases needing mechanical ventilation for effective breathing and survival. Furthermore, despite the benefits of mechanical ventilators, prolonged or misuse of ventilators may lead to ventilation-associated/ventilation-induced lung injury (VILI). Damaged epithelial cells within the alveoli trigger a local immune response. A key immune cell is the macrophage, which can differentiate into a spectrum of phenotypes ranging from pro- to anti-inflammatory. To gain a greater understanding of the mechanisms of the immune response in the lungs and possible outcomes, we developed several mathematical models of interactions between immune system components and site of damage while accounting for macrophage polarization. We analyzed these models to highlight the parameters and corresponding biological mechanisms that drive outcome and to make predictions about lung health.

We developed a set of ordinary differential equations (ODEs) to model VILI and utilized parameter sampling to evaluate how baseline immune state and lung health, as well as response to tissue damage, affect post-ventilation outcomes. We used a variety of methods to analyze the resulting parameter sets, transients, and outcomes. Analysis showed that parameters and properties of transients related to epithelial repair and M1 activation are important factors. We then used this collection of parameter sets to generate synthetic data and developed algorithms that utilize this collection to predict lung health outcomes based on early time-point data. Our results were comparable to logistic regression and random forest classification methods, and we performed several case studies to highlight how our methods can be used.

Finally, we used different modeling techniques, ODE modeling and agent-based modeling (ABM), to simulate the spectrum of macrophage activation to general pro- and anti-inflammatory stimuli on an individual cell level. The ODE model includes two hallmark pro- and anti-inflammatory signaling pathways and the ABM incorporates similar M1-M2 rules but in a spatio-temporal platform. We then performed simulations with various initial conditions to replicate different experimental setups. Comparing the two models' results sheds light on the important features of each modeling approach. In the future, when more data is available, these features can be considered when choosing techniques to best fit the needs of the modeler and application.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

5-13-2021

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