Degree Type

Dissertation

Date of Award

2008

Degree Name

Doctor of Philosophy

Department

Theses & dissertations (Interdisciplinary)

Major

Bioinformatics and Computational Biology

First Advisor

Karin Dorman

Second Advisor

Douglas Jones

Third Advisor

Alicia Carriquiry

Abstract

Computer simulations of infectious disease allow for the identification and estimation of important pathogen and immune parameters, the validation of theoretical biological models with experimental data, and the characterization of the host-pathogen interactions that lead to emergent and sometimes counterintuitive behavior. This thesis describes the development, analysis, and calibration of a computer model of Leishmania major infection, the identification of correlates of escape mutant success and optimal escape strategies in a computer model of a viral infection, and statistical software to aid in computer model analysis and calibration.;In an agent-based model of L. major infection, sensitivity analysis reveals that increasing growth rates can favor or suppress parasite load, depending on the stage of the infection and the ability of the pathogen to avoid detection. Calibration of the computer model suggests that the pathogen has a relatively slow growth rate and can grow for an extended time before damaging the host cell.;In a computer model of viral infection, we find that the relative overall importance of the cellular (or humoral) response consistently correlates with both the success of immune escape and the optimal escape strategy, and that correlation is relatively robust to the time the escape mutant arises. Mutants that simultaneously escape both responses perform substantially better than humoral or cellular escape mutants alone, highlighting the importance of both responses in controlling infection. Interestingly, loss of infectiousness of humoral escape mutants favors the virus, likely because decreasing infectivity weakens the cellular response.;Finally, Gaussian processes (GP) are commonly used as fast predictors of computer model output and are an essential part of the calibration and analysis of time-consuming computer models. We describe the R package mlegp, which fits GPs to scalar or multivariate computer model output and performs sensitivity analysis to identify and characterize the effects of important model parameters.

DOI

https://doi.org/10.31274/rtd-180813-17036

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Garrett Marc Dancik

Language

en

Proquest ID

AAI3316191

OCLC Number

270962385

ISBN

9780549688259

File Format

application/pdf

File Size

160 pages

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