ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Description
The phycologist, M. R. Droop, studied vitamin B12 limitation in the flagellate Monochrysis lutheri and concluded that its specific growth rate depended on the concentration of the vitamin within the cell; i.e. the cell quota of the vitamin B12. The Droop model provides a mathematical expression to link growth rate to the intracellular concentration of a limiting nutrient. Although the Droop model has been an important modeling tool in ecology, it has only recently been applied to study cancer biology. Cancer cells live in an ecological setting, interacting and competing with normal and other cancerous cells for nutrients and space, and evolving and adapting to their environment. Here, the Droop equation is used to model three cancers.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
ContributorsEverett, Rebecca Anne (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Milner, Fabio (Committee member) / Crook, Sharon (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Arizona State University (Publisher)
Created2015
Description
Infectious diseases have emerged as a significant threat to wildlife. Environmental change is often implicated as an underlying factor driving this emergence. With this recent rise in disease emergence and the acceleration of environmental change, it is important to identify the environmental factors that alter host-pathogen dynamics and their underlying mechanisms. The emerging pathogen Batrachochytrium dendrobatidis (Bd) is a clear example of the negative effects infectious diseases can have on wildlife. Bd is linked to global declines in amphibian diversity and abundance. However, there is considerable variation in population-level responses to Bd, with some hosts experiencing marked declines while others persist. Environmental factors may play a role in this variation. This research used populations of pond-breeding chorus frogs (Pseudacris maculata) in Arizona to test if three rapidly changing environmental factors nitrogen (N), phosphorus (P), and temperature influence the presence, prevalence, and severity of Bd infections. I evaluated the reliability of a new technique for detecting Bd in water samples and combined this technique with animal sampling to monitor Bd in wild chorus frogs. Monitoring from 20 frog populations found high Bd presence and prevalence during breeding. A laboratory experiment found 85% adult mortality as a result of Bd infection; however, estimated chorus frog densities in wild populations increased significantly over two years of sampling despite high Bd prevalence. Presence, prevalence, and severity of Bd infections were not correlated with aqueous concentrations of N or P. There was, however, support for an annual temperature-induced reduction in Bd prevalence in newly metamorphosed larvae. A simple mathematical model suggests that this annual temperature-induced reduction of Bd infections in larvae in combination with rapid host maturation may help chorus frog populations persist despite high adult mortality. These results demonstrate that Bd can persist across a wide range of environmental conditions, providing little support for the influence of N and P on Bd dynamics, and show that water temperature may play an important role in altering Bd dynamics, enabling chorus frogs to persist with this pathogen. These findings demonstrate the importance of environmental context and host life history for the outcome of host-pathogen interactions.
ContributorsHyman, Oliver J. (Author) / Collins, James P. (Thesis advisor) / Davidson, Elizabeth W. (Committee member) / Anderies, John M. (Committee member) / Elser, James J. (Committee member) / Escalante, Ananias (Committee member) / Arizona State University (Publisher)
Created2012
Description
Predicting resistant prostate cancer is critical for lowering medical costs and improving the quality of life of advanced prostate cancer patients. I formulate, compare, and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). I accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). I demonstrate that the inverse problem of parameter estimation might be too complicated and simply relying on data fitting can give incorrect conclusions, since there is a large error in parameter values estimated and parameters might be unidentifiable. I provide confidence intervals to give estimate forecasts using data assimilation via an ensemble Kalman Filter. Using the ensemble Kalman Filter, I perform dual estimation of parameters and state variables to test the prediction accuracy of the models. Finally, I present a novel model with time delay and a delay-dependent parameter. I provide a geometric stability result to study the behavior of this model and show that the inclusion of time delay may improve the accuracy of predictions. Also, I demonstrate with clinical data that the inclusion of the delay-dependent parameter facilitates the identification and estimation of parameters.
ContributorsBaez, Javier (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2017
Description
The complex life cycle and widespread range of infection of Plasmodium parasites, the causal agent of malaria in humans, makes them the perfect organism for the study of various evolutionary mechanisms. In particular, multigene families are considered one of the main sources for genome adaptability and innovation. Within Plasmodium, numerous species- and clade-specific multigene families have major functions in the development and maintenance of infection. Nonetheless, while the evolutionary mechanisms predominant on many species- and clade-specific multigene families have been previously studied, there are far less studies dedicated to analyzing genus common multigene families (GCMFs). I studied the patterns of natural selection and recombination in 90 GCMFs with diverse numbers of gene gain/loss events. I found that the majority of GCMFs are formed by duplications events that predate speciation of mammal Plasmodium species, with many paralogs being neutrally maintained thereafter. In general, multigene families involved in immune evasion and host cell invasion commonly showed signs of positive selection and species-specific gain/loss events; particularly, on Plasmodium species is the simian and rodent clades. A particular multigene family: the merozoite surface protein-7 (msp7) family, is found in all Plasmodium species and has functions related to the erythrocyte invasion. Within Plasmodium vivax, differences in the number of paralogs in this multigene family has been previously explained, at least in part, as potential adaptations to the human host. To investigate this I studied msp7 orthologs in closely related non-human primate parasites where homology was evident. I also estimated paralogs’ evolutionary history and genetic polymorphism. The emerging patterns where compared with those of Plasmodium falciparum. I found that the evolution of the msp7 multigene family is consistent with a Birth-and-Death model where duplications, pseudogenization and gene lost events are common. In order to study additional aspects in the evolution of Plasmodium, I evaluated the trends of long term and short term evolution and the putative effects of vertebrate- host’s immune pressure of gametocytes across various Plasmodium species. Gametocytes, represent the only sexual stage within the Plasmodium life cycle, and are also the transition stages from the vertebrate to the mosquito vector. I found that, while male and female gametocytes showed different levels of immunogenicity, signs of positive selection were not entirely related to the location and presence of immune epitope regions. Overall, these studies further highlight the complex evolutionary patterns observed in Plasmodium.
ContributorsCastillo Siri, Andreina I (Author) / Rosenberg, Michael (Thesis advisor) / Escalante, Ananias (Committee member) / Taylor, Jesse (Committee member) / Collins, James (Committee member) / Arizona State University (Publisher)
Created2016
Description
The representation of a patient’s characteristics as the parameters of a model is a key component in many studies of personalized medicine, where the underlying mathematical models are used to describe, explain, and forecast the course of treatment. In this context, clinical observations form the bridge between the mathematical frameworks and applications. However, the formulation and theoretical studies of the models and the clinical studies are often not completely compatible, which is one of the main obstacles in the application of mathematical models in practice. The goal of my study is to extend a mathematical framework to model prostate cancer based mainly on the concept of cell-quota within an evolutionary framework and to study the relevant aspects for the model to gain useful insights in practice. Specifically, the first aim is to construct a mathematical model that can explain and predict the observed clinical data under various treatment combinations. The second aim is to find a fundamental model structure that can capture the dynamics of cancer progression within a realistic set of data. Finally, relevant clinical aspects such as how the patient's parameters change over the course of treatment and how to incorporate treatment optimization within a framework of uncertainty quantification, will be examined to construct a useful framework in practice.
ContributorsPhan, Tin (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J (Committee member) / Crook, Sharon (Committee member) / Maley, Carlo (Committee member) / Bryce, Alan (Committee member) / Arizona State University (Publisher)
Created2021