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
In 1968, phycologist M.R. Droop published his famous discovery on the functional relationship between growth rate and internal nutrient status of algae in chemostat culture. The simple notion that growth is directly dependent on intracellular nutrient concentration is useful for understanding the dynamics in many ecological systems. The cell quota in particular lends itself to ecological stoichiometry, which is a powerful framework for mathematical ecology. Three models are developed based on the cell quota principal in order to demonstrate its applications beyond chemostat culture.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
ContributorsPacker, Aaron (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Smith, Hal (Committee member) / Kostelich, Eric (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
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
Cancer is a disease involving abnormal growth of cells. Its growth dynamics is perplexing. Mathematical modeling is a way to shed light on this progress and its medical treatments. This dissertation is to study cancer invasion in time and space using a mathematical approach. Chapter 1 presents a detailed review of literature on cancer modeling.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
ContributorsHan, Lifeng (Author) / Kuang, Yang (Thesis advisor) / Fricks, John (Thesis advisor) / Kostelich, Eric (Committee member) / Baer, Steve (Committee member) / Gumel, Abba (Committee member) / Arizona State University (Publisher)
Created2020