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
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
Throughout the Southwest, complex geology and physiography concomitant with climatic variability contribute to diverse stream hydrogeomorphologies. Many riparian plant species store their seeds in soil seed banks, and germinate in response to moisture pulses, but the climatic controls of this response are poorly understood. To better understand the ecological implications of a changing climate on riparian plant communities, I investigated seed bank responses to seasonal temperature patterns and to stream hydrogeomorphic type. I asked the following questions: Are there distinct suites of warm and cool temperature germinating species associated with Southwestern streams; how do they differ between riparian and terrestrial zones, and between ephemeral and perennial streams? How does alpha diversity of the soil seed bank differ between streams with ephemeral, intermittent, and perennial flow, and between montane and basin streams? Do streams with greater elevational change have higher riparian zone seed bank beta-diversity? Does nestedness or turnover contribute more to within stream beta-diversity?
I collected soil samples from the riparian and terrestrial zones of 21 sites, placing them in growth chambers at one of two temperature regimes, and monitoring emergence of seedlings for 12 weeks. Results showed an approximately equal number of warm and cool specialists in both riparian and terrestrials zones; generalists also were abundant, particularly in the riparian zone. The number of temperature specialists and generalists in the riparian zones did not differ significantly between perennial headwater and ephemeral stream types. In montane streams, alpha diversity of the soil seed bank was highest for ephemeral reaches; in basin streams the intermittent and perennial reaches had higher diversity. Spatial turnover was primarily responsible for within stream beta-diversity—reaches had different species assemblages. The large portion of temperature specialists found in riparian seed banks indicates that even with available moisture riparian zone plant community composition will likely be impacted by changing temperatures. However, the presence of so many temperature generalists in the riparian zones suggests that some component of the seed bank is adapted to variable conditions and might offer resilience in a changing climate. Study results confirm the importance of conserving multiple hydrogeomorphic reach types because they support unique species assemblages.
I collected soil samples from the riparian and terrestrial zones of 21 sites, placing them in growth chambers at one of two temperature regimes, and monitoring emergence of seedlings for 12 weeks. Results showed an approximately equal number of warm and cool specialists in both riparian and terrestrials zones; generalists also were abundant, particularly in the riparian zone. The number of temperature specialists and generalists in the riparian zones did not differ significantly between perennial headwater and ephemeral stream types. In montane streams, alpha diversity of the soil seed bank was highest for ephemeral reaches; in basin streams the intermittent and perennial reaches had higher diversity. Spatial turnover was primarily responsible for within stream beta-diversity—reaches had different species assemblages. The large portion of temperature specialists found in riparian seed banks indicates that even with available moisture riparian zone plant community composition will likely be impacted by changing temperatures. However, the presence of so many temperature generalists in the riparian zones suggests that some component of the seed bank is adapted to variable conditions and might offer resilience in a changing climate. Study results confirm the importance of conserving multiple hydrogeomorphic reach types because they support unique species assemblages.
ContributorsSetaro, Danika (Author) / Stromberg, Juliet (Thesis advisor) / Franklin, Janet (Committee member) / Makings, Elizabeth (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
Description
Bouteloua eriopoda (Torr.) Torr., also known as black grama, is a perennial bunchgrass native to arid and semiarid ecosystems in the southwestern region of North America. As a result of anthropogenic climate change, this region is predicted to increase in aridity and experience more frequent extreme drought and extreme wet years. This change in precipitation will no doubt affect black grama; however, few studies have investigated how the specific structural components of this grass will respond. The purpose of this study was to examine the effects of years since start of treatment and annual precipitation amount on tiller and stolon densities, and to test for interaction between the two predictor variables. Additionally, the effects of annual precipitation on ramets and axillary buds were investigated. By using 36 experimental plots that have been receiving drought, irrigated, or control treatments since 2007, tiller density was the most responsive component to both annual precipitation amount and years since start of treatment. Years since start of treatment and annual precipitation amount also had a statistically significant interaction, meaning the effect of precipitation amount on tiller density differs depending on how many years have passed since treatments began. Stolon density was the second-most responsive component; the predictor variables were found to have no statistically significant interaction, meaning their effects on stolon density are independent of one another. Ramet density, ramets per stolon, and axillary bud metabolic activity and density were found to be independent of annual precipitation amount for 2021. The results indicate that multiple-year extreme wet and multiple-year extreme dry conditions in the Southwest will both likely reduce tiller and stolon densities in black grama patches. Prolonged drought conditions reduced tiller and stolon production in black grama because of negative legacies from previous years. Reduced production during prolonged wet conditions could be due to increased competition between adjacent plants.
ContributorsSutter, Bryce Madison (Author) / Sala, Osvaldo E (Thesis advisor) / Makings, Elizabeth (Committee member) / Wojciechowski, Martin F (Committee member) / Arizona State University (Publisher)
Created2022