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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- All Subjects: Biology
- Creators: Kostelich, Eric
- Creators: Angilletta, Michael
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
There is considerable recent interest in the dynamic nature of immune function in the context of an animal’s internal and external environment. An important focus within this field of ecoimmunology is on how availability of resources such as energy can alter immune function. Water is an additional resource that drives animal development, physiology, and behavior, yet the influence hydration has on immunity has received limited attention. In particular, hydration state may have the greatest potential to drive fluctuations in immunity and other physiological functions in species that live in water-limited environments where they may experience periods of dehydration. To shed light on the sensitivity of immune function to hydration state, I first tested the effect of hydration states (hydrated, dehydrated, and rehydrated) and digestive states on innate immunity in the Gila monster, a desert-dwelling lizard. Though dehydration is often thought to be stressful and, if experienced chronically, likely to decrease immune function, dehydration elicited an increase in immune response in this species, while digestive state had no effect. Next, I tested whether dehydration was indeed stressful, and tested a broader range of immune measures. My findings validated the enhanced innate immunity across additional measures and revealed that Gila monsters lacked a significant stress hormone response during dehydration (though results were suggestive). I next sought to test if life history (in terms of environmental stability) drives these differences in dehydration responses using a comparative approach. I compared four confamilial pairs of squamate species that varied in habitat type within each pair—four species that are adapted to xeric environments and four that are adapted to more mesic environments. No effect of life history was detected between groups, but hydration was a driver of some measures of innate immunity and of stress hormone concentrations in multiple species. Additionally, species that exhibited a stress response to dehydration did not have decreased innate immunity, suggesting these physiological responses may often be decoupled. My dissertation work provides new insight into the relationship between hydration, stress, and immunity, and it may inform future work exploring disease transmission or organismal responses to climate change.
ContributorsMoeller, Karla T (Author) / DeNardo, Dale (Thesis advisor) / Angilletta, Michael (Committee member) / French, Susannah (Committee member) / Rutowski, Ronald (Committee member) / Sabo, John (Committee member) / Arizona State University (Publisher)
Created2016
Description
Desert environments provide considerable challenges to organisms because of high temperatures and limited food and water resources. Accordingly, desert species have behavioral and physiological traits that enable them to cope with these constraints. However, continuing human activity as well as anticipated further changes to the climate and the vegetative community pose a great challenge to such balance between an organism and its environment. This is especially true in the Arabian Desert, where climate conditions are extreme and environmental disturbances substantial. This study combined laboratory and field components to enhance our understanding of dhub (Uromastyx aegyptius) ecophysiology and determine whether habitat protection influences dhub behavior and physiology.
Results of this study showed that while body mass and body condition consistently diminished as the active season progressed, they were both greater in protected habitats compared to non-protected habitats, regardless of season. Dhubs surface activity and total body water decreased while evaporative water loss and body temperature increased as the active season progressed and ambient temperature got hotter. Total body water was also significantly affected by habitat protection.
Overall, this study revealed that, while habitat protection provided more vegetation, it had little effect on seasonal changes in surface activity. While resource availability in protected areas might allow for larger dhub populations, unprotected areas showed similar body morphometrics, activity, and body temperatures. By developing an understanding of how different coping strategies are linked to particular ecological, morphological, and phylogenetic traits, we will be able to make more accurate predictions regarding the vulnerability of species. By combining previous studies pertaining to conservation of protected species with the results of my study, a number of steps in ecosystem management are recommended to help in the preservation of dhubs in the Kuwaiti desert.
Results of this study showed that while body mass and body condition consistently diminished as the active season progressed, they were both greater in protected habitats compared to non-protected habitats, regardless of season. Dhubs surface activity and total body water decreased while evaporative water loss and body temperature increased as the active season progressed and ambient temperature got hotter. Total body water was also significantly affected by habitat protection.
Overall, this study revealed that, while habitat protection provided more vegetation, it had little effect on seasonal changes in surface activity. While resource availability in protected areas might allow for larger dhub populations, unprotected areas showed similar body morphometrics, activity, and body temperatures. By developing an understanding of how different coping strategies are linked to particular ecological, morphological, and phylogenetic traits, we will be able to make more accurate predictions regarding the vulnerability of species. By combining previous studies pertaining to conservation of protected species with the results of my study, a number of steps in ecosystem management are recommended to help in the preservation of dhubs in the Kuwaiti desert.
ContributorsAl-Sayegh, Mohammed (Author) / DeNardo, Dale (Thesis advisor) / Angilletta, Michael (Committee member) / Smith, Andrew (Committee member) / Sabo, John (Committee member) / Majeed, Qais (Committee member) / Arizona State University (Publisher)
Created2017
Description
The migratory grasshopper (Melanoplus sanguinipes) is one of the most economically important grasshoppers in the western rangelands of the United States (US), capable of causing incredible amounts of damage to crops and rangelands. While M. sanguinipes has been the focus of many research studies, areas like field nutritional physiology and ecology, and interactions between nutritional physiology and biopesticide resistance have very little research. This dissertation presents a multifaceted approach through three research-driven chapters that examine the nutritional physiology of M. sanguinipes and how it interacts with an entomopathogenic fungus for grasshopper management, as well as the challenges of using biopesticides for grasshopper management. Using the Geometric Framework for Nutrition (GFN), I established baseline macronutrient intake for M. sanguinipes, both in laboratory and field populations. Through this work, I found that field and lab populations can exhibit different protein (p) to carbohydrate (c) ratios, or Intake Targets (ITs), but that the field populations had ITs that matched the nutrients available in their environment. I also used the GFN to show that infections with the fungal entomopathogen Metarhizium robertsii DWR2009 did not alter ITs in M. sanguinipes. Although, when confined to carbohydrate- or protein-biased diets, infected grasshoppers had a slightly extended lifespan relative to grasshoppers fed balanced protein:carbohydrate diets. Interestingly, in a postmortem for the grasshopper, the fungus was only able to effectively sporulate on grasshoppers fed the 1p:1c diets, suggesting that grasshopper diet can have substantial impacts on the spread of fungal biopesticides throughout a population, in the absence of any inhibitory abiotic factors. Lastly, I examined the major barriers to fungal and microsporidian biopesticide usage in the United States, including low efficacy, thermal and environmental sensitivity, non-target effects, unregistered or restricted use, and economic or accessibility barriers. I also explored potential solutions to these challenges. This dissertation's focus on Melanoplus sanguinipes and Metarhizium roberstii Strain DWR2009, generates new information about how nutritional physiology and immunology intersect to impact M. sanguinipes performance. The methodology in each of the experimental chapters provides a framework for examining other problematic grasshopper species, by determining baseline nutritional physiology, and coupling nutrition with immunology to maximize the effectiveness of biological pesticides.
ContributorsZembrzuski, Deanna (Author) / Cease, Arianne (Thesis advisor) / Harrison, Jon (Committee member) / Angilletta, Michael (Committee member) / Jaronski, Stefan (Committee member) / Arizona State University (Publisher)
Created2023
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020
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
The analysis focuses on a two-population, three-dimensional model that attempts to accurately model the growth and diffusion of glioblastoma multiforme (GBM), a highly invasive brain cancer, throughout the brain. Analysis into the sensitivity of the model to
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
ContributorsTrent, Austin Lee (Author) / Kostelich, Eric (Thesis advisor) / Gumel, Abba (Committee member) / Kuang, Yang (Committee member) / Arizona State University (Publisher)
Created2020
Description
Ectotherms rely on external heat to attain target body temperatures which can vary based on the animal’s current physiological activity. Many ectotherms become thermophilic (“heat-loving”) during crucial physiological processes like digestion and reproduction, behaviorally thermoregulating to increase body temperature higher than what they otherwise prefer. However, there is a positive relationship between body temperature and water loss that dictates increasing body temperature typically elicits an increase in water loss. Animals that inhabit areas where water is at least seasonally limited (e.g., deserts, wet-dry forests) may face a tradeoff between prioritizing behavioral thermophily to optimize physiological processes versus prioritizing water balance and potentially sacrificing some aspect of total performance capability.It is thus far unknown how reduced water availability and subsequent dehydration may influence thermophily in ectotherms. I hypothesized that behaviorally thermoregulating ectotherms exhibit thermophily during critical physiological events, and the extent to which thermophily is expressed is influenced by the animal’s hydric state. Using Children’s pythons (Antaresia childreni), I investigated the effects of dehydration on behavioral thermophily during digestion and reproduction. I found that dehydration caused a suppression in digestion-associated thermophily, where dehydrated snakes returned to pre-feeding body temperature sooner than they did when they were hydrated. In contrast, water deprivation at different reproductive stages had no effect on thermophily despite leading to a significant increase in the female’s plasma osmolality.
ii
Additionally, the timing of water deprivation during reproduction had differing effects on plasma osmolality and circulating triglyceride, total protein, and corticosterone concentrations. My research provides evidence of the sensitive and complex dynamic between body temperature, water balance, and physiological processes. At a time when many dry ecosystems are becoming hotter and drier, my investigation of dehydration and its influence on thermal dynamics and physiological metrics provides insight into cryptic effects on the vital processes of digestion and reproduction.
ContributorsAzzolini, Jill L. (Author) / Denardo, Dale F. (Thesis advisor) / John-Alder, Henry (Committee member) / Angilletta, Michael (Committee member) / Pratt, Stephen (Committee member) / Arizona State University (Publisher)
Created2023