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.
Displaying 1 - 9 of 9
Filtering by
- All Subjects: Biology
- Creators: Kostelich, Eric
- Creators: Baluch, Debra P
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
Pregnancy and childbirth are both natural occurring events, but still little is known about the signaling mechanisms that induce contractions. Throughout the world, premature labor occurs in 12% of all pregnancies with 36% of infant deaths resulting from preterm related causes. Even though the cause of preterm labor can vary, understanding alternative signaling pathways, which affect muscle contraction, could provide additional treatment options in stopping premature labor. The uterus is composed of smooth muscle, which is innervated, with a plexus of nerves that cover the muscle fibers. Smooth muscle can be stimulated or modulated by many sources such as neurotransmitters [i.e. dopamine], hormones [i.e. estrogen], peptides [i.e. oxytocin] and amines. This study focuses on the biogenic monoamine tyramine, which is produced in the tyrosine catecholamine biosynthesis pathway. Tyramine is known to be associated with peripheral vasoconstriction, increased cardiac output, increased respiration, elevated blood glucose and the release of norepinephrine. This research has found tyramine, and its specific receptor TAAR1, to be localized within mouse uterus and that this monoamine can induce uterine contractions at levels similar to oxytocin.
ContributorsObayomi, SM Bukola (Author) / Baluch, Debra P (Thesis advisor) / Deviche, Pierre (Thesis advisor) / Smith, Brian H. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Human preterm labor is the single most significant issue in modern obstetrics andgynecology, affecting ten percent of pregnancies, constituting the leading cause of infant death, and contributing significantly to chronic childhood disease. Obstetricians and reproductive scientists are faced with the major challenge of trying to increase the understanding of the complex molecular and cellular signals that regulate uterine activity during human pregnancy and labor. Even though preterm labor accounts for a large portion of perinatal mortality and morbidity, there still is not an effective therapeutic strategy for the treatment or prevention of preterm labor. This dissertation presents tyramine as an alternative modulator of uterine activity. In this dissertation the aims were as follows: 1) to investigate the localization of tyramine and trace amine associated receptor 1 (TAAR1) in the mouse uterine horn using immunohistochemistry as well as confirm the presence of tyramine in the uterine tissue using high performance liquid chromatography, 2) identify which TAAR 1-9 subtypes were present in the mouse uterine horn using RT-qPCR, 3) investigate ultrastructural differences in the mouse uterine horn following tyramine and dopamine treatment using transmission electron microscopy and 4) investigate pinopod ultrastructure as well as pinopod ultrastructural differences following tyramine and dopamine treatment. The research presented in this dissertation showed: 1) tyramine has very specific localization in the mouse endometrium, mainly in the uterine glands, TAAR1 is localized all throughout the perimetrium, myometrium and endometrium, and that tyramine was confirmed and quantified using HPLC, 2) TAAR 1- 9 genes are expressed in trace levels in the mouse uterine horn, 3) tyramine influences changes in endometrial ultrastructure, and 4) tyramine influences changes in pinopod ultrastructure. Ultimately these findings can help with identifying novel treatment options not only for spontaneous preterm labor contractions but also for other uterine related disorders.
ContributorsObayomi, SM Bukola (Author) / Baluch, Debra P (Thesis advisor) / Roberson, Robert (Thesis advisor) / Sweazea, Karen (Committee member) / Brent, Colin (Committee member) / Arizona State University (Publisher)
Created2023
Description
According to the World Health Organization, obesity has nearly tripled since 1975 and forty-one million children under the age of 5 are overweight or obese (World Health Organization, 2018). Exercise is a potential intervention to prevent obesity-induced cardiovascular complications as exercise training has been shown to aid nitric oxide (NO) production as well as preserving endothelial function in obese mice (Silva et al., 2016). A soil-derived organic mineral compound (OMC) has been shown to lower blood sugar in diabetic mice (Deneau et al., 2011). Prior research has shown that, while OMC did not prevent high fat diet (HFD)-induced increases in body fat in male Sprague-Dawley rats, it was effective at preventing HFD-induced impaired vasodilation (M. S. Crawford et al., 2019). Six-weeks of HFD has been shown to impair vasodilation through oxidative-stress mediated scavenging of NO as well as upregulation of inflammatory pathways including inducible nitric oxide synthase (iNOS) and cyclooxygenase (Karen L. Sweazea et al., 2010). Therefore, the aim of the present study was to determine whether OMC alters protein expression of iNOS and endothelial NOS (eNOS) in the vasculature of rats fed a control or HFD with and without OMC supplementation. Six-week old male Sprague-Dawley rats were fed either a standard chow diet (CHOW) or a HFD composed of 60% kcal from fat for 10 weeks. The rats were administered OMC at doses of 0 mg/mL (control), 0.6 mg/mL, or 3.0 mg/mL added to their drinking water. Following euthanasia with sodium pentobarbital (200 mg/kg, i.p.), mesenteric arteries and the surrounding perivascular adipose tissue were isolated and prepared for Western Blot analyses. Mesenteric arteries from HFD rats had more uncoupled eNOS (p = 0.006) and iNOS protein expression (p = 0.027) than rats fed the control diet. OMC was not effective at preventing the uncoupling of eNOS or increase in iNOS induced by HFD. Perivascular adipose tissue (PVAT) showed no significant difference in iNOS protein expression between diet or OMC treatment groups. These findings suggest that OMC is not likely working through the iNOS or eNOS pathways to improve vasodilation in these rats, but rather, appears to be working through another mechanism.
ContributorsNelson, Morgan Allen (Author) / Sweazea, Karen L (Thesis advisor) / Katsanos, Christos S (Committee member) / Baluch, Debra P (Committee member) / Arizona State University (Publisher)
Created2020
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
The desire to start a family is something millions of people around the globe strive to achieve. However, many factors such as the societal changes in family planning due to increasing maternal age, use of birth control, and ever-changing lifestyles have increased the number of infertility cases seen in the United States each year. Infertility can manifest as a prolonged inability to conceive, or inability to carry a pregnancy full-term. Modern advancements in the field of reproductive medicine have begun to promote the use of Assisted Reproductive Technologies (ART) to circumvent reduced fertility in both men and women. Implementation of techniques such as In Vitro Fertilization, Intracytoplasmic Sperm Injection, and Pre-Implantation Genetic Testing have allowed many couples to conceive. There is continual effort being made towards developing more effective and personalized fertility treatments. This often begins in the form of animal research—a fundamental step in biomedical research. This dissertation examines infertility as a medical condition through the characterization of normal reproductive anatomy and physiology in the introductory overview of reproduction. Specific pathologies of male and female-factor infertility are described, which necessitates the use of ARTs. The various forms of ARTs currently utilized in a clinical setting are addressed including history, preparations, and protocols for each technology. To promote continual advancement of the field, both animal studies and human trials provide fundamental stepping-stones towards the execution of new techniques and protocols. Examples of research conducted for the betterment of human reproductive medicine are explored, including an animal study conducted in mice exploring the role of tyramine in ovulation. With the development and implementation of new technologies and protocols in the field, this also unearths ethical dilemmas that further complicate the addition of new technologies in the field. Combining an extensive review in assisted reproduction, research and clinical fieldwork, this study investigates the history and development of novel research conducted in reproductive medicine and explores the broader implications of new technologies in the field.
ContributorsPeck, Shelbi Marie (Author) / Baluch, Debra P (Thesis advisor) / Maienschein, Jane (Thesis advisor) / Sweazea, Karen (Committee member) / Ellison, Karin (Committee member) / Arizona State University (Publisher)
Created2021