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
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
Endocrine disruptors are chemicals that interact with the hormone system to negative effect. They ‘disrupt’ normal processes to cause diseases like vaginal cancer and obesity, reproductive issues like t-shaped uteri and infertility, and developmental abnormalities like spina bifida and cleft palate. These chemicals are ubiquitous in our daily lives, components in everything from toothpaste to microwave popcorn to plastic water bottles. My dissertation looks at the history, science, and regulation of these impactful substances in order to answer the question of how endocrine disruptors appeared, got interpreted by different groups, and what role science played in the process. My analysis reveals that endocrine disruptors followed a unique science policy trajectory in the US, rapidly going from their proposal in 1991 to their federal regulation in 1996, even amid intense and majority scientific disagreement over whether the substances existed at all. That trajectory resulted from the work of a small number of scientist-activists who constructed a concept and category as scientific, social, and regulatory. By playing actors from each sphere against each other and advancing a very specific scientific narrative that fit into a regulatory and social window of opportunity in the 1990s, those scientist-activists made endocrine disruptors a national issue that few could ignore. Those actions resulted in the Endocrine Disruptor Screening Program, a heavily-criticized and ineffective regulatory program. My dissertation tells a story of the past that informs the present. In 2018, the work of researchers, public media, and policymakers in the 1990s continues to play out, evident in the deep scientific division over endocrine disrupting effects and the inability of the European Union to settle on even a definition of endocrine disruptors for regulation purposes.
ContributorsAbboud, Alexis J (Author) / Maienschein, Jane A (Thesis advisor) / Crow, Michael M. (Committee member) / Hurlbut, J. Benjamin (Committee member) / Marchant, Gary E (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation investigates how ideas of the right relationships among science, the public, and collective decision-making about science and technology come to be envisioned in constructions of public engagement. In particular, it explores how public engagement has come to be constructed in discourse around gene editing to better understand how it holds together with visions for good, democratic governance of those technologies and with what effects. Using a conceptual idiom of the co-production of science and the social order, I investigate the mutual formation of scientific expertise, responsibility, and democracy through constructions of public engagement. I begin by tracing dominant historical narratives of contemporary public engagement as a continuation of public understanding of science’s projects of social ordering for democratic society. I then analyze collections of prominent expert meetings, publications, discussions, and interventions about development, governance, and societal implications human heritable germline gene editing and gene drives that developed in tandem with commitments to public engagement around those technologies. Synthesizing the evidence from across gene editing discourse, I offer a constructive critique of constructions of public engagement as expressions and evidence of scientific responsibility as ultimately reasserting and reinforcing of scientific experts' authority in gene editing decision-making, despite intentions for public engagement to extend decision-making participation and power to publics. Such constructions of public engagement go unrecognized in gene editing discourse and thereby subtly reinforce broader visions of scientific expertise as essential to good governance by underwriting the legitimacy and authority of scientific experts to act on behalf of public interests. I further argue that the reinforcement of scientific expert authority in gene editing discourse through public engagement also centers scientific experts in a sociotechnical imaginary that I call “not for science alone.” This sociotechnical imaginary envisions scientific experts as guardians and guarantors of good, democratic governance. I then propose a possible alternatives to public engagement alone to improve gene editing governance by orienting discourse around notions of public accountability for potential shared benefits and collective harms of gene editing.
ContributorsRoss, Christian (Author) / Hurlbut, James B. (Thesis advisor) / Maienschein, Jane (Thesis advisor) / Collins, James P. (Committee member) / Crow, Michael M. (Committee member) / Sarewitz, Daniel R. (Committee member) / Arizona State University (Publisher)
Created2021
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