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
Cell adhesion is an important aspect of many biological processes. The atomic force microscope (AFM) has made it possible to quantify the forces involved in cellular adhesion using a technique called single cell force spectroscopy (SCFS). AFM based SCFS offers versatile control over experimental conditions for probing directly the interaction between specific cell types and specific proteins, surfaces, or other cells. Transmembrane integrins are the primary proteins involved in cellular adhesion to the extra cellular matix (ECM). One of the chief integrins involved in the adhesion of leukocyte cells is αMβ2 (Mac-1). The experiments in this dissertation quantify the adhesion of Mac-1 expressing human embryonic kidney (HEK Mac-1), platelets, and neutrophils cells on substrates with different concentrations of fibrinogen and on fibrin gels and multi-layered fibrinogen coated fibrin gels. It was shown that multi-layered fibrinogen reduces the adhesion force of these cells considerably. A novel method was developed as part of this research combining total internal reflection microscopy (TIRFM) with SCFS allowing for optical microscopy of HEK Mac-1 cells interacting with bovine serum albumin (BSA) coated glass after interacting with multi-layered fibrinogen. HEK Mac-1 cells are able to remove fibrinogen molecules from the multi-layered fibrinogen matrix. An analysis methodology for quantifying the kinetic parameters of integrin-ligand interactions from SCFS experiments is proposed, and the kinetic parameters of the Mac-1 fibrinogen bond are quantified. Additional SCFS experiments quantify the adhesion of macrophages and HEK Mac-1 cells on functionalized glass surfaces and normal glass surfaces. Both cell types show highest adhesion on a novel functionalized glass surface that was prepared to induce macrophage fusion. These experiments demonstrate the versatility of AFM based SCFS, and how it can be applied to address many questions in cellular biology offering quantitative insights.
ContributorsChristenson, Wayne B (Author) / Ros, Robert (Thesis advisor) / Beckstein, Oliver (Committee member) / Lindsay, Stuart (Committee member) / Ugarova, Tatiana (Committee member) / Arizona State University (Publisher)
Created2016
Description
Emerging pathogens present several challenges to medical diagnostics. Primarily, the exponential spread of a novel pathogen through naïve populations require a rapid and overwhelming diagnostic response at the site of outbreak. While point-of-care (PoC) platforms have been developed for detection of antigens, serologic responses, and pathogenic genomes, only nucleic acid diagnostics currently have the potential to be developed and manufactured within weeks of an outbreak owing to the speed of next-generation sequencing and custom DNA synthesis. Among nucleic acid diagnostics, isothermal amplification strategies are uniquely suited for PoC implementation due to their simple instrumentation and lack of thermocycling requirement. Unfortunately, isothermal strategies are currently prone to spurious nonspecific amplification, hindering their specificity and necessitating extensive empirical design pipelines that are both time and resource intensive. In this work, isothermal amplification strategies are extensively compared for their feasibility of implementation in outbreak response scenarios. One such technology, Loop-mediated Amplification (LAMP), is identified as having high-potential for rapid development and PoC deployment. Various approaches to abrogating nonspecific amplification are described including a novel in silico design tool based on coarse-grained simulation of interactions between thermophilic DNA polymerase and DNA strands in isothermal reaction conditions. Nonspecific amplification is shown to be due to stabilization of primer secondary structures by high concentrations of Bst DNA polymerase and a mechanism of micro-complement-mediated cross-priming is demonstrated as causal via nanopore sequencing of nonspecific reaction products. The resulting computational model predicts primer set background in 64% of 67 test assays and its usefulness is illustrated further by determining problematic primers in a West Nile Virus-specific LAMP primer set and optimizing primer 3’ nucleotides to eliminate micro-complements within the reaction, resulting in inhibition of background accumulation. Finally, the emergence of Orthopox monkeypox (MPXV) as a recurring threat is discussed and SimCycle is utilized to develop a novel technique for clade-specific discrimination of MPXV based on bridging viral genomic rearrangements (Bridging LAMP). Bridging LAMP is implemented in a 4-plex microfluidic format and demonstrates 100% sensitivity in detection of 100 copies of viral lysates and 45 crude MPXV-positive patient samples collected during the 2022 Clade IIb outbreak.
ContributorsKnappenberger, Mark Daniel (Author) / Anderson, Karen S (Thesis advisor) / LaBaer, Joshua (Committee member) / Roberson, Robert (Committee member) / Lindsay, Stuart (Committee member) / Arizona State University (Publisher)
Created2023
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