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 - 10 of 11
Filtering by
- All Subjects: Biology
- Creators: Roberson, Robert
- Creators: Kostelich, Eric
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
The cyanobacterium Synechocystis sp. PCC 6803 performs oxygenic photosynthesis. Light energy conversion in photosynthesis takes place in photosystem I (PSI) and photosystem II (PSII) that contain chlorophyll, which absorbs light energy that is utilized as a driving force for photosynthesis. However, excess light energy may lead to formation of reactive oxygen species that cause damage to photosynthetic complexes, which subsequently need repair or replacement. To gain insight in the degradation/biogenesis dynamics of the photosystems, the lifetimes of photosynthetic proteins and chlorophyll were determined by a combined stable-isotope (15N) and mass spectrometry method. The lifetimes of PSII and PSI proteins ranged from 1-33 and 30-75 hours, respectively. Interestingly, chlorophyll had longer lifetimes than the chlorophyll-binding proteins in these photosystems. Therefore, photosynthetic proteins turn over and are replaced independently from each other, and chlorophyll is recycled from the damaged chlorophyll-binding proteins. In Synechocystis, there are five small Cab-like proteins (SCPs: ScpA-E) that share chlorophyll a/b-binding motifs with LHC proteins in plants. SCPs appear to transiently bind chlorophyll and to regulate chlorophyll biosynthesis. In this study, the association of ScpB, ScpC, and ScpD with damaged and repaired PSII was demonstrated. Moreover, in a mutant lacking SCPs, most PSII protein lifetimes were unaffected but the lifetime of chlorophyll was decreased, and one of the nascent PSII complexes was missing. SCPs appear to bind PSII chlorophyll while PSII is repaired, and SCPs stabilize nascent PSII complexes. Furthermore, aminolevulinic acid biosynthesis, an early step of chlorophyll biosynthesis, was impaired in the absence of SCPs, so that the amount of chlorophyll in the cells was reduced. Finally, a deletion mutation was introduced into the sll1906 gene, encoding a member of the putative bacteriochlorophyll delivery (BCD) protein family. The Sll1906 sequence contains possible chlorophyll-binding sites, and its homolog in purple bacteria functions in proper assembly of light-harvesting complexes. However, the sll1906 deletion did not affect chlorophyll degradation/biosynthesis and photosystem assembly. Other (parallel) pathways may exist that may fully compensate for the lack of Sll1906. This study has highlighted the dynamics of photosynthetic complexes in their biogenesis and turnover and the coordination between synthesis of chlorophyll and photosynthetic proteins.
ContributorsYao, Cheng I Daniel (Author) / Vermaas, Wim (Thesis advisor) / Fromme, Petra (Committee member) / Roberson, Robert (Committee member) / Webber, Andrew (Committee member) / Arizona State University (Publisher)
Created2011
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
Male reproductive dysfunction accounts for almost half of male infertility cases, yet the signaling mechanisms involved in the male reproductive system remain unclear. Although the exact cause of male reproductive dysfunction varies, obtaining a better understanding of the modulators of smooth muscle contractions may provide new targets for the treatment of male reproductive conditions. The male reproductive tract, consisting of the testes, epididymis, vas deferens, and penis, is lined with innervated smooth muscle fibers that transport spermatozoa through the system. Contractions of these smooth muscle fibers can be modulated by neurotransmitters and hormones, like dopamine and norepinephrine, as well as biogenic amines. The focus of this study is on the biogenic amine tyramine, which is produced by the breakdown of tyrosine via decarboxylation. Tyramine has been shown to modulate vasoconstriction and increase blood pressure due to its effect on smooth muscle contractions. This study has found that tyramine localizes in male reproductive tissues and modulates smooth muscle contractions. Age and environment were also found to play a significant role in the expression of tyramine and its associated receptor, TAAR1.
ContributorsSteadman, Solange (Author) / Baluch, Debra (Thesis advisor) / Roberson, Robert (Committee member) / Sweazea, Karen (Committee member) / Arizona State University (Publisher)
Created2023
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
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
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 partitioning of photosynthates between their sites of production (source) and their sites of utilization (sink) is a major determinant of crop yield and the potential of regulating this translocation promises substantial opportunities for yield increases. Ubiquitous overexpression of the plant type I proton pyrophosphatase (H+-PPase) in crops improves several valuable traits including salt tolerance and drought resistance, nutrient and water use efficiencies, and increased root biomass and yield. Originally, type I H+-PPases were described as pyrophosphate (PPi)-dependent proton pumps localized exclusively in vacuoles of mesophyll and meristematic tissues. It has been proposed that in the meristematic tissues, the role of this enzyme would be hydrolyzing PPi originated in biosynthetic reactions and favoring sink strength. Interestingly, this enzyme has been also localized at the plasma membrane of companion cells in the phloem which load and transport photosynthates from source leaves to sinks. Of note, the plasma membrane-localized H+-PPase could only function as a PPi-synthase in these cells due to the steep proton gradient between the apoplast and cytosol. The generated PPi would favor active sucrose loading through the sucrose/proton symporter in the phloem by promoting sucrose hydrolysis through the Sucrose Synthase pathway and providing the ATP required to maintain the proton gradient. To better understand these two different roles of type I H+-PPases, a series of Arabidopsis thaliana transgenic plants were generated. By expressing soluble pyrophosphatases in companion cells of Col-0 ecotype and H+-PPase mutants, impaired photosynthates partitioning was observed, suggesting phloem-localized H+-PPase could generate the PPi required for sucrose loading. Col-0 plants expressed with either phloem- or meristem-specific AVP1 overexpression cassette and the cross between the two tissue specific lines (Cross) were generated. The results showed that the phloem-specific AVP1-overexpressing plants had increased root hair elongation under limited nutrient conditions and both phloem- and meristem-overexpression of AVP1 contributed to improved rhizosphere acidification and drought resistance. It was concluded that H+-PPases localized in both sink and source tissues regulate plant growth and performance under stress through its versatile enzymatic functions (PPi hydrolase and synthase).
ContributorsLi, Lin (Author) / Park, Yujin (Thesis advisor) / Mangone, Marco (Committee member) / Roberson, Robert (Committee member) / Vermaas, Willem (Committee member) / Arizona State University (Publisher)
Created2022