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: LaBaer, Joshua
- Creators: Castillo-Chavez, Carlos
Description
Mathematical modeling of infectious diseases can help public health officials to make decisions related to the mitigation of epidemic outbreaks. However, over or under estimations of the morbidity of any infectious disease can be problematic. Therefore, public health officials can always make use of better models to study the potential implication of their decisions and strategies prior to their implementation. Previous work focuses on the mechanisms underlying the different epidemic waves observed in Mexico during the novel swine origin influenza H1N1 pandemic of 2009 and showed extensions of classical models in epidemiology by adding temporal variations in different parameters that are likely to change during the time course of an epidemic, such as, the influence of media, social distancing, school closures, and how vaccination policies may affect different aspects of the dynamics of an epidemic. This current work further examines the influence of different factors considering the randomness of events by adding stochastic processes to meta-population models. I present three different approaches to compare different stochastic methods by considering discrete and continuous time. For the continuous time stochastic modeling approach I consider the continuous-time Markov chain process using forward Kolmogorov equations, for the discrete time stochastic modeling I consider stochastic differential equations using Wiener's increment and Poisson point increments, and also I consider the discrete-time Markov chain process. These first two stochastic modeling approaches will be presented in a one city and two city epidemic models using, as a base, our deterministic model. The last one will be discussed briefly on a one city SIS and SIR-type model.
ContributorsCruz-Aponte, Maytee (Author) / Wirkus, Stephen A. (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Camacho, Erika T. (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by progressive autoimmune destruction of insulin-producing pancreatic β-cells. Genetic, immunological and environmental factors contribute to T1D development. The focus of this dissertation is to track the humoral immune response in T1D by profiling autoantibodies (AAbs) and anti-viral antibodies using an innovative protein array platform called Nucleic Acid Programmable Protein Array (NAPPA).
AAbs provide value in identifying individuals at risk, stratifying patients with different clinical courses, improving our understanding of autoimmune destructions, identifying antigens for cellular immune response and providing candidates for prevention trials in T1D. A two-stage serological AAb screening against 6,000 human proteins was performed. A dual specificity tyrosine-phosphorylation-regulated kinase 2 (DYRK2) was validated with 36% sensitivity at 98% specificity by an orthogonal immunoassay. This is the first systematic screening for novel AAbs against large number of human proteins by protein arrays in T1D. A more comprehensive search for novel AAbs was performed using a knowledge-based approach by ELISA and a screening-based approach against 10,000 human proteins by NAPPA. Six AAbs were identified and validated with sensitivities ranged from 16% to 27% at 95% specificity. These two studies enriched the T1D “autoantigenome” and provided insights into T1D pathophysiology in an unprecedented breadth and width.
The rapid rise of T1D incidence suggests the potential involvement of environmental factors including viral infections. Sero-reactivity to 646 viral antigens was assessed in new-onset T1D patients. Antibody positive rate of EBV was significantly higher in cases than controls that suggested a potential role of EBV in T1D development. A high density-NAPPA platform was demonstrated with high reproducibility and sensitivity in profiling anti-viral antibodies.
This dissertation shows the power of a protein-array based immunoproteomics approach to characterize humoral immunoprofile against human and viral proteomes. The identification of novel T1D-specific AAbs and T1D-associated viruses will help to connect the nodes in T1D etiology and provide better understanding of T1D pathophysiology.
AAbs provide value in identifying individuals at risk, stratifying patients with different clinical courses, improving our understanding of autoimmune destructions, identifying antigens for cellular immune response and providing candidates for prevention trials in T1D. A two-stage serological AAb screening against 6,000 human proteins was performed. A dual specificity tyrosine-phosphorylation-regulated kinase 2 (DYRK2) was validated with 36% sensitivity at 98% specificity by an orthogonal immunoassay. This is the first systematic screening for novel AAbs against large number of human proteins by protein arrays in T1D. A more comprehensive search for novel AAbs was performed using a knowledge-based approach by ELISA and a screening-based approach against 10,000 human proteins by NAPPA. Six AAbs were identified and validated with sensitivities ranged from 16% to 27% at 95% specificity. These two studies enriched the T1D “autoantigenome” and provided insights into T1D pathophysiology in an unprecedented breadth and width.
The rapid rise of T1D incidence suggests the potential involvement of environmental factors including viral infections. Sero-reactivity to 646 viral antigens was assessed in new-onset T1D patients. Antibody positive rate of EBV was significantly higher in cases than controls that suggested a potential role of EBV in T1D development. A high density-NAPPA platform was demonstrated with high reproducibility and sensitivity in profiling anti-viral antibodies.
This dissertation shows the power of a protein-array based immunoproteomics approach to characterize humoral immunoprofile against human and viral proteomes. The identification of novel T1D-specific AAbs and T1D-associated viruses will help to connect the nodes in T1D etiology and provide better understanding of T1D pathophysiology.
ContributorsBian, Xiaofang (Author) / LaBaer, Joshua (Thesis advisor) / Mandarino, Lawrence (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015
Description
The most advanced social insects, the eusocial insects, form often large societies in which there is reproductive division of labor, queens and workers, have overlapping generations, and cooperative brood care where daughter workers remain in the nest with their queen mother and care for their siblings. The eusocial insects are composed of representative species of bees and wasps, and all species of ants and termites. Much is known about their organizational structure, but remains to be discovered.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
ContributorsRodríguez Messan, Marisabel (Author) / Kang, Yun (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Kuang, Yang (Committee member) / Page Jr., Robert E (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2018
Description
The immune system plays a dual role during neoplastic progression. It can suppress tumor growth by eliminating cancer cells, and also promote neoplastic expansion by either selecting for tumor cells that are fitter to survive in an immunocompetent host or by establishing the right conditions within the tumor microenvironment. First, I present a model to study the dynamics of subclonal evolution of cancer. I model selection through time as an epistatic process. That is, the fitness change in a given cell is not simply additive, but depends on previous mutations. Simulation studies indicate that tumors are composed of myriads of small subclones at the time of diagnosis. Because some of these rare subclones harbor pre-existing treatment-resistant mutations, they present a major challenge to precision medicine. Second, I study the question of self and non-self discrimination by the immune system, which is fundamental in the field in cancer immunology. By performing a quantitative analysis of the biochemical properties of thousands of MHC class I peptides, I find that hydrophobicity of T cell receptors contact residues is a hallmark of immunogenic epitopes. Based on these findings, I further develop a computational model to predict immunogenic epitopes which facilitate the development of T cell vaccines against pathogen and tumor antigens. Lastly, I study the effect of early detection in the context of Ebola. I develope a simple mathematical model calibrated to the transmission dynamics of Ebola virus in West Africa. My findings suggest that a strategy that focuses on early diagnosis of high-risk individuals, caregivers, and health-care workers at the pre-symptomatic stage, when combined with public health measures to improve the speed and efficacy of isolation of infectious individuals, can lead to rapid reductions in Ebola transmission.
ContributorsChowell-Puente, Diego (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Anderson, Karen S (Thesis advisor) / Maley, Carlo C (Committee member) / Wilson Sayres, Melissa A (Committee member) / Blattman, Joseph N (Committee member) / Arizona State University (Publisher)
Created2016
Description
A key factor in the success of social animals is their organization of work. Mathematical models have been instrumental in unraveling how simple, individual-based rules can generate collective patterns via self-organization. However, existing models offer limited insights into how these patterns are shaped by behavioral differences within groups, in part because they focus on analyzing specific rules rather than general mechanisms that can explain behavior at the individual-level. My work argues for a more principled approach that focuses on the question of how individuals make decisions in costly environments.
In Chapters 2 and 3, I demonstrate how this approach provides novel insights into factors that shape the flexibility and robustness of task organization in harvester ant colonies (Pogonomyrmex barbatus). My results show that the degree to which colonies can respond to work in fluctuating environments depends on how individuals weigh the costs of activity and update their behavior in response to social information. In Chapter 4, I introduce a mathematical framework to study the emergence of collective organization in heterogenous groups. My approach, which is based on the theory of multi-agent systems, focuses on myopic agents whose behavior emerges out of an independent valuation of alternative choices in a given work environment. The product of this dynamic is an equilibrium organization in which agents perform different tasks (or abstain from work) with an analytically defined set of threshold probabilities. The framework is minimally developed, but can be extended to include other factors known to affect task decisions including individual experience and social facilitation. This research contributes a novel approach to developing (and analyzing) models of task organization that can be applied in a broader range of contexts where animals cooperate.
In Chapters 2 and 3, I demonstrate how this approach provides novel insights into factors that shape the flexibility and robustness of task organization in harvester ant colonies (Pogonomyrmex barbatus). My results show that the degree to which colonies can respond to work in fluctuating environments depends on how individuals weigh the costs of activity and update their behavior in response to social information. In Chapter 4, I introduce a mathematical framework to study the emergence of collective organization in heterogenous groups. My approach, which is based on the theory of multi-agent systems, focuses on myopic agents whose behavior emerges out of an independent valuation of alternative choices in a given work environment. The product of this dynamic is an equilibrium organization in which agents perform different tasks (or abstain from work) with an analytically defined set of threshold probabilities. The framework is minimally developed, but can be extended to include other factors known to affect task decisions including individual experience and social facilitation. This research contributes a novel approach to developing (and analyzing) models of task organization that can be applied in a broader range of contexts where animals cooperate.
ContributorsUdiani, Oyita (Author) / Kang, Yun (Thesis advisor) / Fewell, Jennifer H (Thesis advisor) / Janssen, Marcus A (Committee member) / Castillo-Chavez, Carlos (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
According to the World Health Organization, cancer is one of the leading causes of death around the world. Although early diagnostics using biomarkers and improved treatments with targeted therapy have reduced the rate of cancer related mortalities, there remain many unknowns regarding the contributions of the tumor microenvironment to cancer progression and therapeutic resistance. The tumor microenvironment plays a significant role by manipulating the progression of cancer cells through biochemical and biophysical signals from the surrounding stromal cells along with the extracellular matrix. As such, there is a critical need to understand how the tumor microenvironment influences the molecular mechanisms underlying cancer metastasis to facilitate the discovery of better therapies. This thesis described the development of microfluidic technologies to study the interplay of cancer cells with their surrounding microenvironment. The microfluidic model was used to assess how exposure to chemoattractant, epidermal growth factor (EGF), impacted 3D breast cancer cell invasion and enhanced cell motility speed was noted in the presence of EGF validating physiological cell behavior. Additionally, breast cancer and patient-derived cancer-associated fibroblast (CAF) cells were co-cultured to study cell-cell crosstalk and how it affected cancer invasion. GPNMB was identified as a novel gene of interest and it was shown that CAFs enhanced breast cancer invasion by up-regulating the expression of GPNMB on breast cancer cells resulting in increased migration speed. Lastly, this thesis described the design, biological validation, and use of this microfluidic platform as a new in vitro 3D organotypic model to study mechanisms of glioma stem cell (GSC) invasion in the context of a vascular niche. It was confirmed that CXCL12-CXCR4 signaling is involved in promoting GSC invasion in a 3D vascular microenvironment, while also demonstrating the effectiveness of the microfluidic as a drug screening assay. Taken together, the broader impacts of the microfluidic model developed in this dissertation include, a possible alternative platform to animal testing that is focused on mimicking human physiology, a potential ex vivo platform using patient-derived cells for studying the interplay of cancer cells with its surrounding microenvironment, and development of future therapeutic strategies tailored toward disrupting key molecular pathways involved in regulatory mechanisms of cancer invasion.
ContributorsTruong, Danh, Ph.D (Author) / Nikkhah, Mehdi (Thesis advisor) / LaBaer, Joshua (Committee member) / Smith, Barbara (Committee member) / Mouneimne, Ghassan (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Cancer autoantibody biomarker discovery and validation using nucleic acid programmable protein array
Description
Currently in the US, many patients with cancer do not benefit from the population-based screening, due to challenges associated with the existing cancer screening scheme. Blood-based diagnostic assays have the potential to detect diseases in a non-invasive way. Proteins released from small early tumors may only be present intermittently and get diluted to tiny concentrations in the blood, making them difficult to use as biomarkers. However, they can induce autoantibody (AAb) responses, which can amplify the signal and persist in the blood even if the antigen is gone. Circulating autoantibodies is a promising class of molecules that have potential to serve as early detection biomarkers for cancers. This Ph.D thesis aims to screen for autoantibody biomarkers for the early detection of two deadly cancer, basal-like breast cancer and lung adenocarcinoma. First, a method was developed to display proteins in both native and denatured conformation on protein array. This method adopted a novel protein tag technology, called HaloTag, to covalently immobilize proteins on glass slide surface. The covalent attachment allowed these proteins to endure harsh treatment without getting dissociated from slide surface, which enabled the profiling of antibody responses against both conformational and linear epitopes. Next, a plasma screening protocol was optimized to significantly increase signal to noise ratio of protein array based AAb detection. Following this, the AAb responses in basal-like breast cancer were explored using nucleic acid programmable protein arrays (NAPPA) containing 10,000 full-length human proteins in 45 cases and 45 controls. After verification in a large sample set (145 basal-like breast cancer cases / 145 controls / 70 non-basal breast cancer) by ELISA, a 13-AAb classifier was developed to differentiate patients from controls with a sensitivity of 33% at 98% specificity. Similar approach was also applied to the lung cancer study to identify AAbs that distinguished lung cancer patients from computed-tomography positive benign pulmonary nodules (137 lung cancer cases, 127 smoker controls, 170 benign controls). In this study, two panels of AAbs were discovered that showed promising sensitivity and specificity. Six out of eight AAb targets were also found to have elevated mRNA level in lung adenocarcinoma patients using TCGA data. These projects as a whole provide novel insights on the association between AAbs and cancer, as well as general B cell antigenicity against self-proteins.
ContributorsWang, Jie (Author) / LaBaer, Joshua (Thesis advisor) / Anderson, Karen S (Committee member) / Lake, Douglas F (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015
Description
Glioblastoma (GBM), the most common and aggressive primary brain tumor affecting adults, is characterized by an aberrant yet druggable epigenetic landscape. The Histone Deacetylases (HDACs), a major family of epigenetic regulators, favor transcriptional repression by mediating chromatin compaction and are frequently overexpressed in human cancers, including GBM. Hence, over the last decade there has been considerable interest in using HDAC inhibitors (HDACi) for the treatment of malignant primary brain tumors. However, to date most HDACi tested in clinical trials have failed to provide significant therapeutic benefit to patients with GBM. This is because current HDACi have poor or unknown pharmacokinetic profiles, lack selectivity towards the different HDAC isoforms, and have narrow therapeutic windows. Isoform selectivity for HDACi is important given that broad inhibition of all HDACs results in widespread toxicity across different organs. Moreover, the functional roles of individual HDAC isoforms in GBM are still not well understood. Here, I demonstrate that HDAC1 expression increases with brain tumor grade and is correlated with decreased survival in GBM. I find that HDAC1 is the essential HDAC isoform in glioma stem cells and its loss is not compensated for by its paralogue HDAC2 or other members of the HDAC family. Loss of HDAC1 alone has profound effects on the glioma stem cell phenotype in a p53-dependent manner and leads to significant suppression of tumor growth in vivo. While no HDAC isoform-selective inhibitors are currently available, the second-generation HDACi quisinostat harbors high specificity for HDAC1. I show that quisinostat exhibits potent growth inhibition in multiple patient-derived glioma stem cells. Using a pharmacokinetics- and pharmacodynamics-driven approach, I demonstrate that quisinostat is a brain-penetrant molecule that reduces tumor burden in flank and orthotopic models of GBM and significantly extends survival both alone and in combination with radiotherapy. The work presented in this thesis thereby unveils the non-redundant functions of HDAC1 in therapy- resistant glioma stem cells and identifies a brain-penetrant HDACi with higher selectivity towards HDAC1 as a potent radiosensitizer in preclinical models of GBM. Together, these results provide a rationale for developing quisinostat as a potential adjuvant therapy for the treatment of GBM.
ContributorsLo Cascio, Costanza (Author) / LaBaer, Joshua (Thesis advisor) / Mehta, Shwetal (Committee member) / Mirzadeh, Zaman (Committee member) / Mangone, Marco (Committee member) / Paek, Andrew (Committee member) / Arizona State University (Publisher)
Created2022
Description
Transient Receptor Potential Vanilloid-1 (TRPV1) is an integral membrane polymodal cation channel involved in various essential biological functions, including thermosensing, thermoregulation, and nociception. Discrete TRPV1 activation modes such as ligand, heat, and proton have been challenging to disentangle. However, dissecting the polymodal nature of TRPV1 is essential for therapeutic development. The human TRPV1 (hTRPV1) voltage-sensing like domain (VSLD; transmembrane helices S1-S4) contains the canonical vanilloid ligand binding site and significantly contributes to thermosensing. Nuclear magnetic resonance (NMR)-detected studies probe the role of the hTRPV1-VSLD in TRPV1 polymodal function. The hTRPV1-VSLD is identified as an allosteric hub for all three primary TRPV1 activation modes and demonstrates plasticity in chemical ligand modulation. The presented results underscore molecular features in the VSLD that dictate TRPV1 function, highlighting important considerations for future therapeutic design.
ContributorsOwens, Aerial M. (Author) / Van Horn, Wade D. (Thesis advisor) / Levitus, Marcia (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2023