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 31 - 38 of 38
Description
RNA aptamers adopt tertiary structures that enable them to bind to specific ligands. This capability has enabled aptamers to be used for a variety of diagnostic, therapeutic, and regulatory applications. This dissertation focuses on the use RNA aptamers in two biological applications: (1) nucleic acid diagnostic assays and (2) scaffolding of enzymatic pathways. First, sensors for detecting arbitrary target RNAs based the fluorogenic RNA aptamer Broccoli are designed and validated. Studies of three different sensor designs reveal that toehold-initiated Broccoli-based aptasensors provide the lowest signal leakage and highest signal intensity in absence and in presence of the target RNA, respectively. This toehold-initiated design is used for developing aptasensors targeting pathogens. Diagnostic assays for detecting pathogen nucleic acids are implemented by integrating Broccoli-based aptasensors with isothermal amplification methods. When coupling with recombinase polymerase amplification (RPA), aptasensors enable detection of synthetic valley fever DNA down to concentrations of 2 fM. Integration of Broccoli-based aptasensors with nucleic acid sequence-based amplification (NASBA) enables as few as 120 copies/mL of synthetic dengue RNA to be detected in reactions taking less than three hours. Moreover, the aptasensor-NASBA assay successfully detects dengue RNA in clinical samples. Second, RNA scaffolds containing peptide-binding RNA aptamers are employed for programming the synthesis of nonribosomal peptides (NRPs). Using the NRP enterobactin pathway as a model, RNA scaffolds are developed to direct the assembly of the enzymes entE, entB, and entF from E. coli, along with the aryl-carrier protein dhbB from B. subtilis. These scaffolds employ X-shaped RNA motifs from bacteriophage packaging motors, kissing loop interactions from HIV, and peptide-binding RNA aptamers to position peptide-modified NRP enzymes. The resulting RNA scaffolds functionalized with different aptamers are designed and evaluated for in vitro production of enterobactin. The best RNA scaffold provides a 418% increase in enterobactin production compared with the system in absence of the RNA scaffold. Moreover, the chimeric scaffold, with E. coli and B. subtilis enzymes, reaches approximately 56% of the activity of the wild-type enzyme assembly. The studies presented in this dissertation will be helpful for future development of nucleic acid-based assays and for controlling protein interaction for NRPs biosynthesis.
ContributorsTang, Anli (Author) / Green, Alexander (Thesis advisor) / Yan, Hao (Committee member) / Woodbury, Neal (Committee member) / Arizona State University (Publisher)
Created2020
Description
Modern manufacturing systems are part of a complex supply chain where customer preferences are constantly evolving. The rapidly evolving market demands manufacturing organizations to be increasingly agile and flexible. Medium term capacity planning for manufacturing systems employ queueing network models based on stationary demand assumptions. However, these stationary demand assumptions are not very practical for rapidly evolving supply chains. Nonstationary demand processes provide a reasonable framework to capture the time-varying nature of modern markets. The analysis of queues and queueing networks with time-varying parameters is mathematically intractable. In this dissertation, heuristics which draw upon existing steady state queueing results are proposed to provide computationally efficient approximations for dynamic multi-product manufacturing systems modeled as time-varying queueing networks with multiple customer classes (product types). This dissertation addresses the problem of performance evaluation of such manufacturing systems.
This dissertation considers the two key aspects of dynamic multi-product manufacturing systems - namely, performance evaluation and optimal server resource allocation. First, the performance evaluation of systems with infinite queueing room and a first-come first-serve service paradigm is considered. Second, systems with finite queueing room and priorities between product types are considered. Finally, the optimal server allocation problem is addressed in the context of dynamic multi-product manufacturing systems. The performance estimates developed in the earlier part of the dissertation are leveraged in a simulated annealing algorithm framework to obtain server resource allocations.
This dissertation considers the two key aspects of dynamic multi-product manufacturing systems - namely, performance evaluation and optimal server resource allocation. First, the performance evaluation of systems with infinite queueing room and a first-come first-serve service paradigm is considered. Second, systems with finite queueing room and priorities between product types are considered. Finally, the optimal server allocation problem is addressed in the context of dynamic multi-product manufacturing systems. The performance estimates developed in the earlier part of the dissertation are leveraged in a simulated annealing algorithm framework to obtain server resource allocations.
ContributorsJampani Hanumantha, Girish (Author) / Askin, Ronald (Thesis advisor) / Ju, Feng (Committee member) / Yan, Hao (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2020
Description
Proteins are a large collection of biomolecules that orchestrate the vital
cellular processes of life. The last decade has witnessed dramatic advances in the
field of proteomics, which broadly include characterizing the composition, structure,
functions, interactions, and modifications of numerous proteins in biological systems,
and elucidating how the miscellaneous components collectively contribute to the
phenotypes associated with various disorders. Such large-scale proteomics studies
have steadily gained momentum with the evolution of diverse high-throughput
technologies. This work illustrates the development of novel high-throughput
proteomics platforms and their applications in translational and structural biology. In
Chapter 1, nucleic acid programmable protein arrays displaying the human
proteomes were applied to immunoprofiling of paired serum and cerebrospinal fluid
samples from patients with Alzheimer’s disease. This high-throughput
immunoproteomic approach allows us to investigate the global antibody responses
associated with Alzheimer’s disease and potentially identify the diagnostic
autoantibody biomarkers. In Chapter 2, a versatile proteomic pipeline based on the
baculovirus-insect cell expression system was established to enable high-throughput
gene cloning, protein production, in vivo crystallization and sample preparation for Xray diffraction. In conjunction with the advanced crystallography methods, this endto-end pipeline promises to substantially facilitate the protein structural
determination. In Chapter 3, modified nucleic acid programmable protein arrays
were developed and used for probing protein-protein interactions at the proteome
level. From the perspective of biomarker discovery, structural proteomics, and
protein interaction networks, this work demonstrated the power of high-throughput
proteomics technologies in myriad applications for proteome-scale structural,
functional, and biomedical research.
cellular processes of life. The last decade has witnessed dramatic advances in the
field of proteomics, which broadly include characterizing the composition, structure,
functions, interactions, and modifications of numerous proteins in biological systems,
and elucidating how the miscellaneous components collectively contribute to the
phenotypes associated with various disorders. Such large-scale proteomics studies
have steadily gained momentum with the evolution of diverse high-throughput
technologies. This work illustrates the development of novel high-throughput
proteomics platforms and their applications in translational and structural biology. In
Chapter 1, nucleic acid programmable protein arrays displaying the human
proteomes were applied to immunoprofiling of paired serum and cerebrospinal fluid
samples from patients with Alzheimer’s disease. This high-throughput
immunoproteomic approach allows us to investigate the global antibody responses
associated with Alzheimer’s disease and potentially identify the diagnostic
autoantibody biomarkers. In Chapter 2, a versatile proteomic pipeline based on the
baculovirus-insect cell expression system was established to enable high-throughput
gene cloning, protein production, in vivo crystallization and sample preparation for Xray diffraction. In conjunction with the advanced crystallography methods, this endto-end pipeline promises to substantially facilitate the protein structural
determination. In Chapter 3, modified nucleic acid programmable protein arrays
were developed and used for probing protein-protein interactions at the proteome
level. From the perspective of biomarker discovery, structural proteomics, and
protein interaction networks, this work demonstrated the power of high-throughput
proteomics technologies in myriad applications for proteome-scale structural,
functional, and biomedical research.
ContributorsTang, Yanyang (Author) / LaBaer, Joshua (Thesis advisor) / Anderson, Karen S (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2020
Description
Functional regression models are widely considered in practice. To precisely understand an underlying functional mechanism, a good sampling schedule for collecting informative functional data is necessary, especially when data collection is limited. However, scarce research has been conducted on the optimal sampling schedule design for the functional regression model so far. To address this design issue, efficient approaches are proposed for generating the best sampling plan in the functional regression setting. First, three optimal experimental designs are considered under a function-on-function linear model: the schedule that maximizes the relative efficiency for recovering the predictor function, the schedule that maximizes the relative efficiency for predicting the response function, and the schedule that maximizes the mixture of the relative efficiencies of both the predictor and response functions. The obtained sampling plan allows a precise recovery of the predictor function and a precise prediction of the response function. The proposed approach can also be reduced to identify the optimal sampling plan for the problem with a scalar-on-function linear regression model. In addition, the optimality criterion on predicting a scalar response using a functional predictor is derived when the quadratic relationship between these two variables is present, and proofs of important properties of the derived optimality criterion are also provided. To find such designs, an algorithm that is comparably fast, and can generate nearly optimal designs is proposed. As the optimality criterion includes quantities that must be estimated from prior knowledge (e.g., a pilot study), the effectiveness of the suggested optimal design highly depends on the quality of the estimates. However, in many situations, the estimates are unreliable; thus, a bootstrap aggregating (bagging) approach is employed for enhancing the quality of estimates and for finding sampling schedules stable to the misspecification of estimates. Through case studies, it is demonstrated that the proposed designs outperform other designs in terms of accurately predicting the response and recovering the predictor. It is also proposed that bagging-enhanced design generates a more robust sampling design under the misspecification of estimated quantities.
ContributorsRha, Hyungmin (Author) / Kao, Ming-Hung (Thesis advisor) / Pan, Rong (Thesis advisor) / Stufken, John (Committee member) / Reiser, Mark R. (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2020
Description
To maintain long term success, a manufacturing company should be managed and operated under the guidance of properly designed capacity, production and logistics plans that are formulated in coordination with its manufacturing footprint, so that its managerial goals on both strategic and tactical levels can be fulfilled. In particular, sufficient flexibility and efficiency should be ensured so that future customer demand can be met at a profit. This dissertation is motivated by an automobile manufacturer's mid-term and long-term decision problems, but applies to any multi-plant, multi-product manufacturer with evolving product portfolios and significant fixed and variable production costs. Via introducing the concepts of effective capacity and product-specific flexibility, two mixed integer programming (MIP) models are proposed to help manufacturers shape their mid-term capacity plans and long-term product allocation plans. With fixed tooling flexibility, production and logistics considerations are integrated into a mid-term capacity planning model to develop well-informed and balanced tactical plans, which utilize various capacity adjustment options to coordinate production, inventory, and shipping schedules throughout the planning horizon so that overall operational and capacity adjustment costs are minimized. For long-term product allocation planning, strategic tooling configuration plans that empower the production of multi-generation products at minimal configuration and operational costs are established for all plants throughout the planning horizon considering product-specific commonality and compatibility. New product introductions and demand uncertainty over the planning horizon are incorporated. As a result, potential production sites for each product and corresponding process flexibility are determined. An efficient heuristic method is developed and shown to perform well in solution quality and computational requirements.
ContributorsYao, Xufeng (Author) / Askin, Ronald (Thesis advisor) / Sefair, Jorge (Thesis advisor) / Escobedo, Adolfo (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2021
Description
Originally conceived as a way to scaffold molecules of interest into three-dimensional (3D) crystalline lattices for X ray crystallography, the field of deoxyribonucleic acid (DNA) nanotechnology has dramatically evolved since its inception. The unique properties of DNA nanostructures have promoted their use not only for X ray crystallography, but for a suite of biomedical applications as well. The work presented in this dissertation focuses on both of these exciting applications in the field: 1) Nucleic acid nanostructures as multifunctional drug and vaccine delivery platforms, and 2) 3D DNA crystals for structure elucidation of scaffolded guest molecules.Chapter 1 illustrates how a wide variety of DNA nanostructures have been developed for the delivery of drugs and vaccine components. However, their applications are limited under physiological conditions due to their lack of stability in low salt environments, susceptibility to enzymatic degradation, and tendency for endosomal entrapment. To address these issues, Chapter 2 describes a PEGylated peptide coating molecule was designed to electrostatically adhere to and protect DNA origami nanostructures and to facilitate their cytosolic delivery by peptide-mediated endosomal escape. The development of this molecule will aid in the use of nucleic acid nanostructures for biomedical purposes, such as the delivery of messenger ribonucleic acid (mRNA) vaccine constructs. To this end, Chapter 3 discusses the fabrication of a structured mRNA nanoparticle for more cost-efficient mRNA vaccine manufacture and proposes a multi-epitope mRNA nanostructure vaccine design for targeting human papillomavirus (HPV) type 16-induced head and neck cancers.
DNA nanotechnology was originally envisioned to serve as three-dimensional scaffolds capable of positioning proteins in a rigid array for their structure elucidation by X ray crystallography. Accordingly, Chapter 4 explores design parameters, such as sequence and Holliday junction isomeric forms, for efficient crystallization of 3D DNA lattices. Furthermore, previously published DNA crystal motifs are used to site-specifically position and structurally evaluate minor groove binding molecules with defined occupancies. The results of this study provide significant advancement towards the ultimate goal of the field.
ContributorsHenry, Skylar J.W. (Author) / Stephanopoulos, Nicholas (Thesis advisor) / Anderson, Karen (Thesis advisor) / Blattman, Joseph (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2023
Description
Peptide-based vaccines represent a promising strategy to develop personalized treatments for cancer immunotherapy. Despite their specificity and low cost of production, these vaccines have had minimal success in clinical studies due to their lack of immunogenicity, creating a need for more effective vaccine designs. Adjuvants can be incorporated to enhance their immunogenicity by promoting dendritic cell activation and antigen cross-presentation. Due to their favorable size and ability to incorporate peptides and adjuvants, nanoparticles represent an advantageous platform for designing peptide vaccines. One prime example is RNA origami (RNA-OG) nanostructures, which are nucleic acid nanostructures programmed to assemble into uniform shapes and sizes. These stable nanostructures can rationally incorporate small molecules giving them a wide array of functions. Furthermore, RNA-OG itself can function as an adjuvant to stimulate innate immune cells. In the following study, self-adjuvanted RNA-OG was employed as a vaccine assembly platform, incorporating tumor peptides onto the nanostructure to design RNA-OG-peptide nanovaccines for cancer immunotherapy. RNA-OG-peptide was found to induce dendritic cell activation and antigen cross-presentation, which mobilized tumor-specific cytotoxic T cells to elicit protective anti-tumor immunity in tumor-bearing mice. These findings demonstrate the therapeutic potential of RNA-OG as a stable, carrier-free nanovaccine platform. In an attempt to further enhance the efficacy by optimizing the amount of peptides assembled, RNA-OG was complexed with polylysine-linked peptides, a simple strategy that allowed peptide amounts to be varied. Interestingly, increasing the peptide load led to decreased vaccine efficacy, which was correlated with an ineffective CD8+ T cell response. On the other hand, the vaccine efficacy was improved by decreasing the amount of peptide loaded onto RNA-OG, which may have attributed to greater complex stability compared to the high peptide load. These results highlight a simple strategy that can be used to optimize vaccine efficacy by altering the load of assembled peptides. These studies advance our understanding of RNA-OG as a peptide vaccine platform and provide various strategies to improve the design of peptide vaccines for translation into cancer immunotherapy.
ContributorsYip, Theresa (Author) / Chang, Yung (Thesis advisor) / Borges Florsheim, Esther (Committee member) / Lake, Douglas (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2024
Description
This dissertation contributes to uncertainty-aware neural networks using multi-modality data, with a focus on industrial and aviation applications. Drawing from seminal works in recent years that have significantly advanced the field, this dissertation develops techniques for incorporating uncertainty estimation and leveraging multi-modality information into neural networks for tasks such as fault detection and environmental perception. The escalating complexity of data in engineering contexts demands models that predict accurately and quantify uncertainty in these predictions. The methods proposed in this document utilize various techniques, including Bayesian Deep Learning, multi-task regularization and feature fusion, and efficient use of unlabeled data. Popular methods of uncertainty quantification are analyzed empirically to derive important insights on their use in real world engineering problems. The primary objective is to develop and refine Bayesian neural network models for enhanced predictive accuracy and decision support in engineering. This involves exploring novel architectures, regularization methods, and data fusion techniques. Significant attention is given to data handling challenges in deep learning, particularly in the context of quality inspection systems. The research integrates deep learning with vision systems for engineering risk assessment and decision support tasks, and introduces two novel benchmark datasets designed for semantic segmentation and classification tasks. Additionally, the dissertation delves into RGB-Depth data fusion for pipeline defect detection and the use of semi-supervised learning algorithms for manufacturing inspection tasks with imaging data. The dissertation contributes to bridging the gap between advanced statistical methods and practical engineering applications.
ContributorsRathnakumar, Rahul (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Jayasuriya, Suren (Committee member) / Zhuang, Houlong (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2024