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- Genre: Doctoral Dissertation
- Genre: Masters Thesis
Description
Nature is a master at organizing biomolecules in all intracellular processes, and researchers have conducted extensive research to understand the way enzymes interact with each other through spatial and orientation positioning, substrate channeling, compartmentalization, and more.
DNA nanostructures of high programmability and complexity provide excellent scaffolds to arrange multiple molecular/macromolecular components at nanometer scale to construct interactive biomolecular complexes and networks. Due to the sequence specificity at different positions of the DNA origami nanostructures, spatially addressable molecular pegboard with a resolution of several nm (less than 10 nm) can be achieved. So far, DNA nanostructures can be used to build nanodevices ranging from in vitro small molecule biosensing to sophisticated in vivo therapeutic drug delivery systems and multi-enzyme networks.
This thesis focuses on how to use DNA nanostructures as programmable biomolecular scaffolds to arranges enzymatic systems. Presented here are a series of studies toward this goal. First, we survey approaches used to generate protein-DNA conjugates and the use of structural DNA nanotechnology to engineer rationally designed nanostructures. Second, novel strategies for positioning enzymes on DNA nanoscaffolds has been developed and optimized, including site-specific/ non site-specific protein-DNA conjugation, purification and characterization. Third, an artificial swinging arm enzyme-DNA complex has been developed to mimic substrate channeling process. Finally, we extended to build a artificial 2D multi-enzyme network.
DNA nanostructures of high programmability and complexity provide excellent scaffolds to arrange multiple molecular/macromolecular components at nanometer scale to construct interactive biomolecular complexes and networks. Due to the sequence specificity at different positions of the DNA origami nanostructures, spatially addressable molecular pegboard with a resolution of several nm (less than 10 nm) can be achieved. So far, DNA nanostructures can be used to build nanodevices ranging from in vitro small molecule biosensing to sophisticated in vivo therapeutic drug delivery systems and multi-enzyme networks.
This thesis focuses on how to use DNA nanostructures as programmable biomolecular scaffolds to arranges enzymatic systems. Presented here are a series of studies toward this goal. First, we survey approaches used to generate protein-DNA conjugates and the use of structural DNA nanotechnology to engineer rationally designed nanostructures. Second, novel strategies for positioning enzymes on DNA nanoscaffolds has been developed and optimized, including site-specific/ non site-specific protein-DNA conjugation, purification and characterization. Third, an artificial swinging arm enzyme-DNA complex has been developed to mimic substrate channeling process. Finally, we extended to build a artificial 2D multi-enzyme network.
ContributorsYang, Yuhe Renee (Author) / Yan, Hao (Thesis advisor) / Liu, Yan (Thesis advisor) / Chen, Julian (Committee member) / Hayes, Mark (Committee member) / Arizona State University (Publisher)
Created2016
Description
DNA nanotechnology has been a rapidly growing research field in the recent decades, and there have been extensive efforts to construct various types of highly programmable and robust DNA nanostructures. Due to the advantage that DNA nanostructure can be used to organize biochemical molecules with precisely controlled spatial resolution, herein we used DNA nanostructure as a scaffold for biological applications. Targeted cell-cell interaction was reconstituted through a DNA scaffolded multivalent bispecific aptamer, which may lead to promising potentials in tumor therapeutics. In addition a synthetic vaccine was constructed using DNA nanostructure as a platform to assemble both model antigen and immunoadjuvant together, and strong antibody response was demonstrated in vivo, highlighting the potential of DNA nanostructures to serve as a new platform for vaccine construction, and therefore a DNA scaffolded hapten vaccine is further constructed and tested for its antibody response. Taken together, my research demonstrated the potential of DNA nanostructure to serve as a general platform for immunological applications.
ContributorsLiu, Xiaowei (Author) / Liu, Yan (Thesis advisor) / Chang, Yung (Thesis advisor) / Yan, Hao (Committee member) / Allen, James (Committee member) / Zhang, Peiming (Committee member) / Arizona State University (Publisher)
Created2012
Description
In eukaryotes, DNA is packed in a highly condensed and hierarchically organized structure called chromatin, in which DNA tightly wraps around the histone octamer consisting of one histone 3-histone 4 (H3-H4) tetramer and two histone 2A- histone 2B (H2A-H2B) dimers with 147 base pairs in an almost two left handed turns. Almost all DNA dependent cellular processes, such as DNA duplication, transcription, DNA repair and recombination, take place in the chromatin form. Based on the critical importance of appropriate chromatin condensation, this thesis focused on the folding behavior of the nucleosome array reconstituted using different templates with various controllable factors such as histone tail modification, linker DNA length, and DNA binding proteins. Firstly, the folding behaviors of wild type (WT) and nucleosome arrays reconstituted with acetylation on the histone H4 at lysine 16 (H4K16 (Ac)) were studied. In contrast to the sedimentation result, atomic force microscopy (AFM) measurements revealed no apparent difference in the compact nucleosome arrays between WT and H4K16 (Ac) and WT. Instead, an optimal loading of nucleosome along the template was found necessary for the Mg2+ induced nucleosome array compaction. This finding leads to the further study on the role of linker DNA in the nucleosome compaction. A method of constructing DNA templates with varied linker DNA lengths was developed, and uniformly and randomly spaced nucleosome arrays with average linker DNA lengths of 30 bp and 60 bp were constructed. After comprehensive analyses of the nucleosome arrays' structure in mica surface, the lengths of the linker DNA were found playing an important role in controlling the structural geometries of nucleosome arrays in both their extended and compact forms. In addition, higher concentration of the DNA binding domain of the telomere repeat factor 2 (TRF2) was found to stimulate the compaction of the telomeric nucleosome array. Finally, AFM was successfully applied to investigate the nucleosome positioning behaviors on the Mouse Mammary Tumor Virus (MMTV) promoter region, and two highly positioned region corresponded to nucleosome A and B were identified by this method.
ContributorsFu, Qiang (Author) / Lindsay, Stuart M (Thesis advisor) / Yan, Hao (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2010
Description
Telomerase is a special reverse transcriptase that extends the linear chromosome termini in eukaryotes. Telomerase is also a unique ribonucleoprotein complex which is composed of the protein component called Telomerase Reverse Transcriptase (TERT) and a telomerase RNA component (TR). The enzyme from most vertebrate species is able to utilize a short template sequence within TR to synthesize a long stretch of telomeric DNA, an ability termed "repeat addition processivity". By using human telomerase reconstituted both in vitro (Rabbit Reticulocyte Lysate) and in vivo (293FT cells), I have demonstrated that a conserved motif in the reverse transcriptase domain of the telomerase protein is crucial for telomerase repeat addition processivity and rate. Furthermore, I have designed a "template-free" telomerase to show that RNA/DNA duplex binding is a critical step for telomere repeat synthesis. In an attempt to expand the understanding of vertebrate telomerase, I have studied RNA-protein interactions of telomerase from teleost fish. The teleost fish telomerase RNA (TR) is by far the smallest vertebrate TR identified, providing a valuable model for structural research.
ContributorsXie, Mingyi (Author) / Chen, Julian J.L. (Thesis advisor) / Yan, Hao (Committee member) / Wachter, Rebekka M. (Committee member) / Arizona State University (Publisher)
Created2010
Description
Uncertainty quantification is critical for engineering design and analysis. Determining appropriate ways of dealing with uncertainties has been a constant challenge in engineering. Statistical methods provide a powerful aid to describe and understand uncertainties. This work focuses on applying Bayesian methods and machine learning in uncertainty quantification and prognostics among all the statistical methods. This study focuses on the mechanical properties of materials, both static and fatigue, the main engineering field on which this study focuses. This work can be summarized in the following items: First, maintaining the safety of vintage pipelines requires accurately estimating the strength. The objective is to predict the reliability-based strength using nondestructive multimodality surface information. Bayesian model averaging (BMA) is implemented for fusing multimodality non-destructive testing results for gas pipeline strength estimation. Several incremental improvements are proposed in the algorithm implementation. Second, the objective is to develop a statistical uncertainty quantification method for fatigue stress-life (S-N) curves with sparse data.Hierarchical Bayesian data augmentation (HBDA) is proposed to integrate hierarchical Bayesian modeling (HBM) and Bayesian data augmentation (BDA) to deal with sparse data problems for fatigue S-N curves. The third objective is to develop a physics-guided machine learning model to overcome limitations in parametric regression models and classical machine learning models for fatigue data analysis. A Probabilistic Physics-guided Neural Network (PPgNN) is proposed for probabilistic fatigue S-N curve estimation. This model is further developed for missing data and arbitrary output distribution problems. Fourth, multi-fidelity modeling combines the advantages of low- and high-fidelity models to achieve a required accuracy at a reasonable computation cost. The fourth objective is to develop a neural network approach for multi-fidelity modeling by learning the correlation between low- and high-fidelity models. Finally, conclusions are drawn, and future work is outlined based on the current study.
ContributorsChen, Jie (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Ren, Yi (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2022
Description
Molecular tessellation research aims to elucidate the underlying principles that govern intricate patterns in nature and to leverage these principles to create precise and ordered structures across multiple scales, thereby facilitating the emergence of novel functionalities. DNA origami technology enables the fabrication of nearly arbitrary DNA architectures with nanoscale precision, which can serve as excellent building blocks for the construction of tessellation patterns. However, the size and complexity of DNA origami tessellation systems are currently limited by several unexplored factors relevant to the accuracy of essential design parameters, the applicability of design strategies, and the compatibility between different tiles. Here, a general design and assembly method are described for creating DNA origami tiles that grow into tessellation patterns with micrometer-scale order and nanometer-scale precision. A critical design parameter, interhelical distance (D), was identified, which determined the conformation of monomer tiles and the outcome of tessellation. Finely tuned D facilitated the accurate geometric design of monomer tiles with minimized curvature and improved tessellation capability. To demonstrate the generality of the design method, 9 tile geometries and 15 unique tile designs were generated. The designed tiles were assembled into single-crystalline lattices ranging from tens to hundreds of square micrometers with micrometer-scale, nearly defect-free areas readily visualized by atomic force microscopy. Two strategies were applied to further increase the complexity of DNA origami tessellation, including reducing the symmetry of monomer tiles and co-assembling tiles of various geometries. The designed 6 complex tilings that includes 5 Archimedean tilings and a 12-fold quasicrystal tiling yielded various tiling patterns that great in size and quality, indicating the robustness of the optimized tessellation system. The described design and assembly approach can also be employed to create square DNA origami units for algorithmic self-assembly. As the square units assembled and expanded, they executed the binary function XOR, which generated the Sierpinski triangular pattern according to the predetermined instructions. This study will promote DNA-templated, programmable molecular and material patterning and open up new opportunities for applications in metamaterial engineering, nanoelectronics, and nanolithography.
ContributorsTang, Yue (Author) / Yan, Hao (Thesis advisor) / Guo, Jia (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
For multiple reasons, the consumption of fresh fruits and vegetables in the United States has progressively increased. This has resulted in increased domestic production and importation of these products. The associated logistics is complex due to the perishability of these products, and most current logistics systems rely on marketing and supply chains practices that result in high levels of food waste and limited offer diversity. For instance, given the lack of critical mass, small growers are conspicuously absent from mainstream distribution channels. One way to obtain these critical masses is using associative schemes such as co-ops. However, the success level of traditional associate schemes has been mixed at best. This dissertation develops decision support tools to facilitate the formation of coalitions of small growers in complementary production regions to act as a single-like supplier. Thus, this dissertation demonstrates the benefits and efficiency that could be achieved by these coalitions, presents a methodology to efficiently distribute the value of a new identified market opportunity among the growers participating in the coalition, and develops a negotiation framework between a buyer(s) and the agent representing the coalition that results in a prototype contract.There are four main areas of research contributions in this dissertation. The first is the development of optimization tools to allocate a market opportunity to potential production regions while considering consumer preferences for special denomination labels such as “local”, “organic”, etc. The second contribution is in the development of a stochastic optimization and revenue-distribution framework for the formation of coalitions of growers to maximize the captured value of a market opportunity. The framework considers the growers’ individual preferences and production characteristics (yields, resources, etc.) to develop supply contracts that entice their participation in the coalition. The third area is the development of a negotiation mechanism to design contracts between buyers and groups of growers considering the profit expectations and the variability of the future demand. The final contribution is the integration of these models and tools into a framework capable of transforming new market opportunities into implementable production plans and contractual agreement between the different supply chain participants.
ContributorsUlloa, Rodrigo (Author) / Villalobos, Jesus (Thesis advisor) / Fowler, John (Committee member) / Mac Cawley, Alejandro (Committee member) / Yan, Hao (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2022
Description
The biological lipid bilayer on cells or the cell membrane is a surface teeming with activity. Several membrane proteins decorate the lipid bilayer to carry out various functionalities that help a cell interact with the environment, gather resources and communicate with other cells. This provides a repertoire of biological structures and processes that can be mimicked and manipulated. Since its inception in the late 20th century deoxyribonucleic acid (DNA) nanotechnology has been used to create nanoscale objects that can be used for such purposes. Using DNA as the building material provides the user with a programmable and functionalizable tool box to design and demonstrate these ideas. In this dissertation, I describe various DNA nanostructures that can insert or interact with lipid bilayers for cargo transport, diagnostics and therapeutics. First, I describe a reversibly gated DNA nanopore of 20.4nm x 20.4nm cross sectional width. Controlled transport of cargoes of various sizes across a lipid bilayer through a channel formed by the DNA nanopore was demonstrated. This demonstration paves the way for a class of nanopores that can be activated by different stimuli. The membrane insertion capability of the DNA nanopore is further utilized to design a nanopore sensor that can detect oligonucleotides of a specific s equence inside a lipid vesicle. The ease with which the sensor can be modified to i dentify different diagnostic markers for disease detection was shown by designing a sensor that can identify the non small cell lung cancer marker micro ribonucleic acid -21 (miRNA21). Finally, I demonstrate the therapeutic capabilities of DNA devices with a DNA tetrabody that can recruit natural killer cells (NK cells) to target cancer cells. The DNA tetrabody functionalized with cholesterol molecules and Her2 affibody inserts into NK cell membrane leading it to Her2 positive cancer cells. This shows that inthe presence of DNA tetrabody, the NK cell activation gets accelerated.
ContributorsAbraham, Leeza (Author) / Yan, Hao (Thesis advisor) / Liu, Uan (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Nonalcoholic Steatohepatitis (NASH) is a severe form of Nonalcoholic fatty liverdisease, that is caused due to excessive calorie intake, sedentary lifestyle and in the
absence of severe alcohol consumption. It is widely prevalent in the United States
and in many other developed countries, affecting up to 25 percent of the population.
Due to being asymptotic, it usually goes unnoticed and may lead to liver failure if
not treated at the right time.
Currently, liver biopsy is the gold standard to diagnose NASH, but being an
invasive procedure, it comes with it's own complications along with the inconvenience
of sampling repeated measurements over a period of time. Hence, noninvasive
procedures to assess NASH are urgently required. Magnetic Resonance Elastography
(MRE) based Shear Stiffness and Loss Modulus along with Magnetic Resonance
Imaging based proton density fat fraction have been successfully combined to predict
NASH stages However, their role in the prediction of disease progression still remains
to be investigated.
This thesis thus looks into combining features from serial MRE observations to
develop statistical models to predict NASH progression. It utilizes data from an experiment
conducted on male mice to develop progressive and regressive NASH and
trains ordinal models, ordered probit regression and ordinal forest on labels generated
from a logistic regression model. The models are assessed on histological data collected
at the end point of the experiment. The models developed provide a framework
to utilize a non-invasive tool to predict NASH disease progression.
ContributorsDeshpande, Eeshan (Author) / Ju, Feng (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2021
Description
National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.
ContributorsPang, Yutian (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Zhuang, Houlong (Committee member) / Marvi, Hamid (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023