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Description
The principle of Darwinian evolution has been applied in the laboratory to nucleic acid molecules since 1990, and led to the emergence of in vitro evolution technique. The methodology of in vitro evolution surveys a large number of different molecules simultaneously for a pre-defined chemical property, and enrich for molecules with the particular property. DNA and RNA sequences with versatile functions have been identified by in vitro selection experiments, but many basic questions remain to be answered about how these molecules achieve their functions. This dissertation first focuses on addressing a fundamental question regarding the molecular recognition properties of in vitro selected DNA sequences, namely whether negatively charged DNA sequences can be evolved to bind alkaline proteins with high specificity. We showed that DNA binders could be made, through carefully designed stringent in vitro selection, to discriminate different alkaline proteins. The focus of this dissertation is then shifted to in vitro evolution of an artificial genetic polymer called threose nucleic acid (TNA). TNA has been considered a potential RNA progenitor during early evolution of life on Earth. However, further experimental evidence to support TNA as a primordial genetic material is lacking. In this dissertation we demonstrated the capacity of TNA to form stable tertiary structure with specific ligand binding property, which suggests a possible role of TNA as a pre-RNA genetic polymer. Additionally, we discussed the challenges in in vitro evolution for TNA enzymes and developed the necessary methodology for future TNA enzyme evolution.
ContributorsYu, Hanyang (Author) / Chaput, John C (Thesis advisor) / Chen, Julian (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2013
Description
Due to a continued interest in the fundamental properties of dihydrofolate reductase (DHFR) and its enzymatic activities, this study employed the use of six fluorescent tryptophan derivatives, for single site amino acid replacements. The two positions 30 and 47 within DHFR were studied to discover the rate at which these larger tryptophan analogues may be incorporated. Additionally, it was to be determined how much activity the mutated DHFR’s could retain when compared to their wild type counterpart. Through a review of literature, it was shown that previous studies have illustrated successful incorporation and toleration of unnatural amino acids.
Each of the six analogues A through F were relatively efficiently incorporated into the enzyme and well tolerated. Each maintained at least a third of their catalytic activity, measured through the consumption of β-nicotinamide adenine dinucleotide phosphate. Primarily, derivatives B, C, and D were able to retain the highest amount of activity in each position; B and D were the most tolerated in positions 30 and 47 with respective values of 68 ± 6.1 and 80 ± 12. The findings in this study illustrate that single tryptophan derivatives are able to be incorporated into Escherichia coli DHFR while still allowing the maintenance of a significant portion of its enzymatic activity.
Each of the six analogues A through F were relatively efficiently incorporated into the enzyme and well tolerated. Each maintained at least a third of their catalytic activity, measured through the consumption of β-nicotinamide adenine dinucleotide phosphate. Primarily, derivatives B, C, and D were able to retain the highest amount of activity in each position; B and D were the most tolerated in positions 30 and 47 with respective values of 68 ± 6.1 and 80 ± 12. The findings in this study illustrate that single tryptophan derivatives are able to be incorporated into Escherichia coli DHFR while still allowing the maintenance of a significant portion of its enzymatic activity.
ContributorsBaldwin, Edwin Alexander (Author) / Hecht, Sidney (Thesis director) / Chen, Shengxi (Committee member) / Barrett, The Honors College (Contributor) / W. P. Carey School of Business (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Over the past few decades, medical imaging is becoming important in medicine for disease diagnosis, prognosis, treatment assessment and health monitoring. As medical imaging has progressed, imaging biomarkers are being rapidly developed for early diagnosis and staging of disease. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. While large objects can often be automatically or semi-automatically delineated, segmenting small objects (blobs) is challenging. The small object of particular interest in this dissertation are glomeruli from kidney magnetic resonance (MR) images. This problem has its unique challenges. First of all, the size of glomeruli is extremely small and very similar with noises from images. Second, there are massive of glomeruli in kidney, e.g. over 1 million glomeruli in human kidney, and the intensity distribution is heterogenous. A third recognized issue is that a large portion of glomeruli are overlapping and touched in images. The goal of this dissertation is to develop computational algorithms to identify and discover glomeruli related imaging biomarkers. The first phase is to develop a U-net joint with Hessian based Difference of Gaussians (UH-DoG) blob detector. Joining effort from deep learning alleviates the over-detection issue from Hessian analysis. Next, as extension of UH-DoG, a small blob detector using Bi-Threshold Constrained Adaptive Scales (BTCAS) is proposed. Deep learning is treated as prior of Difference of Gaussian (DoG) to improve its efficiency. By adopting BTCAS, under-segmentation issue of deep learning is addressed. The second phase is to develop a denoising convexity-consistent Blob Generative Adversarial Network (BlobGAN). BlobGAN could achieve high denoising performance and selectively denoise the image without affecting the blobs. These detectors are validated on datasets of 2D fluorescent images, 3D synthetic images, 3D MR (18 mice, 3 humans) images and proved to be outperforming the competing detectors. In the last phase, a Fréchet Descriptors Distance based Coreset approach (FDD-Coreset) is proposed for accelerating BlobGAN’s training. Experiments have shown that BlobGAN trained on FDD-Coreset not only significantly reduces the training time, but also achieves higher denoising performance and maintains approximate performance of blob identification compared with training on entire dataset.
ContributorsXu, Yanzhe (Author) / Wu, Teresa (Thesis advisor) / Iquebal, Ashif (Committee member) / Yan, Hao (Committee member) / Beeman, Scott (Committee member) / Arizona State University (Publisher)
Created2022
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
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
Deoxyribonucleic acid (DNA) has emerged as an excellent molecular building block for nanoconstruction in addition to its biological role of preserving genetic information. Its unique features such as predictable conformation and programmable intra- and inter-molecular Watson-Crick base pairing interactions make it a remarkable engineering material. A variety of convenient design rules and reliable assembly methods have been developed to engineer DNA nanostructures. The ability to create designer DNA architectures with accurate spatial control has allowed researchers to explore novel applications in directed material assembly, structural biology, biocatalysis, DNA
computing, nano-robotics, disease diagnosis, and drug delivery.
This dissertation focuses on developing the structural design rules for "static" DNA nano-architectures with increasing complexity. By using a modular self-assembly method, Archimedean tilings were achieved by association of different DNA motifs with designed arm lengths and inter-tile sticky end interactions. By employing DNA origami method, a new set of design rules was created to allow the scaffolds to travel in arbitrary directions in a designed geometry without local symmetry restrictions. Sophisticated wireframe structures of higher-order complexity were designed and constructed successfully. This dissertation also presents the use of "dynamic" DNA nanotechnology to construct DNA origami nanostructures with programmed reconfigurations.
computing, nano-robotics, disease diagnosis, and drug delivery.
This dissertation focuses on developing the structural design rules for "static" DNA nano-architectures with increasing complexity. By using a modular self-assembly method, Archimedean tilings were achieved by association of different DNA motifs with designed arm lengths and inter-tile sticky end interactions. By employing DNA origami method, a new set of design rules was created to allow the scaffolds to travel in arbitrary directions in a designed geometry without local symmetry restrictions. Sophisticated wireframe structures of higher-order complexity were designed and constructed successfully. This dissertation also presents the use of "dynamic" DNA nanotechnology to construct DNA origami nanostructures with programmed reconfigurations.
ContributorsZhang, Fei (Author) / Yan, Hao (Thesis advisor) / Liu, Yan (Thesis advisor) / Gould, Ian (Committee member) / Zhang, Peiming (Committee member) / Arizona State University (Publisher)
Created2015