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 120
Description
Telomerase is a specialized enzyme that adds telomeric DNA repeats to the chromosome ends to counterbalance the progressive telomere shortening over cell divisions. It has two essential core components, a catalytic telomerase reverse transcriptase protein (TERT), and a telomerase RNA (TR). TERT synthesizes telomeric DNA by reverse transcribing a short template sequence in TR. Unlike TERT, TR is extremely divergent in size, sequence and structure and has only been identified in three evolutionarily distant groups. The lack of knowledge on TR from important model organisms has been a roadblock for vigorous studies on telomerase regulation. To address this issue, a novel in vitro system combining deep-sequencing and bioinformatics search was developed to discover TR from new phylogenetic groups. The system has been validated by the successful identification of TR from echinoderm purple sea urchin Strongylocentrotus purpuratus. The sea urchin TR (spTR) is the first invertebrate TR that has been identified and can serve as a model for understanding how the vertebrate TR evolved with vertebrate-specific traits. By using phylogenetic comparative analysis, the secondary structure of spTR was determined. The spTR secondary structure reveals unique sea urchin specific structure elements as well as homologous structural features shared by TR from other organisms. This study enhanced the understanding of telomerase mechanism and the evolution of telomerase RNP. The system that was used to identity telomerase RNA can be employed for the discovery of other TR as well as the discovery of novel RNA from other RNP complex.
ContributorsLi, Yang (Author) / Chen, Julian Jl (Thesis advisor) / Yan, Hao (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2011
Description
Mostly, manufacturing tolerance charts are used these days for manufacturing tolerance transfer but these have the limitation of being one dimensional only. Some research has been undertaken for the three dimensional geometric tolerances but it is too theoretical and yet to be ready for operator level usage. In this research, a new three dimensional model for tolerance transfer in manufacturing process planning is presented that is user friendly in the sense that it is built upon the Coordinate Measuring Machine (CMM) readings that are readily available in any decent manufacturing facility. This model can take care of datum reference change between non orthogonal datums (squeezed datums), non-linearly oriented datums (twisted datums) etc. Graph theoretic approach based upon ACIS, C++ and MFC is laid out to facilitate its implementation for automation of the model. A totally new approach to determining dimensions and tolerances for the manufacturing process plan is also presented. Secondly, a new statistical model for the statistical tolerance analysis based upon joint probability distribution of the trivariate normal distributed variables is presented. 4-D probability Maps have been developed in which the probability value of a point in space is represented by the size of the marker and the associated color. Points inside the part map represent the pass percentage for parts manufactured. The effect of refinement with form and orientation tolerance is highlighted by calculating the change in pass percentage with the pass percentage for size tolerance only. Delaunay triangulation and ray tracing algorithms have been used to automate the process of identifying the points inside and outside the part map. Proof of concept software has been implemented to demonstrate this model and to determine pass percentages for various cases. The model is further extended to assemblies by employing convolution algorithms on two trivariate statistical distributions to arrive at the statistical distribution of the assembly. Map generated by using Minkowski Sum techniques on the individual part maps is superimposed on the probability point cloud resulting from convolution. Delaunay triangulation and ray tracing algorithms are employed to determine the assembleability percentages for the assembly.
ContributorsKhan, M Nadeem Shafi (Author) / Phelan, Patrick E (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Farin, Gerald (Committee member) / Roberts, Chell (Committee member) / Henderson, Mark (Committee member) / Arizona State University (Publisher)
Created2011
Description
Nucleosomes are the basic repetitive unit of eukaryotic chromatin and are responsible for packing DNA inside the nucleus of the cell. They consist of a complex of eight histone proteins (two copies of four proteins H2A, H2B, H3 and H4) around which 147 base pairs of DNA are wrapped in ~1.67 superhelical turns. Although the nucleosomes are stable protein-DNA complexes, they undergo spontaneous conformational changes that occur in an asynchronous fashion. This conformational dynamics, defined by the "site-exposure" model, involves the DNA unwrapping from the protein core and exposing itself transiently before wrapping back. Physiologically, this allows regulatory proteins to bind to their target DNA sites during cellular processes like replication, DNA repair and transcription. Traditional biochemical assays have stablished the equilibrium constants for the accessibility to various sites along the length of the nucleosomal DNA, from its end to the middle of the dyad axis. Using fluorescence correlation spectroscopy (FCS), we have established the position dependent rewrapping rates for nucleosomes. We have also used Monte Carlo simulation methods to analyze the applicability of FRET fluctuation spectroscopy towards conformational dynamics, specifically motivated by nucleosome dynamics. Another important conformational change that is involved in cellular processes is the disassembly of nucleosome into its constituent particles. The exact pathway adopted by nucleosomes is still not clear. We used dual color fluorescence correlation spectroscopy to study the intermediates during nucleosome disassembly induced by changing ionic strength. Studying the nature of nucleosome conformational change and the kinetics is very important in understanding gene expression. The results from this thesis give a quantitative description to the basic unit of the chromatin.
ContributorsGurunathan, Kaushik (Author) / Levitus, Marcia (Thesis advisor) / Lindsay, Stuart (Committee member) / Woodbury, Neal (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
A major goal of synthetic biology is to recapitulate emergent properties of life. Despite a significant body of work, a longstanding question that remains to be answered is how such a complex system arose? In this dissertation, synthetic nucleic acid molecules with alternative sugar-phosphate backbones were investigated as potential ancestors of DNA and RNA. Threose nucleic acid (TNA) is capable of forming stable helical structures with complementary strands of itself and RNA. This provides a plausible mechanism for genetic information transfer between TNA and RNA. Therefore TNA has been proposed as a potential RNA progenitor. Using molecular evolution, functional sequences were isolated from a pool of random TNA molecules. This implicates a possible chemical framework capable of crosstalk between TNA and RNA. Further, this shows that heredity and evolution are not limited to the natural genetic system based on ribofuranosyl nucleic acids. Another alternative genetic system, glycerol nucleic acid (GNA) undergoes intrasystem pairing with superior thermalstability compared to that of DNA. Inspired by this property, I demonstrated a minimal nanostructure composed of both left- and right-handed mirro image GNA. This work suggested that GNA could be useful as promising orthogonal material in structural DNA nanotechnology.
ContributorsZhang, Su (Author) / Chaut, John C (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
The F1Fo ATP synthase is required for energy conversion in almost all living organisms. The F1 complex is a molecular motor that uses ATP hydrolysis to drive rotation of the γ–subunit. It has not been previously possible to resolve the speed and position of the γ–subunit of the F1–ATPase as it rotates during a power stroke. The single molecule experiments presented here measured light scattered from 45X91 nm gold nanorods attached to the γ–subunit that provide an unprecedented 5 μs resolution of rotational position as a function of time. The product of velocity and drag, which were both measured directly, resulted in an average torque of 63±8 pN nm for the Escherichia coli F1-ATPase that was determined to be independent of the load. The rotational velocity had an initial (I) acceleration phase 15° from the end of the catalytic dwell, a slow (S) acceleration phase during ATP binding/ADP release (15°–60°), and a fast (F) acceleration phase (60°–90°) containing an interim deceleration (ID) phase (75°–82°). High ADP concentrations decreased the velocity of the S phase proportional to 'ADP-release' dwells, and the F phase proportional to the free energy derived from the [ADP][Pi]/[ATP] chemical equilibrium. The decreased affinity for ITP increased ITP-binding dwells by 10%, but decreased velocity by 40% during the S phase. This is the first direct evidence that nucleotide binding contributes to F1–ATPase torque. Mutations that affect specific phases of rotation were identified, some in regions of F1 previously considered not to contribute to rotation. Mutations βD372V and γK9I increased the F phase velocity, and γK9I increased the depth of the ID phase. The conversion between S and F phases was specifically affected by γQ269L. While βT273D, βD305E, and αR283Q decreased the velocity of all phases, decreases in velocity due to βD302T, γR268L and γT82A were confined to the I and S phases. The correlations between the structural locations of these mutations and the phases of rotation they affect provide new insight into the molecular basis for F1–ATPase γ-subunit rotation.
ContributorsMartin, James (Author) / Frasch, Wayne D (Thesis advisor) / Chandler, Douglas (Committee member) / Gaxiola, Roberto (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2012
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
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
Solution conformations and dynamics of proteins and protein-DNA complexes are often difficult to predict from their crystal structures. The crystal structure only shows a snapshot of the different conformations these biological molecules can have in solution. Multiple different conformations can exist in solution and potentially have more importance in the biological activity. DNA sliding clamps are a family of proteins with known crystal structures. These clamps encircle the DNA and enable other proteins to interact more efficiently with the DNA. Eukaryotic PCNA and prokaryotic β clamp are two of these clamps, some of the most stable homo-oligomers known. However, their solution stability and conformational equilibrium have not been investigated in depth before. Presented here are the studies involving two sliding clamps: yeast PCNA and bacterial β clamp. These studies show that the β clamp has a very different solution stability than PCNA. These conclusions were reached through various different fluorescence-based experiments, including fluorescence correlation spectroscopy (FCS), Förster resonance energy transfer (FRET), single molecule fluorescence, and various time resolved fluorescence techniques. Interpretations of these, and all other, fluorescence-based experiments are often affected by the properties of the fluorophores employed. Often the fluorescence properties of these fluorophores are influenced by their microenvironments. Fluorophores are known to sometimes interact with biological molecules, and this can have pronounced effects on the rotational mobility and photophysical properties of the dye. Misunderstanding the effect of these photophysical and rotational properties can lead to a misinterpretation of the obtained data. In this thesis, photophysical behaviors of various organic dyes were studied in the presence of deoxymononucleotides to examine more closely how interactions between fluorophores and DNA bases can affect fluorescent properties. Furthermore, the properties of cyanine dyes when bound to DNA and the effect of restricted rotation on FRET are presented in this thesis. This thesis involves studying fluorophore photophysics in various microenvironments and then expanding into the solution stability and dynamics of the DNA sliding clamps.
ContributorsRanjit, Suman (Author) / Levitus, Marcia (Thesis advisor) / Lindsay, Stuart (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2013