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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
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
Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinson’s Disease (PD) and radiomics for glioblastoma.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
ContributorsYoon, Hyunsoo (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland S. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Currently in synthetic biology only the Las, Lux, and Rhl quorum sensing pathways have been adapted for broad engineering use. Quorum sensing allows a means of cell to cell communication in which a designated sender cell produces quorum sensing molecules that modify gene expression of a designated receiver cell. While useful, these three quorum sensing pathways exhibit a nontrivial level of crosstalk, hindering robust engineering and leading to unexpected effects in a given design. To address the lack of orthogonality among these three quorum sensing pathways, previous scientists have attempted to perform directed evolution on components of the quorum sensing pathway. While a powerful tool, directed evolution is limited by the subspace that is defined by the protein. For this reason, we take an evolutionary biology approach to identify new orthogonal quorum sensing networks and test these networks for cross-talk with currently-used networks. By charting characteristics of acyl homoserine lactone (AHL) molecules used across quorum sensing pathways in nature, we have identified favorable candidate pathways likely to display orthogonality. These include Aub, Bja, Bra, Cer, Esa, Las, Lux, Rhl, Rpa, and Sin, which we have begun constructing and testing. Our synthetic circuits express GFP in response to a quorum sensing molecule, allowing quantitative measurement of orthogonality between pairs. By determining orthogonal quorum sensing pairs, we hope to identify and adapt novel quorum sensing pathways for robust use in higher-order genetic circuits.
ContributorsMuller, Ryan (Author) / Haynes, Karmella (Thesis director) / Wang, Xiao (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor)
Created2015-05
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
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
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
Acoustic emission (AE) signals have been widely employed for tracking material properties and structural characteristics. In this study, the aim is to analyze the AE signals gathered during a scanning probe lithography process to classify the known microstructure types and discover unknown surface microstructures/anomalies. To achieve this, a Hidden Markov Model is developed to consider the temporal dependency of the high-resolution AE data. Furthermore, the posterior classification probability and the negative likelihood score for microstructure classification and discovery are computed. Subsequently, a diagnostic procedure to identify the dominant AE frequencies that were used to track the microstructural characteristics is presented. In addition, machine learning methods such as KNN, Naive Bayes, and Logistic Regression classifiers are applied. Finally, the proposed approach applied to identify the surface microstructures of additively manufactured Ti-6Al-4V and show that it not only achieved a high classification accuracy (e.g., more than 90\%) but also correctly identified the microstructural anomalies that may be subjected to further investigation to discover new material phases/properties.
ContributorsSun, Huifeng (Author) / Yan, Hao (Thesis advisor) / Fricks, John (Thesis advisor) / Cheng, Dan (Committee member) / Arizona State University (Publisher)
Created2020
Description
High-dimensional data is omnipresent in modern industrial systems. An imaging sensor in a manufacturing plant a can take images of millions of pixels or a sensor may collect months of data at very granular time steps. Dimensionality reduction techniques are commonly used for dealing with such data. In addition, outliers typically exist in such data, which may be of direct or indirect interest given the nature of the problem that is being solved. Current research does not address the interdependent nature of dimensionality reduction and outliers. Some works ignore the existence of outliers altogether—which discredits the robustness of these methods in real life—while others provide suboptimal, often band-aid solutions. In this dissertation, I propose novel methods to achieve outlier-awareness in various dimensionality reduction methods. The problem is considered from many different angles depend- ing on the dimensionality reduction technique used (e.g., deep autoencoder, tensors), the nature of the application (e.g., manufacturing, transportation) and the outlier structure (e.g., sparse point anomalies, novelties).
ContributorsSergin, Nurettin Dorukhan (Author) / Yan, Hao (Thesis advisor) / Li, Jing (Committee member) / Wu, Teresa (Committee member) / Tsung, Fugee (Committee member) / Arizona State University (Publisher)
Created2021