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Description
The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013
Description
Surface plasmon resonance (SPR) has emerged as a popular technique for elucidating subtle signals from biological events in a label-free, high throughput environment. The efficacy of conventional SPR sensors, whose signals are mass-sensitive, diminishes rapidly with the size of the observed target molecules. The following work advances the current SPR sensor paradigm for the purpose of small molecule detection. The detection limits of two orthogonal components of SPR measurement are targeted: speed and sensitivity. In the context of this report, speed refers to the dynamic range of measured kinetic rate constants, while sensitivity refers to the target molecule mass limitation of conventional SPR measurement. A simple device for high-speed microfluidic delivery of liquid samples to a sensor surface is presented to address the temporal limitations of conventional SPR measurement. The time scale of buffer/sample switching is on the order of milliseconds, thereby minimizing the opportunity for sample plug dispersion. The high rates of mass transport to and from the central microfluidic sensing region allow for SPR-based kinetic analysis of binding events with dissociation rate constants (kd) up to 130 s-1. The required sample volume is only 1 μL, allowing for minimal sample consumption during high-speed kinetic binding measurement. Charge-based detection of small molecules is demonstrated by plasmonic-based electrochemical impedance microscopy (P-EIM). The dependence of surface plasmon resonance (SPR) on surface charge density is used to detect small molecules (60-120 Da) printed on a dextran-modified sensor surface. The SPR response to an applied ac potential is a function of the surface charge density. This optical signal is comprised of a dc and an ac component, and is measured with high spatial resolution. The amplitude and phase of local surface impedance is provided by the ac component. The phase signal of the small molecules is a function of their charge status, which is manipulated by the pH of a solution. This technique is used to detect and distinguish small molecules based on their charge status, thereby circumventing the mass limitation (~100 Da) of conventional SPR measurement.
ContributorsMacGriff, Christopher Assiff (Author) / Tao, Nongjian (Thesis advisor) / Wang, Shaopeng (Committee member) / LaBaer, Joshua (Committee member) / Chae, Junseok (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures.
ContributorsMielke, Clinton (Author) / Mandarino, Lawrence (Committee member) / LaBaer, Joshua (Committee member) / Magee, D. Mitchell (Committee member) / Dinu, Valentin (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
Description
A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values are computed at each level within a sample. If at least one of the P-values is less than a pre-specified significance level, the chart signals out-of-control. The primary advantage of our approach is that only one control chart is required to monitor several parameters simultaneously: the intercept, slope(s), and the error standard deviation. A comprehensive comparison of the proposed method and the existing KMW-Shewhart method for monitoring linear profiles is conducted. In addition, the effect that the number of observations within a sample has on the performance of the proposed method is investigated. The proposed method was also compared to the T^2 method discussed in Kang and Albin (2000) for multivariate, polynomial, and nonlinear profiles. A simulation study shows that overall the proposed P-value method performs satisfactorily for different profile types.
ContributorsAdibi, Azadeh (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Li, Jing (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Recombinant protein expression is essential to biotechnology and molecular medicine, but facile methods for obtaining significant quantities of folded and functional protein in mammalian cell culture have been lacking. Here I describe a novel 37-nucleotide in vitro selected sequence that promotes unusually high transgene expression in a vaccinia driven cytoplasmic expression system. Vectors carrying this sequence in a monocistronic reporter plasmid produce >1,000-fold more protein than equivalent vectors with conventional vaccinia promoters. Initial mechanistic studies indicate that high protein expression results from dual activity that impacts both transcription and translation. I suggest that this motif represents a powerful new tool in vaccinia-based protein expression and vaccine development technology.
ContributorsFlores, Julia Anne (Author) / Chaput, John C (Thesis advisor) / Jacobs, Bertram (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2012
Description
Rapid advance in sensor and information technology has resulted in both spatially and temporally data-rich environment, which creates a pressing need for us to develop novel statistical methods and the associated computational tools to extract intelligent knowledge and informative patterns from these massive datasets. The statistical challenges for addressing these massive datasets lay in their complex structures, such as high-dimensionality, hierarchy, multi-modality, heterogeneity and data uncertainty. Besides the statistical challenges, the associated computational approaches are also considered essential in achieving efficiency, effectiveness, as well as the numerical stability in practice. On the other hand, some recent developments in statistics and machine learning, such as sparse learning, transfer learning, and some traditional methodologies which still hold potential, such as multi-level models, all shed lights on addressing these complex datasets in a statistically powerful and computationally efficient way. In this dissertation, we identify four kinds of general complex datasets, including "high-dimensional datasets", "hierarchically-structured datasets", "multimodality datasets" and "data uncertainties", which are ubiquitous in many domains, such as biology, medicine, neuroscience, health care delivery, manufacturing, etc. We depict the development of novel statistical models to analyze complex datasets which fall under these four categories, and we show how these models can be applied to some real-world applications, such as Alzheimer's disease research, nursing care process, and manufacturing.
ContributorsHuang, Shuai (Author) / Li, Jing (Thesis advisor) / Askin, Ronald (Committee member) / Ye, Jieping (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2012
Description
This study was conducted to observe the effects of vitamin C supplementation upon the expression of sICAM-1 in asthmatic subject. Two groups were created, each with a sample size of 4 subjects. One group was the vitamin C group (VC) and the other was the placebo group (PL). The study was analyzed through observing concentrations of biomolecules present within samples of blood plasma and nasal lavages. These included vitamin C, sICAM-1 expression, and histamine. The following P-values calculated from the data collected from this study. The plasma vitamin C screening was p=0.3, and after 18 days of supplementation, p=0.03. For Nasal ICAM p=0.5 at Day 0, p=0.4 at Day 4, and p=0.9 at Day 18. For the Histamine samples p=0.9 at Day 0 and p=0.9 at Day 18. The following P-values calculated from the data collected from both studies. The plasma vitamin C screening was p=0.8, and after 18 days of supplementation, p=0.03. The change of vitamin C at the end of this study and the combined data both had a P-value that was calculated to be lower than 0.05, which meant that this change was significant because it was due to the intervention and not chance. For Nasal ICAM samples p=0.7 at Day 0, p=0.7 at Day 4, and p=1 at Day 18. For the Histamine p=0.7 at Day 0 and p=0.9 at Day 18. This study carries various implications although the study data was unable to show much significance. This was the second study to test this, and as more research is done, and the sample size grows, one will be able to observe whether this really is the mechanism through which vitamin C plays a role in immunological functions.
ContributorsKapadia, Chirag Vinay (Author) / Johnston, Carol (Thesis director) / LaBaer, Joshua (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
The Department of Defense (DoD) acquisition system is a complex system riddled with cost and schedule overruns. These cost and schedule overruns are very serious issues as the acquisition system is responsible for aiding U.S. warfighters. Hence, if the acquisition process is failing that could be a potential threat to our nation's security. Furthermore, the DoD acquisition system is responsible for proper allocation of billions of taxpayer's dollars and employs many civilians and military personnel. Much research has been done in the past on the acquisition system with little impact or success. One reason for this lack of success in improving the system is the lack of accurate models to test theories. This research is a continuation of the effort on the Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation modeling research on DoD acquisition system. We propose to extend ERAM using agent-based simulation principles due to the many interactions among the subsystems of the acquisition system. We initially identify ten sub models needed to simulate the acquisition system. This research focuses on three sub models related to the budget of acquisition programs. In this thesis, we present the data collection, data analysis, initial implementation, and initial validation needed to facilitate these sub models and lay the groundwork for a full agent-based simulation of the DoD acquisition system.
ContributorsBucknell, Sophia Robin (Author) / Wu, Teresa (Thesis director) / Li, Jing (Committee member) / Colombi, John (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Colorectal cancer (CRC) is one of the most highly diagnosed cancers in the United States and accounts for 9.5% of all new cancer cases worldwide. With a 50% five-year prognosis, it is the second highest cancerous cause of death in the U.S. CRC tumors express antigens that are capable of inducing an immune response. The identification of autoantibodies (AAb) against tumor-associated antigens (TAA) may facilitate personalized tumor treatment in the form of targeted immunotherapy. The objective of this study was to observe the AAb expression raised against a 2000 human gene survey in late-stage colorectal cancer using the Nucleic Acid Programmable Protein Arrays (NAPPA). AAbs from serum samples were collected from 80 patients who died within 24 months of their last blood draw and 80 age and gender matched healthy control were profiled using NAPPA. TAA p53, a well-established protein that is one of the most highly mutated across a variety of cancers, was one of the top candidates based on statistical analysis, which, along with its family proteins p63 and p73 (which showed inverse AAb response profiles) warranted further testing via RAPID ELISA. Statistical analysis from these results revealed an inverse differential relationship between p53 and p63, in which p53 seropositivity was higher in patients than in controls, while the opposite was unexpectedly the case for p63. This study involving the AAb immunoprofiling of advanced stage CRC patients is one of the first to shed light on the high-throughput feasibility of immunoproteomic experiments using protein arrays as well as the identification of immunotherapy targets in a more rapid move towards specialized treatment of advanced CRC.
ContributorsSzeto, Emily (Author) / LaBaer, Joshua (Thesis director) / Qiu, Ji (Committee member) / Demirkan, Gokhan (Committee member) / Barrett, The Honors College (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor)
Created2014-12