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  • 1. Morrison, Kevin Topological Data Analysis and Applications to Influenza

    Master of Science, Miami University, 2020, Mathematics

    This thesis is to provide an overview of Topological Data Analysis (TDA) for the advanced undergraduate or early graduate student. This thesis assumes familiarity with basic graph theory, point-set topology, and introductory algebraic topology. Furthermore, this thesis will study phylogenetic relationship of NA gene in various Influenza A strains and determine if horizontal evolution has occurred with the strains under consideration. Furthermore, this thesis serves as a verification to the results published in a paper in 2013, in which, TDA was used to study influenza A HA and NA.
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    Committee: Daniel Farley PhD (Advisor); Paul Larson PhD (Committee Member); Ivonne Ortiz PhD (Committee Member) Subjects: Bioinformatics; Biology; Genetics; Mathematics; Virology

  • 2. King, Eshan Integrated Pharmacokinetic and Pharmacodynamic Modeling in Drug Resistance: Insights From Novel Computational and Experimental Approaches

    Doctor of Philosophy, Case Western Reserve University, 2024, Nutrition

    Drug resistance in both cancer and infectious disease is a major driver of mortality across the globe. In infectious disease, the emergence of antimicrobial resistance (AMR) outpaces our ability to develop novel drugs, and within-host evolution confounds the use of previously effective drugs during the course of treatment. In cancer, while targeted therapies have improved outcomes for some, many patients continue to face metastatic, drug-resistant disease, with limited therapeutic options available. As both disease types are driven by clonal evolution, a complementary approach to treatment that leverages tools and ideas from evolutionary biology has been beneficial. However, this evolutionary-inspired therapy has thus far been limited in its consideration of drug variation in time and space within a patient (pharmacokinetics) and variable pathogen response to drug (pharmacodynamics). In this dissertation, we describe novel computational and experimental approaches that integrate pharmacokinetics and pharmacodynamics to allow for more physically realistic models of the evolution of drug resistance. We apply these approaches to gain novel insights into drug dosing regimens and drug diffusion in tissue. In Chapters 1 and 2, we briefly review integrated pharmacokinetics and pharmacodynamics in the study of drug resistance and survey the current evidence of fitness costs to drug resistance in cancer. In Chapter 3, we developed a novel, fluorescence-based time-kill protocol for estimating drug dose-dependent death rates in bacteria. In Chapter 4, we described a software package, FEArS, that allows for efficient agent-based simulation of evolution under time-varying drug concentration. In Chapter 5, we leverage both of these methods to gain insight into why some antimicrobial treatments fail using computational modeling and simulated clinical pharmacokinetics. In Chapter 6, we use spatial agent-based modeling to examine how drug diffusion in tissue can promote tumor hetero (open full item for complete abstract)
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    Committee: Mark Chance (Committee Chair); Christopher McFarland (Committee Member); Jacob Scott (Advisor); Michael Hinzcewski (Committee Member); Drew Adams (Committee Member) Subjects: Bioinformatics; Biology; Biomedical Research; Biophysics

  • 3. Raba, Teresa Gene Family Evolution of Digestive Enzymes in the American Pika: A Comparative Genomics Analysis

    BS, Kent State University, 2024, College of Arts and Sciences / Department of Biological Sciences

    The American pika (Ochotona princeps) is an herbivorous mammal that inhabits rocky, mountainous regions across the western United States. Although they share a common ancestor with rabbits and other species in the order Lagomorpha, American pikas have a specialized diet due to an inability to migrate from their narrow habitat range. Gene families are made up of genes similar in sequence and function among species that share a common ancestor. Increases in gene copy number due to random duplication results in gene family expansion, whereas gene deletion results in family contraction. Evolutionary divergence can result in functional and genetic differences in the way that pikas and other lagomorphs digest their food with the help of enzymes. We hypothesized that American pikas have undergone lineage-specific expansions or contractions in gene families encoding enzymes (particularly digestive enzymes), allowing the species to digest available food in their narrow and changing habitat. Using the computational tool, OrthoFinder, protein sequences of the American pika were compared to seven distantly related taxa to identify gene families. CAFE5 analysis identified copy number evolution compared to the most recent common ancestor of all eight species. Functional enrichment analysis with PANTHER showed gene families related to digestive enzymes are significantly expanded in the American pika compared to other species. This indicates that protease digestive enzymes are more highly expressed in the American pika, possibly contributing to their metabolism of plants that inhabit their habitat.
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    Committee: Sangeet Lamichhaney PhD (Advisor); Rafaela Takeshita PhD (Committee Member); Mark Kershner PhD (Committee Member); Helen Piontkivska PhD (Committee Member) Subjects: Bioinformatics; Biology

  • 4. Salyer, Owen A multi-cancer study of CpG island methylator phenotype and its complex set of mutational associations

    Bachelor of Science (BS), Ohio University, 2024, Computer Science

    CpG sites occur at an irregularly high frequency at regions of the genome called CpG islands; in normal samples, CpG islands are typically unmethylated. When a number of these CpG islands are instead hypermethylated in a cancer sample the sample is referred to as possessing CpG island methylator phenotype (CIMP). CIMP has been well-defined (and was first reported) in colorectal cancers, but research has also been performed on numerous cancer types such as gliomas, melanomas, and leukemia. The goal of the work presented here is to analyze the differences in gene mutation and expression between samples with CIMP (CIMP+) and samples without CIMP (CIMP-) across multiple cancer types. First we perform a coverage analysis to find the sets of genes and mutations which best correlate with CIMP+ samples while minimizing correlation with CIMP- samples. The cancer types used in these analyses are colorectal cancer (COADREAD), stomach cancer (STAD), and uterine corpus endometrial carcinoma (UCEC). The results from each tumor type are combined to create a synthesized set of mutations and mutated genes which correlate with CIMP+ samples. We then perform differential expression analysis on RNA-Seq data from The Cancer Genome Atlas (TCGA) to determine genes that are differentially expressed in CIMP+ samples when compared to CIMP- samples. We find 890 mutations and mutated genes with strong positive correlation to CIMP, many of which are also differentially expressed between CIMP+ and CIMP- samples. Among these mutated and differentially expressed genes are numerous genes in the MSigDB KRAS down-signaling gene set. This analysis furthers our understanding of CIMP and cancer in general and may give more insight into the differences between the genomic characteristics of CIMP-positive and CIMP-negative cancers.
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    Committee: Lonnie R. Welch (Advisor) Subjects: Computer Science

  • 5. Chang, Yuzhou Immuno-informatic methods and applications in single-cell and spatial omics

    Doctor of Philosophy, The Ohio State University, 2024, Biomedical Sciences

    My interdisciplinary research integrated bioinformatics and immuno-oncology, focusing on leveraging high-dimensional biological data to understand immune cell characteristics in tumor microenvironment (TME). From the biological point of view, my focus has been on the diverse CD8+ T cell landscape of TME, especially the phenomenon of T cell exhaustion, which impairs anti-tumor immunity. My work aims to understand underlying exhaustion mechanisms and to enhance the effectiveness of immunotherapies by elucidating the heterogeneity and regulatory mechanismss of exhausted T cells using single-cell RNA-seq and spatially resolved omics data. Specifically, I validated biological hypotheses by leveraging both in-house and publicly available scRNA-seq and spatially resolved omics data. In the sex bias study, I confirmed the regulatory role of the androgen receptor in T cell exhaustion in bladder cancer. Furthermore, I also corroborated the function of the GARP-TGFβ axis in immune evasion using TCGA's bulk RNA-seq data, which indirectly promoted T cell exhaustion and undermined immunotherapy. Moving from biology to computation, I leveraged general graph representation models to learn and represent patterns of regulatory mechanisms in specific cell types and functional tissue units (FTUs) from scRNA-seq and spatially resolved omics data. I established an R package and web server, IRIS-FGM and IRIS3, to discover the regulatory pattern using scRNA-seq. Regarding spatially resolved omics data, I formulated RESEPT, a deep learning framework, to effectively characterize and visualize histological patterns and gene expression coherence in spatial domains. Furthermore, I developed SpaGFT to provide an unbiased representation method of FTU patterns. After establishing those general methods, I also fine-tuned these computational tools using specific cases and provided novel hypotheses. Refinement of IRIS3 with sex-specific scRNA-seq data led to the identification of unrecognized (open full item for complete abstract)
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    Committee: Qin Ma (Advisor); Zihai Li (Advisor); Gang Xin (Committee Member); Dongjun Chung (Committee Member) Subjects: Bioinformatics; Biomedical Research; Immunology

  • 6. Yilmaz, Serhan Robust, Fair and Accessible: Algorithms for Enhancing Proteomics and Under-Studied Proteins in Network Biology

    Doctor of Philosophy, Case Western Reserve University, 2023, EECS - Computer and Information Sciences

    This dissertation presents a comprehensive approach to advancing proteomics and under-studied proteins in network biology, emphasizing the development of reliable algorithms, fair evaluation practices, and accessible computational tools. A key contribution of this work is the introduction of RoKAI, a novel algorithm that integrates multiple sources of functional information to infer kinase activity. By capturing coordinated changes in signaling pathways, RoKAI significantly improves kinase activity inference, facilitating the identification of dysregulated kinases in diseases. This enables deeper insights into cellular signaling networks, supporting targeted therapy development and expanding our understanding of disease mechanisms. To ensure fairness in algorithm evaluation, this research carefully examines potential biases arising from the under-representation of under-studied proteins and proposes strategies to mitigate these biases, promoting a more comprehensive evaluation and encouraging the discovery of novel findings. Additionally, this dissertation focuses on enhancing accessibility by developing user-friendly computational tools. The RoKAI web application provides a convenient and intuitive interface to perform RoKAI analysis. Moreover, RokaiXplorer web tool simplifies proteomic and phospho-proteomic data analysis for researchers without specialized expertise. It enables tasks such as normalization, statistical testing, pathway enrichment, provides interactive visualizations, while also offering researchers the ability to deploy their own data browsers, promoting the sharing of findings and fostering collaborations. Overall, this interdisciplinary research contributes to proteomics and network biology by providing robust algorithms, fair evaluation practices, and accessible tools. It lays the foundation for further advancements in the field, bringing us closer to uncovering new biomarkers and potential therapeutic targets in diseases like cancer, Alzheimer' (open full item for complete abstract)
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    Committee: Mehmet Koyutürk (Committee Chair); Mark Chance (Committee Member); Vincenzo Liberatore (Committee Member); Kevin Xu (Committee Member); Michael Lewicki (Committee Member) Subjects: Bioinformatics; Biomedical Research; Computer Science

  • 7. Weaver, Davis Novel Approaches for Optimal Therapy Design in Drug-Resistant Populations

    Doctor of Philosophy, Case Western Reserve University, 2023, Systems Biology and Bioinformatics

    The current maximum tolerated dose treatment paradigm for cancer and bacteria fails to account for the capacity of these disease agents to evolve. When treatment fails to achieve rapid extinction, drug resistant clones rapidly proliferate into an uncontrollable tumor. To make significant progress for cancer patients, we need to better understand the evolutionary processes that drive cancer, and design treatments that explicitly account for them. In this dissertation, I will describe 3 projects that support the design of evolutionary therapies that explicitly account for the capacity of cancer (and bacteria) to evolve resistance in response to drug therapy. In Chapter 2, we developed two novel methods to support precision targeting of tumors; a novel leave-one-out style method for node ranking and a novel algorithm for ranking miRNA combinations that maximizes tumor disruption while minimizing toxicity. In Chapter 3, we described crosstalkr, an open-source software package to facilitate interactomic analyses. In Chapter 4, we described a novel approach for designing evolutionary therapies that leverages reinforcement learning to learn drug cycling policies given only limited information about an evolving system.
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    Committee: Mehmet Koyuturk (Committee Chair); Jacob Scott (Committee Member); Michael Hinczewski (Committee Member); George Dubyak (Committee Member) Subjects: Bioinformatics; Computer Science

  • 8. Turzo, SM Bargeen Incorporating Sparse Data to Enhance Computational Modeling of Protein Structures

    Doctor of Philosophy, The Ohio State University, 2023, Chemistry

    Protein structure prediction aids in various stages of a research pipeline and thus is of interest to both academia and industry. Computational methods, over the years, have become increasingly accurate at predicting structures of proteins. The availability of experimental data for protein structures has also improved these methods. Ion mobility (IM) coupled to native mass spectrometry techniques can provide structural information in the form of an orientationally-averaged collision cross-section (CCSIM). There are several computational methods to predict CCSIM. However, these methods are often limited by accuracy and/or computational cost. Therefore, Chapter 2 demonstrates the development of a novel method to predict CCSIM from tertiary structures of proteins. Thorough testing and analyses have been conducted on this method, revealing its combination of both accuracy and efficiency. Following the development of this CCSIM prediction method, we became interested in exploring whether we could improve the accuracy of protein structure prediction by integrating this method with the structural information encoded in the experimental CCSIM data. Therefore, in Chapter 2 we additionally developed a score function to utilize CCSIM data from ion mobility mass spectrometry (IMMS) experiments to predict protein structures from sequence. To test its efficacy, the approach was applied to a benchmark set of 25 proteins. Our results showed that all 25 proteins either improved their root-mean-square deviation (RMSD) or maintained it within 0.1 A, demonstrating the usefulness of experimental CCSIM data in structure prediction. We additionally observed an average improvement of 2.0 A RMSD (when compared to that of predictions without CCSIM data). The method developed in Chapter 2 requires users to have prior experience with the command-line interface (CLI) to predict CCSIM. Therefore, in Chapter 3 we built an intuitive web application to predict CCSIM. Our case studies from Chapte (open full item for complete abstract)
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    Committee: Steffen Lindert (Advisor); Marcos Sotomayor (Committee Member); John Herbert (Committee Member); Vicki Wysocki (Committee Member) Subjects: Biology; Biophysics; Chemistry

  • 9. Marlowe, Alicja Expression of Selected Cadherins in Adult Zebrafish Visual System and Regenerating Retina, and Microarray Analysis of Gene Expression in Protocadherin-17 Morphants

    Doctor of Philosophy, University of Akron, 2022, Integrated Bioscience

    Cadherins are cell-adhesion molecules that play important roles in animal development, maintenance and/or regeneration of adult animal tissues. In order to understand cadherins' functions in adult vertebrate visual structures, one must study their distribution in those structures. First, I examined expression of cadherin-6, cadherin-7, protocadherin-17 and protocadherin-19 in the visual structures of normal adult zebrafish using RNA in situ hybridization, followed by studying expression of two Kruppel-like transcription factors (klf6a and klf7), that are known markers for regenerating adult zebrafish retinas and optic nerves, in normal adult zebrafish brain, normal and regenerating adult zebrafish retinas. Then, I investigated expression of these cadherins in regenerating adult zebrafish retinas using both RNA in situ hybridization and quantitative PCR. Finally, as the first step in elucidating molecular mechanisms underlying protocadherin-17 (one of the cadherins that I studied) function in zebrafish visual system development, I used DNA microarray analysis to study gene expression of zebrafish embryos with their protocadherin-17 expression blocked by morpholino antisense oligonucleotides (these embryos are called protocadherin- 17 morphants). The major findings include: 1) cadherin-6, cadherin-7, protocadherin-17 and protocadherin-19 were differently expressed in the retina and major visual structures of normal adult zebrafish brain. 2) klf6a and klf7 showed similar expression patterns in most visual structures in the adult fish brain, and in regenerating retinas, but klf6a appeared to be a superior regeneration marker based on RNA in situ hybridization. 3) These four cadherin molecules showed distinct expression patterns in the regenerating zebrafish retinas. 4) Several genes involved in vision and/or visual development were significantly downregulated in the protocadherin-17 morphants compared to control embryos. My results suggest that (open full item for complete abstract)
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    Committee: Qin Liu (Advisor); Richard Londraville (Committee Member); Rolando Ramirez (Committee Member); Brian Bagatto (Committee Member); Zhong-Hui Duan (Committee Member) Subjects: Anatomy and Physiology; Bioinformatics; Biology; Developmental Biology; Molecular Biology

  • 10. Andrejek, Luke Mathematical Models Explaining Leaf Curling and Robustness via Adaxial-Abaxial Patterning in Arabidopsis

    Doctor of Philosophy, The Ohio State University, 2022, Mathematics

    Biology provides examples of complex systems whose mechanisms and properties allow organisms to develop in a highly reproducible, or robust, manner. One such system is the growth and development of leaves in Arabidopsis thaliana. The leaf development system results in thin but expansive leaves with remarkable consistency. This growth results from inputs such as gene interactions and the geometry, growth, and division of individual cells. We want to better understand how the genetic and cellular information controls leaf growth. Mathematical modeling provides tools to better understand complex biological systems. In the case of leaf growth, we can represent gene interactions and cell geometry mathematically to simulate leaf growth. We begin by constructing a one-dimensional model which describes the gene interactions in a single stationary vertical column of leaf cells. This model shows how gene interactions produce proper gene expression and identifies system components which contribute to robustness. We then expand to a two-dimensional model which describes the gene interactions in a two dimensional cross section of cells which grow and divide according to physical forces and genetic information. This model predicts the presence of an additional gene and explains how gene interactions cause perpetual cell growth and regulate leaf curling. It also predicts and explains the phenomenon that increasing levels of environmental noise preferentially result in downward curling, which we verify biologically. Together, these models help us understand the process of Arabidopsis leaf development.
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    Committee: Janet Best (Advisor); Yulong Xing (Committee Member); Adriana Dawes (Committee Member); Aman Husbands (Committee Member) Subjects: Mathematics

  • 11. Crocker, Kyle Quantitative Modeling of DNA Systems

    Doctor of Philosophy, The Ohio State University, 2021, Physics

    Here I develop computationally efficient quantitative models to describe the behavior of DNA-based systems. DNA is of fundamental biological importance, and its physical properties have been harnessed for technological applications. My work involves each of these aspects of DNA function, and thus provides broad insight into this important biomolecule. First, I examine how DNA mismaches are repaired in the cell. Protein complexes involved in DNA mismatch repair appear to diffuse along dsDNA in order to locate a hemimethylated incision site via a dissociative mechanism. I study the probability that these complexes locate a given target site via a semi-analytic, Monte Carlo calculation that tracks the association and dissociation of the complexes. I compare such probabilities to those obtained using a non-dissociative diffusive scan, and determine that for experimentally observed diffusion constants, search distances, and search durations in vitro, both search mechanisms are highly efficient for a majority of hemimethylated site distances. I then examine the space of physically realistic diffusion constants, hemimethylated site distances, and association lifetimes and determine the regions in which dissociative searching is more or less efficient than non-dissociative searching. I conclude that the dissociative search mechanism is advantageous in the majority of the physically realistic parameter space, suggesting that the dissociative search mechanism confers an evolutionary advantage. I then turn to synthetic DNA structures, initially focusing on a composite DNA nano-device. In particular, manipulation of temperature can be used to actuate DNA origami nano-hinges containing gold nanoparticles. I develop a physical model of this system that uses partition function analysis of the interaction between the nano-hinge and nanoparticle to predict the probability that the nano-hinge is open at a given temperature. The model agrees well with experimental data and pre (open full item for complete abstract)
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    Committee: Ralf Bundschuh PhD (Advisor); Carlos Castro PhD (Committee Member); Michael Poirier PhD (Committee Member); Hirata Christopher PhD (Committee Member) Subjects: Biophysics; Nanotechnology; Physics; Polymers; Theoretical Physics

  • 12. Johnson, Travis Integrative approaches to single cell RNA sequencing analysis

    Doctor of Philosophy, The Ohio State University, 2020, Biomedical Sciences

    There are trillions of cells, which make up hundreds of different cell types, found in the human body. These cells make up not only tissues but dictate the functions of those tissues. In diseased tissues, cell types can have a profound impact on the outcome of a patient. For these reasons, having a comprehensive understanding of cell types is important. In the past 10 years, single cell RNA sequencing has profoundly impacted our understanding of known and previously unknown cell types. Along with the numerous single cell datasets, a multitude of bulk expression datasets, multi-omic datasets, and curated information also exist. All of these data sources must be leveraged together to most improve our understanding of human tissues and diseases at the single cell level. We developed methodologies, frameworks, and algorithms that leverage multiple diverse datasets simultaneously to better understand single cell RNA sequencing data and as a result tissue heterogeneity as a whole.
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    Committee: Yan Zhang (Advisor); Kun Huang (Advisor); Jeffrey Parvin (Committee Member); Christopher Bartlett (Committee Member) Subjects: Bioinformatics; Biomedical Research

  • 13. Mohanty, Vakul The Role of Non-oncogenic Variants in Cancers: Onco-passengers and Germline Polymorphisms

    PhD, University of Cincinnati, 2018, Medicine: Systems Biology and Physiology

    Classically much of the focus in cancer biology has been on driver genes, i.e. tumor-suppressors and oncogenes, and how somatic mutations in these genes influence tumor phenotype. Large scale profiling studies like The Cancer Genome Atlas (TCGA) have produced massive repositories of genomic and transcriptomic data. This data has facilitated discovery and characterization of somatic drive mutations across cancers. In addition to these driver mutations cancer also have onco-passenger mutations that are passively acquired. These mutations are particularly common in tumors with extensive structural variations resulting in chromosomal deletions or amplifications. These variants are thought to target driver genes but result in copy number changes in hundreds of genes surrounding the driver genes- these genes are onco-passenger genes. Though individual instances of onco-passenger events have been characterized, a systematic understanding of their role in cancers is lacking. In addition to somatic changes, tumors also carry common heritable variants or germline polymorphisms. Recent studies have shown that these polymorphisms play critical roles in modulating gene expression across tissues. These regulatory polymorphisms are also related to numerous loci associated with predisposition to complex diseases. These studies indicate that germline polymorphisms could have a significant role in cancer biology. However, in contrast to somatic mutations we lack a systematic understanding of the functional role that germline polymorphisms play in cancers. The body of work presented here leverages the TCGA dataset, and using rigorous computational and statistical analysis hope to provide a more comprehensive picture of the role that onco-passenger genes and germline polymorphisms play in cancers.
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    Committee: Kakajan Komurov Ph.D. (Committee Chair); Christian Hong Ph.D. (Committee Member); Gang Huang Ph.D. (Committee Member); Nathan Salomonis M.D. (Committee Member); Yana Zavros Ph.D. (Committee Member) Subjects: Biology; Systematic

  • 14. Renardy, Marissa Parameter Analysis in Models of Yeast Cell Polarization and Stem Cell Lineage

    Doctor of Philosophy, The Ohio State University, 2018, Mathematics

    Understanding the effects of unknown parameters and estimating their values has become a major task in all areas of systems biology. This task is especially challenging in models that are expensive to evaluate or that contain a large number of parameters. This dissertation consists of two major parts, each one addressing the issue of parameter analysis in a different biological context. In the first chapter, we present a methodology for parameter sensitivity analysis and parameter estimation and apply this methodology to a large spatial model for yeast mating polarization. The model consists of 11 partial differential equations with 35 unknown parameters, and we seek to understand the effects of these parameters and estimate their values from experimental data. In models with such a large number of parameters, traditional methods for parameter estimation can become computationally intractable. Our methodology provides a dramatic improvement in computational efficiency from the replacement of model simulation by evaluation of a polynomial surrogate model. This allows us to perform derivative-based parameter sensitivity analysis to reduce the parameter count, followed by rapid Bayesian parameter estimation that would otherwise be prohibitively expensive to perform. We first tested our methodology on a smaller ordinary differential equation (ODE) model of the heterotrimeric G-protein cycle, which shows results consistent with published single-point parameter estimates. Then, applying our methodology to the full spatial model, we are able to reduce the parameter count via sensitivity analysis and obtain probability distributions of the 15 most sensitive parameters. We show that a wide range of parameter values permit polarization in the model. In the second chapter, we consider a compartmental ODE stem cell lineage model for tissue growth. We compare three variants of hierarchical stem cell lineage tissue models with different combinations of negative feedbacks. (open full item for complete abstract)
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    Committee: Ching-Shan Chou (Advisor); Adriana Dawes (Committee Member); Dongbin Xiu (Committee Member) Subjects: Mathematics

  • 15. Kuntala, Prashant Kumar Optimizing Biomarkers From an Ensemble Learning Pipeline

    Master of Science (MS), Ohio University, 2017, Electrical Engineering & Computer Science (Engineering and Technology)

    Understanding gene expression pattern is crucial in deciphering any observed biological phenotypes. Transcription factors (TF) are proteins that regulate genes by binding to a transcription factor binding site (TFBS) within the promoter region of a gene. Motif discovery is a computational approach that conventionally uses stochastic models, enumeration methods and many other techniques to report candidate motifs (TFBS). These methods generate similar motifs for a TF due to various reasons. Motif selection algorithms successfully identify a small set of motifs that address the specificity problem and coverage problem in motif discovery. However, these selected motifs do not always capture all the binding site preferences for a TF. This study verifies the hypothesis that motif discovery tools generate similar motifs for a transcription factor and once these variants (similar motifs) are identified, they can be used to form a super motif set, which may improve the accuracy of motif discovery. This study introduces the concept of Super motif set, a new model to accurately predict the binding sites for a TF. Two heuristic algorithms are introduced to identify Super motif sets, utilizing motif selection algorithms and a motif comparison tool. These super motif sets identified, capture the biological diversity in TFBS preferences of a TF. The algorithms are valuated on ChIP-seq data for 54 TF factor groups from the ENCODE project. Moreover, the proposed algorithms are used to optimize the motifs that are reported by motif selection algorithms and to report super motif sets in three case studies: Chagas disease, pollen specific HRGP genes in Arabidopsis thaliana and Shigellosis. On an average two motif variants are added to the selected motifs, which improve the accuracy of motif discovery.
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    Committee: Frank Drews (Advisor); Lonnie Welch (Committee Chair); Jundong Liu (Committee Member); Erin Murphy (Committee Member) Subjects: Bioinformatics; Biology; Biomedical Research; Computer Engineering; Computer Science; Genetics; Molecular Biology

  • 16. Brown, Andrew Identification of a phospho-hnRNP E1 Nucleic Acid Consensus Sequence Mediating Epithelial to Mesenchymal Transition

    PHD, Kent State University, 2015, College of Arts and Sciences / Department of Biological Sciences

    Protein translational regulation by RNA binding proteins (RBPs) is a critical process in maintaining homeostasis. Epithelial to mesenchymal transition (EMT) is a process in which epithelial cells de-differentiate and become mesenchymal, increasing the propensity toward tumorigenesis and/or metastasis. We have identified a heterogeneous nuclear riboprotein E1 (hnRNP E1)-mediated post-transcriptional operon that controls transcript-selective translational regulation of epithelial / mesenchymal transition (EMT)-associated genes. In this regulatory mechanism, hnRNPE1 binds to the 3'-UTR of select transcripts and silences their translation. TGFß reverses translational silencing through Akt2-dependent phosphorylation of hnRNP E1 at Ser-43, resulting in loss of hnRNP E1 binding to RNA. We have identified approximately forty pro-EMT / metastatic mRNAs that are regulated by this hnRNP E1 operon and our preliminary studies have revealed a short stretch of nucleic acids, that we have termed the BAT element (TGF-beta activated translational (BAT) element), present in their respective 3'-UTRs that may be responsible for hnRNP E1 binding. Herein, through the use of in-vitro and in-vivo assays, we demonstrate the contribution of BAT element mutations and constitutively high levels of pSer43 hnRNP E1 to cancer tumorigenesis and metastasis.
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    Committee: Philip Howe Ph.D. (Advisor); Derek Damron Ph.D. (Committee Member); Srinivasan Vijayaraghavan Ph.D. (Committee Member); Olena Piontkivska Ph.D. (Committee Member); Bidyut Mohanty Ph.D. (Committee Member) Subjects: Bioinformatics; Biology; Biomedical Research; Biostatistics

  • 17. Flatt, Justin STRUCTURAL INSIGHTS INTO RECOGNITION OF ADENOVIRUS BY IMMUNOLOGIC AND SERUM FACTORS

    Doctor of Philosophy, Case Western Reserve University, 2014, Pharmacology

    Adenoviruses (AdVs) are common pathogens that are a major cause of acute infections of the respiratory and intestinal tracts, as well as the eye. Despite having a distinguished and extensive experimental history, there remain many unanswered questions about how AdVs are recognized and eliminated during infection. In order to advance therapy for infectious and inherited diseases, these challenging questions must be addressed. Here we have examined recognition of AdV by immunologic and serum factors using high-resolution cryo-electron microscopy (cryo-EM) and computational modeling. These factors include human alpha defensin 5 (HD5), human blood coagulation factor X (FX), and factor VII (FVII). We also analyzed the structure of an AdV-based vaccine that is designed to provide protective immunity against human immunodeficiency virus (HIV). Structural analysis and modeling studies on HD5 recognition of AdV implicated a key role for intrinsic disorder in mediating a stabilizing interaction that blocks viral infection. Cryo-EM and functional examination of serum factor binding to AdV showed that FX, a noninflammatory humoral factor of the coagulation cascade, binds to the surface of AdV and becomes a pathogen-associated molecular pattern that, upon viral entry into liver cells, triggers activation of innate immunity via the TLR/NF-¿B pathway. In contrast, FVII does not support AdV entry into liver cells because it binds in an altered orientation compared to that of FX and dimerizes, which buries potential liver receptor binding residues within the dimer interface. Characterization of the AdV-based HIV vaccine demonstrated how the adenoviral capsid influences epitope structure, flexibility, and accessibility, all of which affect the host immune response.
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    Committee: Phoebe Stewart (Advisor) Subjects: Biology; Biophysics; Immunology; Virology

  • 18. SINHA, AMIT Discovery and Analysis of Genomic Patterns: Applications to Transcription Factor Binding and Genome Rearrangement

    PhD, University of Cincinnati, 2008, Engineering : Computer Science

    One of the most challenging open problems of the post-genomic era for computer scientists, bioinformaticians and molecular biologists is to identify and characterize all the elements involved in the gene regulation, at both the transcriptional and post-transcriptional level. The rapid availability of multiple genomes also necessitates a need for efficient computational solutions to aid in comparative syntenic analysis - the analysis of relative gene-order conservation between species - which can provide key insights into evolutionary chromosomal dynamics, rearrangement rates between species, and speciation analysis. Thus, in this dissertation, we address these issues and develop computational approaches to identify genomic patterns at both micro and macro levels.To address the problem of gene regulation, we developed algorithms for identifying transcription factor binding sites (short repeated patterns or sequence motifs) in genomes. We have developed a level based search algorithm which is able to identify regulatory motifs in a wide variety of datasets and demonstrate that our method works more efficiently than the current best methods. Further, we have also developed statistical models for identification of known motifs. Finally, we refine the motif discovery process through methods that discriminate and characterize the co-factors of a transcription factor. At macro level, we developed efficient methods (Cinteny) for fast identification of syntenic blocks with various levels of coarse graining and determine evolutionary relationships between genomes in terms of the number of rearrangements (the reversal distance). Cinteny web server integrates syntenic region browsing with evolutionary distance assessment, offers flexibility to adjust all parameters and recompute the results on-the-fly, and ability to work with user provided data.
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    Committee: Raj Bhatnagar (Advisor); Jaroslaw Meller (Committee Co-Chair); Anil Jegga (Committee Co-Chair); Ali Minai (Committee Member); Yizong Cheng (Committee Member) Subjects: Bioinformatics

  • 19. Dabdoub, Shareef Applied Visual Analytics in Molecular, Cellular, and Microbiology

    Doctor of Philosophy, The Ohio State University, 2011, Biophysics

    The current state of biological science is such that many sources of data are simply too large to be analyzed by hand. Furthermore, given the amazing breadth of investigation into the natural world, the potential for serious investigation from just mining heterogenous data sets is too rich to ignore. These two factors combined with the amount of computational power currently available make for ideal conditions from the perspective of visual analytics. Here we describe three computational projects focused on the visualization and analysis of data within the fields of microbial pathogenesis, cell biology, and molecular conformational dynamics. ProkaryMetrics is a new software package providing 3D reconstruction of fluorescent micrographs as well as various visual and statistical tools for analysis of bacterial biofilms. The software FIND is a new platform for promoting computational analysis and enhanced visualization of multicolor flow cytometry data. FIND provides users with user-friendly, cross-platform analysis software, while simultaneously providing algorithm designers a target for implementation. Finally, the Moflow project represents a new visual representation of atomic flow within molecules during conformational changes over time in a more intuitive sense than was previously possible.
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    Committee: William Ray PhD (Committee Chair); Sheryl Justice PhD (Advisor); Shen Han-Wei PhD (Committee Member); Luis Actis PhD (Committee Member); Charles Daniels PhD (Committee Member) Subjects: Bioinformatics; Biophysics; Computer Science

  • 20. Simonson, Michael Signal Transduction in Diabetic Nephropathy

    Doctor of Philosophy, Case Western Reserve University, 2012, Physiology and Biophysics

    Kidney disease in diabetes, or diabetic nephropathy, is the most prevalent cause of end-stage renal failure worldwide. Available therapy does not prevent onset of nephropathy or end-stage renal disease. We inferred dysregulation of signal transduction in diabetic nephropathy by profiling the renal transcriptome of 8 and 16 week db/m control and db/db type 2 diabetic mice. The data suggested that autocrine signaling by endothelin-1 (ET-1) was increased in 8-week db/db kidney, followed at 16 weeks by elevated expression of 16 diverse autocrine factors including transforming growth factor beta (TGFbeta), growth and differentiation factor 15 (GDF15), macrophage chemoattractant protein-1 (MCP-1), interleukin-6 (IL-6) and lipocalin-2 (LCN2). The majority of these autocrine factors have not been associated previously with diabetic nephropathy. The stimulated autocrine signaling network at 16 weeks correlated temporally with hypertrophy, expansion of mesangial matrix and atrophy of proximal tubules. Bioinformatic enrichment analysis suggested that elevated secretion of ET-1 may evoke autocrine cytokine- and chemokine-based signaling. Experiments in human mesangial cells supported this hypothesis. Exogenous ET-1 induced secretion of IL-6 and MCP-1, which in turn increased collagen production by an autocrine feedforward signaling loop. In a comparative study of humans with type 2 diabetes, urine levels of ET-1, TGFbeta, GDF15, MCP-1, IL-6 and LCN2 were elevated compared to age-matched non-diabetic controls. Urine levels of the six autocrine signals correlated inversely with estimated glomerular filtration rate, and ET-1, GDF15 and IL-6 correlated positively with a marker of proximal tubule injury, urine N-acetyl-beta-D-glucosaminidase. Taken together, these results suggest that in mice and humans with type 2 diabetic nephropathy increased autocrine signaling activity in the kidney is conserved. Furthermore, increased secretion of ET-1 may induce a feedforward autocrine loop i (open full item for complete abstract)
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    Committee: Faramarz Ismail-Beigi MD, PhD (Advisor); William Schilling PhD (Committee Chair); Ulrich Hopfer MD, PhD (Committee Member); Andrea Romani MD, PhD (Committee Member); Timothy Kern PhD (Committee Member); Stephen Jones PhD (Committee Member) Subjects: Physiology