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  • 1. 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

  • 2. 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

  • 3. Lal, Jessica Network-Based Multi-Omics Approaches for Precision Cardio-Oncology: Pathobiology, Drug Repurposing and Functional Testing

    Doctor of Philosophy, Case Western Reserve University, 2023, Molecular Medicine

    Cardiovascular disease is the second leading cause of death in cancer survivors. As a result, the cardio-oncology field was established to explore the connection between cancer treatments and adverse cardiovascular outcomes. Understanding the independent mechanisms of cardiotoxicity and implementing precision medicine approaches are crucial for enhancing cancer survivorship. This work investigates novel network medicine, drug repurposing, and translational science strategies to identify novel therapeutic avenues for cardiovascular adverse events. Chapter 1 offers an overview of cardio-oncology adverse events, known cardiotoxic cancer therapies, and an introduction to implementing precision medicine approaches in the field. Chapter 2 presents a case study of using systems biology and network medicine to identify repurposable drugs for atrial fibrillation, a common cardio-oncology adverse event. Metformin emerged as a top candidate, and our study validated its efficacy for relieving atrial fibrillation risk and genomic signatures, using large-scale electronic health record epidemiologic data and functional validation in hiPSC-cardiomyocytes. Chapter 3 investigates a likely mechanism of doxorubicin-mediated heart failure by examining branched-chain amino acid (BCAA) metabolism. We observed that doxorubicin is associated with impaired breakdown of alpha ketoacids, which affects mitochondrial ATP synthesis and baseline oxygen consumption rate. Treatment with metformin improved BCAA catabolism, mitochondrial phenotype, and glycolytic capacity. Subnetwork analysis of CELF5 and IGFL2/ IGFL3 revealed transcriptional signatures related to tissue remodeling and repair, cardiac cell development, and drug metabolism in doxorubicin and metformin treated hiPSC-cardiomyocytes. Lastly, we show that metformin improves doxorubicin-induced cardiotoxicity by improved heart function and cardiac tissue integrity. Chapter 4 explores biomarker discovery techniques to a unique card (open full item for complete abstract)
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    Committee: Feixiong Cheng (Advisor); Jonathan Smit (Committee Chair); Patrick Collier (Committee Chair); John Barnard (Committee Chair); Timothy Chan (Committee Chair); Mina Chung (Committee Member) Subjects: Bioinformatics; Biology; Health Care; Medicine; Pharmaceuticals

  • 4. Blasco Tavares Pereira Lopes, Filipa Towards Curing an Alzheimer's Mouse Model

    Doctor of Philosophy, Case Western Reserve University, 0, Systems Biology and Bioinformatics

    Mouse models of Alzheimer's Disease (AD) show progression through stages reflective of human pathology. Omics identification of temporal and sex-linked factors driving AD related pathways can be used to dissect initiating and propagating events of AD stages to develop biomarkers or design interventions. In this dissertation, we conducted label-free proteome and phosphoproteome measurements of mouse hippocampus tissue with variables of time (three, six, and nine months), genetic background (5XFAD vs. WT), and sex (equal males and females). These time points are associated with well-defined phenotypes with respect to: Aβ42 plaque deposition, memory deficits, and neuronal loss, allowing correlation of proteome based molecular signatures with the mouse model stages. I identified twenty-three novel AD-related proteins, six of which are differentially expressed between male and female 5XFAD. At a pathway level the 5XFAD specific upregulated proteins are significantly enriched for DNA damage and stress-induced senescence at 3-months only, while at 6-months the AD-specific proteome signature is altered and significantly enriched for membrane trafficking and vesicle-mediated transport protein annotations. By 9-months AD-specific dysregulation is also characterized by significant neuro- inflammation with innate immune system, platelet activation and hyper- reactive astrocyte related enrichments. Complementing these findings, the phosphoproteome is also marked by early DNA damage control (including cell survival and mRNA regulation signatures), however at six months these signatures are substituted by striking disruption of synaptic signalling and cell cycle regulation. Lastly, global MSOx profiling exposed the connection between increased Met(O) peptides and the selective downregulation of antioxidant defenses. This dissertation offers a novel systems-based understanding of AD dynamics, our multi-layered characterization of translational and post- translational pathways (open full item for complete abstract)
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    Committee: Mark R. Chance, PhD (Advisor); Xin Qi, PhD (Committee Member); Janna Kiselar, PhD (Committee Member); Mehmet Koyutürk, PhD (Committee Chair) Subjects: Aging; Bioinformatics; Gender Studies; Neurology; Systems Science

  • 5. Whitman, John Topics in Stochastic and Biological Modeling

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

    In this dissertation, we develop models for biological processes at several spatial and temporal scales. Codon optimization is a procedure in which genetic sequences are altered (without affecting protein identity) in an attempt/effort to increase protein expression. Our goals are to identify if a given single input sequence (for example, of a pathogenic protein of interest) has been codon optimized, and if so, to identify the target organism. In Chapter 2, we present multiple metrics that we have devised to identify codon optimization. Using information from publicly available databases, we define methods both on the scale of an entire sequence/genome and on the scale of individual codon differences between two matched sequences; these methods are shown to perform with high levels of success (>85%) on optimization routines centered around codon usage as well as maximization of the codon adaptive index. It is known experimentally that information about different external stimuli to cells are transmitted to the interior through the temporal patterns of transcription factors (TFs). In Chapter 3, we address the question of how genes can decode information contained in different aspects of the temporal patterns of single transcription factors and initiate downstream responses with specificity. We focus on amplitude and duration variation of the TF signals and construct a two-gene module that produces protein distribution that have minimal overlap for different input signals; it can distinguish between four types of signals reliably (>90% success) in the presence of intrinsic stochastic fluctuations inherent in biochemical reactions and extrinsic temporal fluctuations. We provide information-theoretic measures of the performance including capacity obtaining values consistent with experimental measurements on yeast. In Chapter 4, we define a model which explores an interesting observation: replication of influenza A virus in infected epithelial cells on a cell plate pro (open full item for complete abstract)
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    Committee: Ciriyam Jayaprakash (Advisor); Mohit Randeria (Committee Member); Ratnasingham Sooryakumar (Committee Member); Stuart Raby (Committee Member) Subjects: Bioinformatics; Biophysics; Physics

  • 6. Braman, Nathaniel Novel Radiomics and Deep Learning Approaches Targeting the Tumor Environment to Predict Response to Chemotherapy

    Doctor of Philosophy, Case Western Reserve University, 2020, Biomedical Engineering

    As the arsenal of therapeutic strategies in the fight against cancer grows, so too does the need for predictive biomarkers that can precisely guide their use in order to match patients with their optimal personalized treatment plan. Currently, clinicians often have little recourse but to initiate treatment and monitor a tumor for signs of response or progression, which exposes non-responsive patients to overtreatment, harmful side effects, and windows of ineffective therapy that increase a patient's risk of progression or metastasis. Thus, there is an urgent need for new sources of predictive biomarkers to help more effectively plan personalized treatment strategies. Radiological images acquired before treatment may contain previously untapped predictive information that can be quantified in the form of computational imaging biomarkers. The vast majority of existing computational imaging biomarkers provides analysis limited to the tumor region itself. However, the tumor environment contains critical biological information pertinent to tumor progression and treatment outcome, such as tumor-associated vascularization and immune response. This dissertation focuses on the development of new, biologically-inspired computational imaging biomarkers targeting the tumor environment for the prediction of response to a wide range of chemotherapeutic and targeted treatment strategies in oncology. First, we explore measurements of textural heterogeneity within the tumor and surrounding peritumoral environment, and demonstrate the ability to predict therapeutic response and tumor biology to neoadjuvant chemotherapy in primary and targeted therapy in primary and metastatic breast cancer. Second, we introduce morphologic techniques for the quantification of the twistedness and organization of the tumor-associated vasculature, and demonstrate their association with response and survival following four different therapeutic strategies in breast cancer MRI and non-small cell lung canc (open full item for complete abstract)
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    Committee: Madabhushi Anant (Advisor); Wilson David (Committee Chair); Abraham Jame (Committee Member); Gilmore Hannah (Committee Member); Plecha Donna (Committee Member); Varadan Vinay (Committee Member) Subjects: Biomedical Engineering; Biomedical Research; Computer Science; Medical Imaging; Medicine; Oncology; Radiology

  • 7. Cuevas Santamaría, Sergio My MFA Experience

    Master of Fine Arts, The Ohio State University, 2018, Art

    This MFA thesis explores the threshold of phenomenological perception, audience attention and the mystery of imaginary worlds I perceive between microscopic and macroscopic dimensions. In the BioArt projects and digital immersive environments I present in this thesis, I have found the potential to explore real and imaginary landscapes. This exploration further expands, adding new physical and virtual layers to my work that activate the audience. My work incorporates the synthesis of projection mapping, biological living systems and interactive multimedia. It is the vehicle I use to contemplate the impermanence of time and the illusion of reality.
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    Committee: Ken Rinaldo (Advisor); Amy Youngs (Committee Member); Alex Oliszewski (Committee Member) Subjects: Art Criticism; Art Education; Art History; Biology; Computer Science; Dance; Environmental Studies; Fine Arts; Music; Plant Biology; Pollen; Spirituality

  • 8. Buckalew, Richard Mathematical Models in Cell Cycle Biology and Pulmonary Immunity

    Doctor of Philosophy (PhD), Ohio University, 2014, Mathematics (Arts and Sciences)

    Mathematical models are used to study two biological systems: pulmonary innate immunity and autonomous oscillation in yeast. In order to better understand the dynamics of an early infection of the lungs, we construct a predator-prey ODE model of pulmonary innate immunity which describes several observed properties of the pulmonary innate immune system. Under reasonable biological assumptions, the model predicts a single nontrivial equilibrium point with a stable and unstable manifold. Trajectories to one side of the stable manifold are asymptotic to the disease-free equilibrium and on the other side are unbounded in the size of the infection. The model also reproduces a phenomenon observed by Ben-David et al whereby the innate response to an infectious challenge reduces the ability of further infections to take hold. The model may be useful in analyzing and understanding time series data obtained by new methods in pathogen detection in ventilated patients. We also examine several models of autonomous oscillation in yeast (YAO), called the Immediate, Gap, and Mediated models. These models are based on a new concept of Response / Signaling (RS) coupled oscillator models, where feedback signaling and response are phase-dependent. In all three models, clustering of the type seen in YAO is a robust and generic phenomenon. The Gap and Mediated models add a dynamical delay, the latter by modeling a signaling agent present in the culture. For dense populations the Mediated model approximates the Immediate model, but the Mediated model includes dynamical quorum sensing where clustered solutions become stable through density-dependent bifurcations. A partial differential equations model is also examined, and we demonstrate existence and uniqueness of solutions for most parameter values.
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    Committee: Todd Young (Advisor); Winfried Just (Committee Member); Alexander Neiman (Committee Member); Tatiana Savin (Committee Member) Subjects: Applied Mathematics; Cellular Biology; Immunology

  • 9. Shen, Kaiyu Gravitropic Signal Transduction: A Systems Approach to Gene Discovery

    Doctor of Philosophy (PhD), Ohio University, 2014, Molecular and Cellular Biology (Arts and Sciences)

    Gravity is an important stimulus for plants. Gravitropism, the plants' response to gravity, can be divided into three phases: gravity perception, signal transduction and response. Various theories have been proposed to explain the process of gravitropism, yet more genes are needed to elucidate the mechanism of gravitropic signal transduction. A transcriptome analysis, in combination with the Gravity Persistent Signal treatment, was performed to specifically study the genes involved in signal transduction. Analysis generated a list of 318 transcripts that were differentially expressed in plants that were reoriented with respect to gravity as compared to vertical controls. Based on the expression profiles and gene function annotations, five transcription factors, WRKY18, WRKY26, WRKY33, BT2 and ATAIB, were selected for further study. In addition to the standard analysis of differentially expressed genes, a systems approach was adopted to uncover more gravity related genes. A semi-supervised learning method was developed to find additional novel genes. This learning method took a set of 32 known gravity genes from the literature as well as a collection of heterogeneous annotation features, such as existing protein-protein interactions, and co-expression profiles. The learning classifier predicted a list of 50 genes that are functionally related to gravity signal transduction. Based on this list of genes, an interaction network was predicted based two complementary approaches: a dynamic Bayesian network and a time-lagged correlation coefficient. To increase confidence in the predication, genes/interactions that appeared in both networks were selected. This 'intersected' network provided 20 hub and bottleneck genes, fourteen of which had not been previously identified as involved in gravitropism. Such an approach provides a framework to extend current research in a more comprehensive manner, and serves a complementary to the traditional mutant/gene discovery (open full item for complete abstract)
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    Committee: Sarah Wyatt Dr. (Advisor); Allan Showalter Dr. (Committee Chair); Lonnie Welch Dr. (Committee Member); Frank Horodyski Dr. (Committee Member) Subjects: Biology; Computer Science

  • 10. Ghosh, Krishnendu Formal Analysis of Automated Model Abstractions under Uncertainty: Applications in Systems Biology

    PhD, University of Cincinnati, 2012, Engineering and Applied Science: Computer Science and Engineering

    In this dissertation, three fundamental problems in modeling of large scale biological systems are addressed. 1. Modeling of chemical reaction under imprecise rate of reactions: A framework is created to model chemical reactions with an interval based approach, incorporating imprecision as well as creating a finite space. Algorithms are presented to construct model abstraction efficiently. The results of the algorithms on a prototype elucidate the model. The formalism presents a novel way to represent continuous data of concentrations for the chemicals and quantitative analysis of temporal behavior of the system. 2. Multiscale formalism in discrete domains: Biological processes are multiscale. We formalize the definition of multiscale modeling in discrete domains. A polynomial algorithm is constructed to compute identifiability of multiscale systems. 3. Formal analysis of gene regulatory network: A formalism that incorporates noise in the data is presented to study gene regulation. Computational efficiency of the formalism is evaluated on a prototype constructed from biological experimental data.
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    Committee: John Schlipf PhD (Committee Chair); Raj Bhatnagar PhD (Committee Member); Yizong Cheng PhD (Committee Member); Mario Medvedovic PhD (Committee Member); George Stan PhD (Committee Member) Subjects: Computer Science

  • 11. Kalluru, Vikram Gajanan Identify Condition Specific Gene Co-expression Networks

    Master of Science, The Ohio State University, 2012, Electrical and Computer Engineering

    Since co-expressed genes often are co-regulated by a group of transcription factors, different conditions (e.g., disease versus normal) may lead to different transcription factor activities and therefore different co-expression relationships. A method for identifying condition specific co-expression networks by combining the recently developed network quasi-clique mining algorithm and the Expected Conditional F-statistic has been proposed. This method has been applied to compare the transcriptional programs between the non-basal and basal types of breast cancers. This work is a translational bioinformatics study integrating network analysis which lifts the traditional gene list based disease biomarker discovery to the gene and protein interaction level. This work presents a method for identifying condition specific gene co-expression networks. The method involves construction of a Weighted Graph Co-expression Network (WGCN) and mining the WGCNs to identify dense co-expression networks followed by a chi-square test based enrichment analysis for detecting condition specific co-expression relationship. The expression values in all the conditions for the genes constituting a condition specific co-expression network are visualized as heat maps which suggest that the genes are highly correlated in a specific condition but the correlations are disrupted in other conditions.
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    Committee: Kun Huang PhD (Advisor); Raghu Machiraju PhD (Committee Member) Subjects: Bioinformatics; Computer Engineering; Computer Science; Engineering

  • 12. Chen, Jingchun Studies on the topology, modularity, architecture and robustness of the protein-protein interaction network of budding yeast Saccharomyces cerevisiae

    Doctor of Philosophy, The Ohio State University, 2006, Medical Science

    In this dissertation, statistical mechanics, graph theory, and machine learning methods have been used to study the topology, modularity, organization and robustness of the proteome network of Saccharomyces cerevisiae. The protein-protein interaction dataset is obtained by combining high confidence interactions, and is validated from multiple perspectives. Statistical mechanics is then used to analyze the connectivity distribution, graph spectrum, shortest path distance and clustering coefficients of the network, which indicates that the network is both scale-free and modular. Microarray gene expression profiles are used to compute the weight for each interaction and the network is represented as a weighted graph. An edge betweenness-based algorithm is developed and applied on the graph, and a set of functional modules is identified. The functional modules are then validated rigorously against gene annotation, growth phenotype and protein complexes. It is found that genes in the same functional module exhibit similar deletion phenotype, and that known protein complexes are largely contained in the functional modules. Studies on the relationship between the gene expression profiles of hubs and their interacting proteins indicate that subpopulations of hubs exist in the yeast proteome network, which are classified as core, local and global hubs. By examining these hub populations from the perspectives of protein complexes, interaction overlap, clustering coefficients, module connectivity, and visualization, it is found that global hubs form the backbone of module-module interaction, while core hubs are organizers within functional modules. In addition, it is found that each hub type preferentially interacts with hubs from the same population, which suggests an ordered architecture for the network. Studies on gene expression changes suggest that global hubs are the major and early responders in cellular response. Next, network breakdown simulation and graph spectrum ar (open full item for complete abstract)
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    Committee: Bo Yuan (Advisor) Subjects:

  • 13. Wu, Ming Averaging and Monotonicity Analysis of Ca2+/Calmodulin-Dependent Protein Kinase-Phosphatase System

    Master of Science (MS), Ohio University, 2011, Electrical Engineering (Engineering and Technology)

    Ca2+/calmodulin-dependent protein kinase II (CaMKII) is thought to be a key contributor to the induction of long-term potentiation (LTP). Researchers have developed a variety of mathematical models of CaMKII activation intended to produce simulation outputs that agrees with empirical observations. Our research focuses on one such model to which recent theoretical results for input-output monotone systems are applied. Several key findings in the literature are reproduced using simple algebraic computations as opposed to exhaustive, simulation-based analysis when the system input is constant. However, the system input is often periodic in experimental settings, so another important part of our research is averaging analysis, which provides us a way to build up an average model that approximates the original system asymptotically as the perturbation tends to zero. Meanwhile, we intend to establish that the CaMKII activation system acts as a low-pass filter which filters out high frequency components in the input signal. Thus the CaMKII activation system with a periodic input can be approximated by an averaged system with a constant input. In this way, not only is the computational burden of the simulation greatly reduced, but also the system analysis can be simplified significantly.
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    Committee: Douglas Lawrence PhD (Advisor); Jim Zhu PhD (Committee Member); Jundong Liu PhD (Committee Member); William Holmes PhD (Committee Member) Subjects: Electrical Engineering; Systems Science

  • 14. Azzam, Yves PathCase SB: Automating Performance Monitoring And Bugs Detection

    Master of Sciences, Case Western Reserve University, 2012, EECS - Computer and Information Sciences

    After applying some significant performance improvements to PathCase SB and making it run faster and smoother we build a monitoring tool for the system that would facilitate future maintenance. Hence, in this thesis we propose a performance monitor specialized for the current version 4.0 of PathCase SB but that is able to automatically adapt to newer versions. The system can also be used to monitor other applications of the PathCase set of applications. We show the problems that we detected and solved and then we use the performance monitor to detect and fix even more bugs.
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    Committee: Dr. Meral Ozsoyoglu PhD (Advisor); Dr. Vincenzo Liberatore PhD (Committee Member); Dr. Andy Podgurski PhD (Committee Member); Dr. Gultekin Ozsoyoglu PhD (Committee Member) Subjects: Computer Science

  • 15. Coskun, Sarp PATHCASE-SB MODEL SIMULATION AND MODEL COMPOSITION TOOLS FOR SYSTEMS BIOLOGY MODELS

    Master of Sciences, Case Western Reserve University, 2012, EECS - Computer and Information Sciences

    Systems Biology is a field of study which focuses on the interactions in biological systems. There are multiple representation formats for these computational models and Systems Biology Markup Language (SBML) is one of the most widely used. SBML is used not only to store but also to distribute computational models between Systems Biology data sources (e.g. BioModels) and researchers. Therefore, there is a need for all-in-one solutions which support advance SBML functionalities such as uploading, editing, composing, visualizing, simulating, querying and browsing computational models. In this thesis, we describe four web based tools to simulate, compose, edit and compare computational models on top of PathCase-SB (PathCase Systems Biology) web portal. These tools are: 1. Model Simulation Interface, which generates a visual plot according to the user input, 2. iModel Tool, that creates a platform for users to upload their own models, 3. SimCom Tool, that provides a side by side comparison of models in the same pathway, 4. Model Composition Interface, which supports multiple components to facilitate the complex process of merging computational models.
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    Committee: Gultekin Ozsoyoglu Dr (Advisor); Jing Li Dr (Committee Member); Mehmet Koyuturk Dr (Committee Member); Meral Ozsoyoglu Dr (Committee Member); Nicola Lai Dr (Committee Member) Subjects: Computer Science

  • 16. Weis, Michael Computational Models of the Mammalian Cell Cycle

    Doctor of Philosophy, Case Western Reserve University, 2011, EECS - System and Control Engineering

    Systems biology has sometimes been defined as the application of systems science and engineering concepts to biological problems. This dissertation illustrates the usefulness of this approach in understanding the regulation of the mammalian cell cycle. Cell growth and division are fundamental properties of life, and the dysregulation of cell cycle control is central to the development of cancer. Understandably then, the cell cycle has historically been a popular subject for mathematical modeling efforts and we review 154 models developed over the past 80 years. Beyond mathematics however, understanding systems requires the evaluation of models against data. The work presented herein illustrates an approach for estimating the median dynamic expression profiles of cell cycle regulatory molecules from a flow cytometric snapshot of an asynchronous population, and applies this data to the modification and calibration of a computational model of mammalian cell cycle control. This contribution illustrates the value of the systems biology approach in integrating existing evidence, interpreting data, and driving new hypotheses regarding the organizing principles of biological systems. Having used single cell data to model the median trajectory of a population, we then investigate approaches to simulate cell-cell variation and reproduce the distribution of cells originally measured with flow cytometry. This comprehensive methodology also establishes an approach to studying proliferative diseases, such as hematopoietic cancers, which can be easily sampled and measured using flow cytometry. As only one static measurement is needed to define the underlying expression profile, this may provide an entry point to applying computational models and systems engineering methodologies to the treatment of individual patients.
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    Committee: Sree N. Sreenath PhD (Committee Chair); James W. Jacobberger PhD (Committee Member); Kenneth A. Loparo PhD (Committee Member); Vira Chankong PhD (Committee Member); Mihajlo D. Mesarovic PhD (Committee Member) Subjects: Applied Mathematics; Bioinformatics; Biology; Engineering; Molecular Biology; Systems Science

  • 17. Patel, Vishal Colon Cancer and its Molecular Subsystems: Network Approaches to Dissecting Driver Gene Biology

    Doctor of Philosophy, Case Western Reserve University, 2011, Genetics

    The progression of colorectal cancer is driven by the accumulation of mutations in a number of key genes, which synergize with each other to promote tumor growth. These co-regulatory genes are of particular importance as they provide alternative points for pharmacologic modulation of perturbed signaling pathways. In this work, we explored the signaling landscapes of three important regulatory genes – Hpgd, Apc, and Cdkn1a – using genetically engineered mice as models of colon cancer. We assayed the intestinal epithelium using genomic (mRNA arrays) and proteomic (2D gels and label-free proteomics) modalities. To identify genes with a potential regulatory role, we employed protein interaction networks and mRNA coexpression networks to extrapolate from the proteomic measurements. For applications where the signaling pathway was known a priori, we introduced bioinformatic approaches to test these network-based hypotheses. In addition, we also introduced a novel statistical framework for interpreting label-free proteomic data in a genomic context. With the objective of identifying new regulatory targets, we found that the sparsity of proteomic data is ameliorated by relying on inferences from interaction networks and that these inferences are greatly improved by coupling proteomic data with genomic measurements.
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    Committee: Mark Chance PhD (Advisor); Georgia Wiesner MD (Committee Chair); Mehmet Koyuturk PhD (Committee Member); Sudha Iyengar PhD (Committee Member) Subjects: Bioinformatics; Biostatistics; Systems Science

  • 18. Avva, Jayant Complex Systems Biology of Mammalian Cell Cycle Signaling in Cancer

    Doctor of Philosophy, Case Western Reserve University, 2011, EECS - System and Control Engineering

    We present here a complex systems biology approach towards elucidating the role of mammalian cell cycle signaling in cancer. In this context, availability of copious amounts of biological data has done little to alter the paucity of contextually consistent dynamic time profile data, necessary for the calibration and validation of dynamic models of the biological systems. Such computational models form the heart of the complex systems biology approach, and paucity of appropriate data is an immediate impediment. To address this problem, we developed a novel methodology to filter measurement noise and extract time profile variation of cell cycle biochemicals from statically sampled flow cytometry data. Taking a hierarchical viewpoint, a mathematical model of the upstream signaling from the receptor through to the nucleus was developed. A computational model of the downstream cell cycle control system based on a modified Tyson's mathematical model that uses our time profile extraction methodology for calibration was also developed. The approach was demonstrated separately using K562 and MOLT4 cell line experimental data from the wetlab. We built custom software, CytoSys, to facilitate the application of our methodology.
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    Committee: Sree N. Sreenath PhD (Committee Chair); James W. Jacobberger PhD (Committee Member); Mihajlo D. Mesarovic PhD (Committee Member); Kenneth A. Loparo PhD (Committee Member); Vira Chankong PhD (Committee Member) Subjects: Cellular Biology; Engineering; Systems Science

  • 19. Nibbe, Rod Systems Biology of Human Colorectal Cancer

    Doctor of Philosophy, Case Western Reserve University, 2009, Pharmacology

    Like all human cancers colorectal cancer (CRC) is a complicated disease. While a mature body of research involving CRC has implicated the putative sequence of genetic alterations that trigger the disease and sustain its progression, there is a surprising paucity of well-validated, clinically useful diagnostic markers of this disease. For prognosis or guiding therapy, single gene-based markers of CRC often have limited specificity and sensitivity. Genome-wide analyses (microarray) have been used to propose candidate patterns of gene expression that are prognostic of outcome or predict the tumor's response to a therapy regimen, however these patterns frequently do not overlap, and this has raised questions concerning their power as biomarkers. The limitation of gene expression approaches to marker discovery occurs because the change in mRNA expression across tumors is highly variable and alone accounts for a limited variability of the phenotype, e.g. cancer. It is largely unknown how the integration of proteomic data and genomic data, along with protein-protein interaction data may enhance the discovery of more quantitatively powerful biomarkers. In this work we show that a proteomics-first approach can discover significantly, differentially expressed proteins between cancer and control tissues. In turn, these targets may be integrated with mRNA and protein-protein interaction data to discover networks of proteins that are quantitatively significant discriminators of cancer versus control. Further, we show that our bioinformatic methods are extensible and robust with respect to publicly available proteomic data and public PPI datasets. Further, a proteomics-first approach for finding significant sub-networks in CRC is comparable to the same approach seeded instead with a set genes implicated as “drivers” of CRC. Finally, because these network discriminators exist at the level of the proteome, they provide an optimal basis for mechanistic validation in in vitro disease (open full item for complete abstract)
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    Committee: Mark Chance PhD (Advisor); Charles Hoppel MD (Committee Chair); Noa Noy PhD (Committee Member); John Letterio MD (Committee Member) Subjects: Bioinformatics; Biology; Biomedical Research; Genetics

  • 20. Brantner, Justin Mating System Inferences In Representatives From Two Clam Shrimp Families (Limnadiidae and Cyzicidae) Using Histological and Cellular Observations

    Master of Science, University of Akron, 2011, Biology

    Branchiopods represent one of the most ancient crustacean lineages that still include extant representatives, many of which are capable of a wide array of reproductive mechanisms. Specifically, members of the branchiopod suborder Spinicaudata have been found to display at least four different mating system strategies: dioecy, androdioecy, hermaphroditism, and parthenogenesis. Although genetic/molecular, morphological, and sex ratio data have dominated the field in terms of understanding mating systems within the Spinicaudata, cellular and histological evidence have recently started to unveil fundamental questions regarding reproductive biology within various Spinicaudata groups. As such, a clearer understanding of Spinicaudata reproductive biology has aided in more accurate assignment of mating system type within many clam shrimp populations. The purpose of this study was to investigate basic reproductive biology and associated mating systems in multiple clam shrimp species not previously studies using histological or cellular techniques. Based on the histological and cellular findings of this study, anatomical verification is herein provided that supports many previously inferred mating systems in various clam shrimp populations. Also, this study provides anatomical support that can be used to infer likely mating system mechanisms in representative populations of clam shrimp species not previously investigated for reproductive mode. Lastly, anatomical evidence is provided for the first time that suggests that individuals from a Cyzicus gynecia population (previously thought to be asexual) are, in fact, selfing-hermaphrodites. The anatomical data generated from this study for multiple Spinicaudata representatives suggests a useful technique that can be used in conjunction with current commonly used methodologies (namely genetic/molecular data, sex ratios, and morphological evidence) to gain a clearer understanding of the various mating systems found within these un (open full item for complete abstract)
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    Committee: Donald Ott Dr. (Advisor); Joel Duff Dr. (Committee Member); Steve Weeks Dr. (Committee Member) Subjects: Biology
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