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Identifying Biologically Relevant Mechanisms And Biomarkers Using Novel Bioinformatics Methods, Samer Hanoudi

Wayne State University Dissertations

There is a tremendous need to analyze molecular and patient clinical data to identify biomarkers, biological mechanisms, or to simply classify samples accurately. Issues such as: i) limited tools to diagnose many diseases, ii) not considering biological interactions, or iii) damaged DNA samples could cause a challenge in identifying valuable insights. In this work, I try to address these issues by developing different bioinformatic frameworks.First, I present three frameworks to identify i) Sarcoidosis biomarkers, ii) Tuberculosis biomarkers and iii) Cystic fibrosis (CF) biomarkers. I identified Sarcoidosis biomarkers I applied them to classify Sarcoidosis samples from non-Sarcoidosis (healthy controls, Tuberculosis, and …

Methods To Integrate Genetic And Clinical Data For Disease Subtyping, Diana Mabel Diaz-Herrera

Wayne State University Dissertations

Enormous efforts have been made to collect genetic and clinical data from cancer patients to advance the understanding of disease development and progression. Processing and analyzing these flows of data is challenging. Many computational methods have been proposed to help different fronts of biology and medicine. The integration of clinical and genetic data using computational methods towards personalized medicine is considered the future for oncology studies, and this thesis contributes in this direction. This thesis presents three new data integration approaches to elucidate granular and meaningful disease sub-types from high-dimensional complex genetic and clinical variables, which is an essential step …

Biological And Computational Studies Of The Structure And Function Of Pul103, A Human Cytomegalovirus Tegument Protein, Ashley N. Anderson

Wayne State University Dissertations

Human cytomegalovirus (HCMV) is an enveloped, single segment, double-stranded DNA virus. HCMV infection causes disease in immunocompromised (HIV patients, transplant recipients) and immunodeficient (fetuses, neonates) populations. Current treatments are effective but are either limited in use or can lead to organ damage and/or antiviral resistance, and no vaccines are available. Additional antiviral targets are needed. HCMV pUL103 is a potential antiviral target. pUL103 is a conserved herpesvirus protein present in the tegument, layer of proteins and RNA between the envelope and capsid of HCMV virions. pUL103 helps reorganize cellular secretory machinery (Golgi, endosomes) to form the cytoplasmic virion assembly compartment …

Missing Heritability And Novel Germline Risk Loci In Hereditary Ovarian Cancer: Insights From Whole Exome Sequencing And Functional Analyses, Jaime Lyn Stafford

Wayne State University Dissertations

While 25% of ovarian cancer (OVCA) cases are due to inherited factors, most of the genetic risk remains unexplained. This study addressed this gap by identifying previously undescribed OVCA risk loci through the whole exome sequencing (WES) of 48 BRCA1/BRCA2 wild type women diagnosed with OVCA, selected for high risk of genetic inheritance. Five clearly pathogenic variants were identified in this sample, four of which are in two genes featured on current multi-gene panels; (RAD51D, ATM). In addition, a high impact variant in FANCM (R1931*) was identified. FANCM has been recently implicated in familial breast cancer risk but is not …

Qualitative Change Detection Approach For Preventive Therapies, Cristina Mitrea

Wayne State University Dissertations

Currently, most diseases are diagnosed only after disease-associated changes have occurred. In this PhD dissertation, we propose a paradigm shift from treating the disease to maintaining the healthy state. The proposed approach is able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility of therapeutic interventions before the occurrence of symptoms. The change detection method exploits knowledge from biological networks and longitudinal data using a system impact analysis approach. This approach is validated on eight datasets, for seven different model organisms and eight biological phenomena. On these data, our proposed method performs well, consistently identifying …

Functional Analysis Of Sin3 Isoforms In Drosophila, Nirmalya Saha

Wayne State University Dissertations

he multisubunit SIN3 complex is a global transcriptional regulator. In Drosophila, a single Sin3A gene encodes different isoforms of SIN3, of which SIN3 187 and SIN3 220 are the major isoforms. Previous studies have demonstrated functional non-redundancy of SIN3 isoforms. The role of SIN3 isoforms in regulating distinct biological processes, however, is not well characterized. In addition, how the components of the SIN3 complex modulate the gene regulatory activity of the complex is not well understood. In this study, I identified the biological processes regulated by the SIN3 isoforms. Additionally, I explored how Caf1-55 impacts the gene regulatory activity of …

Horizontal And Vertical Integration Of Bio-Molecular Data, Tin Chi Nguyen

Wayne State University Dissertations

Modern biomedical research lies at the crossroads of data gathering, interpretation, and hypothesis testing. Due to noise, study bias, or too small changes in biological signals between disease and healthy, individual studies often fail to identify the true phenomenon. Data integration is the key to obtaining the power needed to pinpoint the biological mechanisms of disease states. Given this, we tried to make important contributions in both horizontal and vertical integration of high-throughput data; the former is meta-analysis of independent studies, while the latter is the integration of multi-omics data.

For horizontal meta-analysis, we developed two frameworks: DANUBE and the …

Network-Based Approaches To Identify The Impacted Genes And Active Interactions, Sahar Ansari

Wayne State University Dissertations

A very important step in system biology is the identification of the networks that are most impacted in the given phenotype.

Such networks explain where the target genes are affected by some other genes, and therefore describe the mechanisms involved in a biological process.

The identified networks are used to: 1) predict the disease or the responses of the system to a specific impact, 2) find the subset of genes that interact with each other and play an important role in the condition of interest, and 3) understand the mechanisms involved in that condition.

In this thesis, we propose an …

Neuronal Insult Either By Exposure To Lead Or By Direct Neuronal Damage Cause Genome-Wide Changes In Dna Methylation And Histone 3 Lysine 36 Trimethylation, Arko Sen

Wayne State University Dissertations

Prenatal and postnatal exposure to pervasive neuro-toxicants such as Lead (Pb) has been reported to causes extensive and diverse changes in the epigenetic profile. Among epigenetic modification, DNA methylation (5mC) is perhaps the most widely studied and has been proposed to be potential early biomarkers for Pb toxicity. Several studies have demonstrated the association between Pb-exposure and 5mC. However most of these studies are restricted to looking at a specific set of target genes or repetitive elements. Therefore, one of the main objectives of our study was to use an unbiased genome-wide approach to look at Pb-exposure associated changes in …

Systems Biology Approaches For The Analysis Of High-Throughput Biological Data, Michele Donato

Wayne State University Dissertations

The identification of biological processes involved with a certain phenotype, such as a disease or drug treatment, is the goal of the majority of life sciences experiments.

Pathway analysis methods are used to interpret high-throughput biological data to identify such processes by incorporating information on biological systems to translate data into biological knowledge.

Although widely used, current methods share a number of limitations.

First, they do not take into account the individual contribution of each gene to the phenotype in analysis.

Second, most of the methods include parameters of difficult interpretation, often arbitrarily set.

Third, the results of all methods …

Modeling The Mechanism Underlying Environmental And Genetic Determinants Of Gene Expression And Complex Traits, Gregory Alan Moyerbrailean

Wayne State University Dissertations

Advances in next-generation sequencing technologies and functional genomics strategies have allowed researchers to identify both common and rare genetic variation, to deeply profile gene expression, and even to determine regions of active gene transcription.

While these technologies and strategies have contributed greatly to our understanding of complex traits and diseases, there are many biological questions and analytical issues to be addressed.

Genome-wide association studies (GWAS) have successfully identified large numbers of genetic variants associated with complex traits and diseases. However, in many cases the mechanistic link between the phenotype and associated variant remains unclear. This may be because most variants …

Efficient Synergistic De Novo Co-Assembly Of Bacterial Genomes From Single Cells Using Colored De Bruijn Graph, Narjes Sadat Movahedi Tabrizi

Wayne State University Dissertations

Recent progress in DNA amplification techniques, particularly multiple displacement

amplification (MDA), has made it possible to sequence and assemble bacterial

genomes from a single cell. However, the quality of single cell genome assembly has

not yet reached the quality of normal multi-cell genome assembly due to the coverage

bias (including uneven depth of coverage and region blackout) and errors caused by

MDA. Computational methods try to mitigates the amplification bias. In this document

we introduce a de novo co-assembly method using colored de Bruijn graph,

which can overcome the problem of blackout regions due to amplification bias. The

algorithm is …

Applications Of Machine Learning In Biology And Medicine, Saied Haidarian Shahri

Wayne State University Dissertations

Machine learning as a field is defined to be the set of computational algorithms that improve their performance by assimilating data.

As such, the field as a whole has found applications in many diverse disciplines from robotics and communication in engineering to economics and finance, and also biology and medicine.

It should not come as a surprise that many popular methods in use today have completely different origins.

Despite this heterogeneity, different methods can be divided into standard tasks, such as supervised, unsupervised, semi-supervised and reinforcement learning.

Although machine learning as a field can be formalized as methods trying to …

Algorithms And Tools For Computational Analysis Of Human Transcriptome Using Rna-Seq, Nan Deng

Wayne State University Dissertations

Alternative splicing plays a key role in regulating gene expression, and more than 90% of human genes are alternatively spliced through different types of alternative splicing. Dysregulated alternative splicing events have been linked to a number of human diseases. Recently, high-throughput RNA-Seq technologies have provided unprecedented opportunities to better characterize and understand transcriptomes, in particular useful for the detection of splicing variants between healthy and diseased human transcriptomes.

We have developed two novel algorithms and tools and a computational workflow to interrogate human transcriptomes between healthy and diseased conditions. The first is a read count-based Expectation-Maximization (EM) algorithm and tool, …

Teak: A Novel Computational And Gui Software Pipeline For Reconstructing Biological Networks, Detecting Activated Biological Subnetworks, And Querying Biological Networks., Thair Judeh

Wayne State University Dissertations

As high-throughput gene expression data becomes cheaper and cheaper, researchers are faced with a deluge of data from which biological insights need to be extracted and mined since the rate of data accumulation far exceeds the rate of data analysis. There is a need for computational frameworks to bridge the gap and assist researchers in their tasks. The Topology Enrichment Analysis frameworK (TEAK) is an open source GUI and software pipeline that seeks to be one of many tools that fills in this gap and consists of three major modules. The first module, the Gene Set Cultural Algorithm, de novo …

The Drosophila Interactions Database: Integrating The Interactome And Transcriptome, Thilakam Murali

Wayne State University Dissertations

In this thesis I describe the integration of heterogeneous interaction data for Drosophila into DroID, the Drosophilainteractions database, making it a one-stop public resource for interaction data. I have also made it possible to filter the interaction data using gene expression data to generate context-relevant networks making DroID a one-of-a kind resource for biologists. In the two years since the upgraded DroID has been available, several studies have used the heterogeneous interaction data in DroID to advance our understanding of Drosophila biology thus validating the need for such a resource for biologists. In addition to this, I have identified …

Towards Personalized Medicine Using Systems Biology And Machine Learning, Calin Voichita

Wayne State University Dissertations

The rate of acquiring biological data has greatly surpassed our ability to interpret it. At the same time, we have started to understand that evolution of many diseases such as cancer, are the results of the interplay between the disease itself and the immune system of the host. It is now well accepted that cancer is not a single disease, but a “complex collection of distinct genetic diseases united by common hallmarks”. Understanding the differences between such disease subtypes is key not only in providing adequate treatments for known subtypes but also identifying new ones. These unforeseen disease subtypes are …

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification, Rebecca Jane Swett

Wayne State University Dissertations

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data.

Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and …

Gene Duplication And The Evolution Of The Higher Diptera, Riyue Bao

Wayne State University Dissertations

Gene duplication is an important source of evolutionary innovation. Using the publicly available genomic information, I studied lineage-specific gene duplications in Drosophila melanogaster (fruit fly), Anopheles gambiae (mosquito), Tribolium castaneum (red flour beetle), and Apis mellifera (honey bee) at three scales: eye-specific genes, developmental genes, and genome-wide. All three studies consistently show that the Drosophila genome contains an exceptionally high number of lineage-specific yet ancient gene duplicates, the majority of which must have originated during the early diversification of the higher Diptera (Brachycera) at least 100 million years ago. Genetic data suggest that gene duplication played an important role in …

Hierarchical Multi-Label Classification For Protein Function Prediction Going Beyond Traditional Approaches, Noor Al Aydie

Wayne State University Dissertations

Hierarchical multi-label classification is a variant of traditional classification in which the

instances can belong to several labels, that are in turn organized in a hierarchy. Functional classification of genes is a challenging problem in functional genomics due to several reasons. First, each gene participates in multiple biological activities. Hence, prediction models should support multi-label classification. Second, the genes are organized and classified according to a hierarchical classification scheme that represents the relationships between the functions of the genes. These relationships should be maintained by the prediction models. In addition, various bimolecular data sources, such as gene expression data and …

Mechanistic Studies Of A Novel Ppar-Gamma Mutant That Causes Lipodystrophy And Diabetes, Olga Astapova

Wayne State University Dissertations

PPAR-gamma is a nuclear receptor that plays a central role in metabolic regulation by regulating extensive gene expression networks in adipose, liver, skeletal muscle and many other tissues. Human PPAR-gamma mutations are rare and cause a monogenetic form of severe type II diabetes with metabolic syndrome, known as familiar partial lypodystrophy. The E157D PPAR-gamma mutant causes atypical lipodystrophy in a large Canadian kindred, presenting with multiple musculoskeletal, neurological and hematological abnormalities in addition to the classic lipodystrophy features of insulin-resistant diabetes, hypertension and dyslipidemia. This mutation is localized to the p-box of PPAR-gamma, a small region that interacts directly with …

Phylogenetic Utility Of Mitochondrial And Nuclear Genes: A Case Study In The Diptera (True Flies), Jason Caravas

Wayne State University Dissertations

The value of mitochondrial versus nuclear gene sequence data in phylogenetic analysis has received much attention without yielding definitive conclusions. Theoretical arguments and empirical data suggest a lower phylogenetic utility than equivalent nuclear gene sequences, but there are also many examples of important progress made using mitochondrial sequences. We therefore undertook a systematic performance analysis of mitochondrial and nuclear sequence partitions taken from a representative sample of dipteran species. For phylogenetic tree reconstruction, mitochondrial genes performed generally inferior to nuclear genes. However, the mitochondrial genes resolved branches for which nuclear genes failed. Moreover, the combined use of mitochondrial and nuclear …

Differential Modeling For Cancer Microarray Data, Omar Odibat

Wayne State University Dissertations

Capturing the changes between two biological phenotypes is a crucial task in understanding the mechanisms of various diseases. Most of the existing computational approaches depend on testing the changes in the expression levels of each single gene individually. In this work, we proposed novel computational approaches to identify the differential genes between two phenotypes. These approaches aim to quantitatively characterize the differences between two phenotypes and can provide better insights and understanding of various diseases. The purpose of this thesis is three-fold. Firstly, we review the state-of-the-art approaches for differential analysis of gene expression data.

Secondly, we propose a novel …

Detecting Phenotype-Specific Interactions Between Biological Processes From Microarray Data And Annotations, Nadeem Ahmed Ansari

Wayne State University Dissertations

The development of high throughput technologies such as DNA microarrays has enabled researchers to measure expression levels on a genomic scale. Correct and efficient biological interpretation of the voluminous data generated by these technologies, however, remains a challenging problem. A commonly used approach in interpreting the results of such high throughput experiments is to map the list of differentially expressed (DE) genes to gene ontology (GO) terms, which provides a list of biological processes, biochemical functions, and cellular locations associated with the DE genes. A previously unexplored aspect is the identifications of unusual associations between biological processes. Such associations may …

Tracking Profiles Of Genomic Instability In Spontaneous Transformation And Tumorigenesis, Lesley Lawrenson

Wayne State University Dissertations

The dominant paradigm for cancer research focuses on the identification of specific genes for cancer causation and for the discovery of therapeutic targets. Alternatively, the current data emphasize the significance of karyotype heterogeneity in cancer progression over specific gene-based causes of cancer. Variability of a magnitude significant to shift cell populations from homogeneous diploid cells to a mosaic of structural and numerical chromosome alterations reflects the characteristic low-fidelity genome transfer of cancer cell populations. This transition marks the departure from micro-evolutionary gene-level change to macro-evolutionary change that facilitates the generation of many unique karyotypes within a cell population. Considering cancer …