published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Mathematics / Master / Master of Philosophy

published_or_final_version / Computer Science / Master / Master of Philosophy

A new era of chemical analysis is upon us. In the past, a small number of samples were selected from a population for use as a statistical representation of the entire population. More recently, advancements in data collection rate, computer memory, and processing speed have allowed entire populations to be sampled and analyzed. The result is massive amounts of data that convey relatively little information, even though they may contain a lot of information. These large quantities of data have already begun to cause bottlenecks in areas such as genetics, drug development, and chemical imaging. The problem is straightforward: condense a large quantity of data into only the useful portions without ignoring or discarding anything important. Performing the condensation in the hardware of the instrument, before the data ever reach a computer is even better. The research proposed tests the hypothesis that clusters of data may be rapidly identified by linear fitting of quantile-quantile plots produced from each principal component of principal component analysis. Integrated Sensing and Processing (ISP) is tested as a means of generating clusters of principal component scores from samples in a hyperspectral near-field scanning optical microscope. Distances from the centers of these multidimensional cluster centers to all other points in hyperspace can be calculated. The result is a novel digital staining technique for identifying anomalies in hyperspectral microscopic and nanoscopic imaging of human atherosclerotic tissue. This general method can be applied to other analytical problems as well.

<p> 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 several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification.</p>

The last decade has seen considerable research focusing on understanding the factors underlying schizophrenia and bipolar disorder. A major challenge encountered in studying these disorders, however, has been the contribution of genetic, or etiological, heterogeneity to the so-called “missing heritability” [1-6]. Further, recent successes of large-scale genome-wide association studies (GWAS) have nonetheless seen only limited advancements in the delineation of the specific roles of implicated genes in disease pathophysiology. The study of microRNAs (miRNAs), given their ability to alter the transcription of hundreds of targeted genes, has the potential to expand our understanding of how certain genes relate to schizophrenia and bipolar disorder. Indeed, the strongest finding of one recent mega-analysis by the Psychiatric GWAS consortium (PGC) was for a miRNA, though little can be said presently about its particular role in the etiologies of schizophrenia and bipolar disorder [52]. Next generation sequencing (NGS) is a versatile technology that can be used to directly sequence either DNA or RNA, thus providing valuable information on variation in the genome and in the transcriptome. A variation of NGS, MicroSeq, focuses on small RNAs and can be used to detect novel, as well as known, miRNAs [26,125, 126]. The following thesis describes the role of miRNAs in schizophrenia and bipolar disorder in various experimental settings. As an index of the interaction between multiple genes and between the genome and the environment, miRNAs are great potential biomarkers for complex disorders such as schizophrenia and bipolar disorder.

The thesis work contains two projects under the same umbrella. The first project is to provide a detailed analysis on the behavior of interfacial water molecules at protein-protein complexes, in this case focusing on homodimeric complexes, and to investigate their effect with respect to different residue types. For that reason the homodimeric data-set, which includes high-resolution (≤ 2.30 Å) X-ray crystal structures of 252 (140 Biological & 112 Non-biological) protein complexes was chosen to explore fundamental differences between interfaces that Nature has “engineered” vs. compared to interfaces found under man-made conditions. The data set was comprised of 5391 water molecules where a maximum of 4 Å from both interfacing proteins. Our analysis is applied a suite of modeling tools based on HINT, a program for hydropathic analysis developed in our laboratory. HINT is based on the experimental measurement of the hydrophobic effect. The second project is designed to explore various means of suppressing the expression of human genes that play critical role in HIV pathogenesis. To achieve this aim, a data set of Affymetrix Human HG Focus Target Array, which measures the expression levels of HIV seronegative and seropositive individuals in human PBMCs, was analyzed with Pathway Studio 9.0 software. This work gives insight into the elucidation of the important mechanisms of human proteins interactions in HIV seropositive individuals and their implications. Hence, we found the kind and types of microRNAs that are suppressing the human genes which have great role for HIV replication in a cell.

The apicomplexan Cryptosporidium is a protozoan parasite of humans and other mammals. Cryptosporidium species cause acute gastro-enteritis and diarrheal disease in healthy humans and animals, and cause life-threatening infection in immuno-compromised individuals such as people with AIDS. It has a one-host life cycle and invades intestinal epithelial cells causing diarrhea, or more rarely the pulmonary epithelium. Cryptosporidium carries out all the asexual reproductive stages like several other apicomplexans. Current annotation of this organism predicts it to contain 3884 genes of which only 1581 genes have predicted functions. By using a combination of bioinformatics analysis, biochemical evidence, and high-throughput data, a genome-scale metabolic model of Cryptosporidium hominis is being constructed. The current model is comprised of approximately 213 gene-associated enzymes involved in major metabolic pathways including carbohydrate, nucleotide, amino acid, and energy metabolism. The approach of constructing a genome-scale model provides a link between the genotype and the phenotypic behavior of the organism, making it possible to study and predict behavior based upon genome content. This modeling approach provides an overview for evaluating missing components in a metabolic network and provides an analytical framework for interpreting data as more research becomes available. The goal of constructing this model is to systematically study and analyze various functional behaviors of C. hominis with respect to its stages in life cycle and pathogenicity.