Computational Biology Commons^™

Characterization And Advancement Of Molluscan Venom Gland Cell Model Systems, James V. Parziale

Dissertations, Theses, and Capstone Projects

Venomous mollusks, including cone snails and coleoid cephalopods, produce rich repertoires of bioactive peptides with profound effects on cellular physiology, yet there are few tractable experimental systems that exist to study their development, maintenance, and venom secretion. Although therapeutics such as the cone snail peptide Ziconotide demonstrate the biomedical potential of molluscan venoms, progress in understanding how venom glands form, function, and evolve has been hindered by the lack of reliable husbandry and the complete absence of in vitro cell models. Modern comparative genomics has revealed widespread convergent evolution across venomous lineages, but molluscan datasets remain underrepresented, limiting insights into …

From Venom To Medicine: Harnessing Animal Toxins For Drug Discovery, Christine Vega, Ying Jia

Research Colloquium

Background: Drug development research has long focused on synthesizing novel therapeutics while overlooking isolation from naturally available sources such as animal venoms. Recently, the discovery of the therapeutic effects of highly bioavailable animal venom caused the field of venom research to soar in popularity. Animal venom serves as a natural, sophisticated tool optimized over millions of years by evolution with the ability to bind to ion channels such as nAChRs, non-specific ligand-gated ion channels distributed throughout the human nervous system. However, purifying individual venom toxins from crude venom is challenging due to its availability. Therefore, synthesizing large quantities of venom …

The Role Of Secondary And Tertiary Structure In The Cap-Independent Translation Of Fgf-9 And Hif-1-Alpha, Amanda Michelle Whittaker

Dissertations, Theses, and Capstone Projects

Under normoxic conditions, eukaryotes initiate translation of RNA through eIF4E recognition of the 5’ cap. However, under cellular stress, eukaryotic translation must be initiated through a 4E-independent, or “cap-independent” mechanism, involving eukaryotic initiation factor 4G (eIF4G) binding directly to the 5’ untranslated regions (5’ UTR) of the RNA. eIF4G binding then recruits the ribosome to the transcript. While this mechanism is useful for translation of apoptotic transcripts and transcripts involved in cell survival, cap-independent translation is also utilized by oncogenic RNA for tumorigenesis. Previous work by our lab and others has categorized this recruitment and initiation mechanism as either internal-ribosome-entry-site …

Noise Leads To The Perceived Increase In Evolutionary Rates Over Short Time Scales, Brian C. O'Meara, Jeremy M. Beaulieu

Biological Sciences Faculty Publications and Presentations

Across a variety of biological datasets, from genomes to conservation to the fossil record, evolutionary rates appear to increase toward the present or over short time scales. This has long been seen as an indication of processes operating differently at different time scales, even potentially as an indicator of a need for new theory connecting macroevolution and microevolution. Here we introduce a set of models that assess the relationship between rate and time and demonstrate that these patterns are statistical artifacts of time-independent errors present across ecological and evolutionary datasets, which produce hyperbolic patterns of rates through time. We show …

Identifying And Training Deep Learning Neural Networks On Biomedical-Related Datasets, Alan E. Woessner, Usman Anjum, Hadi Salman, Jacob Lear, Jeffrey T. Turner, Ross Campbell, Laura Beaudry, Justin Zhan, Lawrence E. Cornett, Susan Gauch, Kyle P. Quinn

Computer Science and Computer Engineering Faculty Publications and Presentations

This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and …

Computational Analysis Of O6-Methylated Guanine And Thioguanine Complexes, Kirsten Stinson, Michael Bowman

Lux et Fides: A Journal for Undergraduate Christian Scholars

DNA methylation occurring on the O⁶ position of guanine has been linked to the formation of cancer. DNA complexes with O⁶-methylated guanine have been studied experimentally, yet questions remain concerning the carcinogenic properties of O⁶-methylguanine. This present research explored the interaction between O⁶-methylguanine and its potential nucleobase pairs of cytosine and adenine in hopes of elucidating the mutagenic characteristics of O⁶-methylguanine. A variety of computational methods including Density Functional Theory (DFT), Symmetry Adapted Perturbation Theory (SAPT), Noncovalent Interaction (NCI) analysis, and Natural Bond Orbital (NBO) analysis were employed to comprehensively probe …

In Silico Analysis Of C-Type Lectins As Co-Infection Receptors Of Dengue And Chikungunya Viruses In Aedes Aegypti, Munawir Sazali, R. C. Hidayat Soesilohadi, Nastiti Wijayanti, Tri Wibawa, Arif Nur Muhammad Ansori

Makara Journal of Science

Aedes aegypti is a primer vector of dengue virus (DENV) and chikungunya virus (CHIKV). The susceptibility of mosquitoes to DENV and CHIKV depends on their recognition receptor of pathogens. C-type lectins (CTLs) are an important mediator of virus infection in A. aegypti. This study aims to identify potential receptors and determine the binding affinity between ligand–receptor interaction, CTLs and virus envelopes (DENV-1, 2, 3, and 4 and CHIKV) interaction based on in silico analysis. Sample sequences were obtained from GenBank (NCBI), and 10 CTLs were acquired from VectorBase. Homology modeling based on a minimum standard of 20% was processed …

Acetate Metabolism In The Fungal Pathogen Cryptococcus Neoformans, Oly Ahmed

All Dissertations

Cryptococcus neoformans is an environmental basidiomycetous fungus with a worldwide distribution and a wide range of habitats. Inhalation of the desiccated yeasts or spores of C. neoformans often leads to opportunistic pulmonary infections in immunocompromised individuals, and in severe cases causes lethal meningitis following hematogenous dissemination. During infection, depending on the tissue and disease state, the invading fungi experience a range of nutrient microenvironments within the host body. As a result, rapid metabolic adaptations geared towards efficient utilization of carbon sources alternative to glucose become one of the prime determinants of survival and growth for the pathogen. Incidentally, cryptococcal infection …

Radiation Exposure Determination In A Secure, Cloud-Based Online Environment, Ben C. Shirley, Eliseos J. Mucaki, Peter Rogan

Biochemistry Publications

Rapid sample processing and interpretation of estimated exposures will be critical for triaging exposed individuals after a major radiation incident. The dicentric chromosome (DC) assay assesses absorbed radiation using metaphase cells from blood. The Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI) identifies DCs and determines radiation doses. This study aimed to broaden accessibility and speed of this system, while protecting data and software integrity. ADCI Online is a secure web-streaming platform accessible worldwide from local servers. Cloud-based systems containing data and software are separated until they are linked for radiation exposure estimation. Dose estimates are identical to ADCI …

Proteomic Analysis Of The Expression Of Masp1 Dragline Silk Protein In E. Coli, Sophie Rae Pazzo, Rajan Amit Patel, Kamrin Athwal, Edward Kim

Pacific Undergraduate Research and Creativity Conference (PURCC)

No abstract provided.

Annual Faculty Research Symposium 2022, Oakwood University

Proceedings

No abstract provided.

Improved Radiation Expression Profiling In Blood By Sequential Application Of Sensitive And Specific Gene Signatures, Eliseos J. Mucaki, Ben C. Shirley, Peter K. Rogan

Biochemistry Publications

Purpose. Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning based signatures (with 8 to 20% misclassification rates). These signatures can quantify therapeutically-relevant as well as accidental radiation exposures. The prodromal symptoms of Acute Radiation Syndrome (ARS) overlap those present in Influenza and Dengue Fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy.

Methods. This study investigated recall by previous and novel radiation signatures independently derived …

Mucin And Splice Variant Profiles Of Pancreatic Adenocarcinoma Predict Patient Survival And Subtyping, Christopher M. Thompson

Theses & Dissertations

PDAC is a pancreatic epithelial malignancy and demonstrates aggressive progression and bleak patient prognosis. Despite decades of research, the evolution of novel diagnostics and intervention modalities for PDAC is stagnant. This dissertation explores the characteristic aberrant and elevated expression of mucins in PDAC. Beginning with the hypothesis that mucins are associated with disease aggressiveness, analysis of PDAC patient survival in TCGA revealed no associations between single mucin expression and patient survival. This led to the underlying issue of PDAC tumor cellularity since this disease demonstrates variability in the proportion of cancer cells within the tumor. Tumor purity assessed with the …

Applications Of Machine Learning In Microbial Forensics, Ryan B. Ghannam

Dissertations, Master's Theses and Master's Reports

Microbial ecosystems are complex, with hundreds of members interacting with each other and the environment. The intricate and hidden behaviors underlying these interactions make research questions challenging – but can be better understood through machine learning. However, most machine learning that is used in microbiome work is a black box form of investigation, where accurate predictions can be made, but the inner logic behind what is driving prediction is hidden behind nontransparent layers of complexity.

Accordingly, the goal of this dissertation is to provide an interpretable and in-depth machine learning approach to investigate microbial biogeography and to use micro-organisms as …

Single-Fluorophore Sensors For Mechanical Force In Living Cells, Sarah Kricheff

Honors Scholar Theses

Mechanotransduction is the process by which a mechanical stimulus is converted to a cellular signal. This process is heavily influential of cell morphology, differentiation, and behavior. However, altered levels of mechanical stimuli are also found in many pathological contexts. For example, cancerous cells have stiffer surrounding tissue than healthy cells, and research suggests that this alters cell behavior and promotes metastasis. Despite these findings, the cellular processes behind these signaling alterations remain widely unknown. Understanding these cascades is critical, as involved proteins can give us a deeper understanding of the role of mechanotransduction, and certain proteins can potentially be targeted …

Pathway-Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Jem Bagchee-Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan

Biochemistry Publications

No abstract provided.

Machine Learning Applications For Drug Repurposing, Hansaim Lim

Dissertations, Theses, and Capstone Projects

The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected …

Deepep: A Deep Learning Framework For Identifying Essential Proteins, Min Zeng, Min Li, Fang-Xiang Wu, Yaohang Li, Yi Pan

Computer Science Faculty Publications

Background: Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results: We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. …

Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan

COBRA Preprint Series

One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease's process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous data sets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards …

Supervised Dimension Reduction For Large-Scale "Omics" Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards, Lauren Spirko-Burns, Karthik Devarajan

COBRA Preprint Series

The past two decades have witnessed significant advances in high-throughput ``omics" technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrence-free survival, with the goal of developing a predictive ``omics" profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential …

Transcription Factor Binding Site Clusters Identify Target Genes With Similar Tissue-Wide Expression And Buffer Against Mutations., Peter Rogan, Ruipeng Lu

Biochemistry Publications

Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were …

Bayesian Analytical Approaches For Metabolomics : A Novel Method For Molecular Structure-Informed Metabolite Interaction Modeling, A Novel Diagnostic Model For Differentiating Myocardial Infarction Type, And Approaches For Compound Identification Given Mass Spectrometry Data., Patrick J. Trainor

Electronic Theses and Dissertations

Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, …

Quantitative Studies Of Amyloidogenic Protein Residue Interaction Networks And Abnormal Ammonia Metabolism In Neurotoxicity And Disease, Jeddidiah Griffin

Electronic Theses and Dissertations

Investigating similarities among neurological diseases can provide insight into disease processes. Two prominent commonalities of neurological diseases are the formation of amyloid deposits and altered ammonia and glutamate metabolism. Computational techniques were used to explore these processes in several neurological diseases. Residue interaction networks (RINs) abstract protein structure into a series of nodes (representing residues) and edges (representing connections between residues likely to interact). Analyzing the RINs of monomeric forms of amyloidogenic proteins for common network features revealed similarities not previously known. First, amyloidogenic variants of lysozyme were used to demonstrate the usefulness of RINs to the study of amyloidogenic …

Prediction Of Lncrna-Disease Associations Based On Inductive Matrix Completion, Chengqian Lu, Mengyun Yang, Feng Luo, Fang-Xiang Wu, Min Li, Yi Pan, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA–disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA–disease associations, especially for novel lncRNAs, remains challenging.

Results: In this study, we propose a method (named SIMCLDA) for predicting potential lncRNA– disease associations based on inductive matrix completion. We compute Gaussian interaction profile kernel of lncRNAs from known lncRNA–disease interactions and functional similarity of diseases based on disease–gene and gene–gene onotology …

Mrub_1325, Mrub_1326, Mrub_1327, And Mrub_1328 Are Orthologs Of B_3454, B_3455, B_3457, B_3458, Respectively Found In Escherichia Coli Coding For A Branched Chain Amino Acid Atp Binding Cassette (Abc) Transporter System, Bennett Tomlin, Adam Buric, Dr. Lori Scott

Meiothermus ruber Genome Analysis Project

In this project we investigated the biological function of the genes Mrub_1325, Mrub_1326, Mrub_1327, and Mrub_1328 (KEGG map number 02010). We predict these genes encode components of a Branched Chain Amino Acid ATP Binding Cassette (ABC) transporter: 1) Mrub_1325 (DNA coordinates 1357399-1358130 on the reverse strand) encodes the ATP binding domain; 2) Mrub_1326 (DNA coordinates 1358127-1359899 on the reverse strand) encodes the ATP-binding domain and permease domain; 3) Mrub_1327 (DNA coordinates 1359899-1360930 on the reverse strand) encodes a permease domain; and 4)Mrub_1328 (DNA coordinates 1711022-1712185 on the reverse strand) encodes the substrate binding domain. This system is not predicted to …

Discovery And Validation Of Information Theory-Based Transcription Factor And Cofactor Binding Site Motifs., Ruipeng Lu, Eliseos J Mucaki, Peter K Rogan

Biochemistry Publications

Data from ChIP-seq experiments can derive the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. We analyzed 765 ENCODE ChIP-seq peak datasets of 207 human TFs with a novel motif discovery pipeline based on recursive, thresholded entropy minimization. This approach, while obviating the need to compensate for skewed nucleotide composition, distinguishes true binding motifs from noise, quantifies the strengths of individual binding sites based on computed affinity and detects adjacent cofactor binding sites that coordinate with the targets of primary, immunoprecipitated TFs. We obtained contiguous and bipartite information theory-based position weight matrices (iPWMs) for 93 sequence-specific TFs, …

Annotation And Identification Of Several Glycerolipid Metabolic Related Ortholog Genes; Mrub_0437, Mrub_1813 And Mrub_2759 In The Organism Meithermus Ruber And Their Predicted Respective Orthologs B3926, B4042 And Bo514 Found In E.Coli., Abdul Rahman Abdul Kader, Dr. Lori R. Scott

Meiothermus ruber Genome Analysis Project

We predict Mrub_0437 encodes the enzyme glycerol kinase (DNA coordinates [417621..419183), which is an intermediary step of the glycerolipid metabolic pathway (KEGG map00561), It catalyzes the conversion of glycerol to sn-Glycerol-3-phosphate. The E. coli K12 MG1655 ortholog is predicted to be b3926.

We predict Mrub_1813 encodes the enzyme diacylglycerol kinase (DNA coordinates [1864659..1865063), which is an intermediary step of the glycerolipid metabolic pathway (KEGG map00561), It catalyzes the conversion of 1,2-diacyl-sn-glycerol to 1,2-diacyl-sn-glycerol 3-phosphate. The E. coli K12 MG1655 ortholog is predicted to be b4042.

We predict Mrub_2759 encodes the enzyme glycerol kinase (DNA coordinates [2799712..2800665), which is an intermediary …

Comparing An Atomic Model Or Structure To A Corresponding Cryo-Electron Microscopy Image At The Central Axis Of A Helix, Stephanie Zeil, Julio Kovacs, Willy Wriggers, Jing He

Computer Science Faculty Publications

Three-dimensional density maps of biological specimens from cryo-electron microscopy (cryo-EM) can be interpreted in the form of atomic models that are modeled into the density, or they can be compared to known atomic structures. When the central axis of a helix is detectable in a cryo-EM density map, it is possible to quantify the agreement between this central axis and a central axis calculated from the atomic model or structure. We propose a novel arc-length association method to compare the two axes reliably. This method was applied to 79 helices in simulated density maps and six case studies using cryo-EM …

Design Of Novel Ion Channel Modulators, Vladimir Yarov-Yarovoy

Science Seminar Series

Function and modulation of neuronal sodium channels are critical for the neuromodulation of electrical excitability and synaptic transmission in neurons - the basis for many aspects of signal transduction, learning, memory and physiological regulation. Mutations in neuronal voltage-gated sodium channel genes are responsible for various human neurological disorders. Furthermore, human neuronal voltage-gated sodium channels are primary targets of therapeutic drugs used as local anesthetics and for treatment of neurological and cardiac disorders. Yarov-Yarovoy's lab is working on rational design of novel therapeutically useful blockers of voltage-gated sodium channels for treatment of pain and epilepsy. Serious, chronic pain affects at least …

A Pipeline For Creation Of Genome-Scale Metabolic Reconstructions, Shaun W. Norris

Theses and Dissertations

The decreasing costs of next generation sequencing technologies and the increasing speeds at which they work have lead to an abundance of 'omic datasets. The need for tools and methods to analyze, annotate, and model these datasets to better understand biological systems is growing. Here we present a novel software pipeline to reconstruct the metabolic model of an organism in silico starting from its genome sequence and a novel compilation of biological databases to better serve the generation of metabolic models. We validate these methods using five Gardnerella vaginalis strains and compare the gene annotation results to NCBI and the …