Computational Biology Commons^™

Dynamic Rewiring Of Microrna Networks In The Brainstem Autonomic Control Circuits During Hypertension Development In The Female Spontaneously Hypertensive Rat, Alison Moss, Ankita Srivastava, Lakshmi Kuttippurathu, James S. Schwaber, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

We describe global microRNA (miRNA) changes in the central autonomic control circuits during the development of neurogenic hypertension. Using the female spontaneously hypertensive rat (SHR) and the normotensive Wistar Kyoto (WKY), we analyzed the dynamic miRNA expression changes in three brainstem regions-the nucleus of the solitary tract, caudal ventrolateral medulla, and rostral ventrolateral medulla-as a time series beginning at 8 wk of age before hypertension onset through to extended chronic hypertension. Our analysis yielded nine miRNAs that were significantly differentially regulated in all three regions between SHR and WKY over time. We collated computationally predicted gene targets of these nine …

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 …

Resolution Of Physics And Deep Learning-Based Protein Engineering Filters: A Case Study With A Lipase For Industrial Substrate Hydrolysis, Spencer Gardiner, Peter Dollinger, Filip Kovacic, Jörge Pietruszka, Daniel Ess, Karl-Erich Jaeger, Gunnar F. Schröder, Dennis Della Corte

Faculty Publications

Computational enzyme design remains a powerful yet imperfect tool for optimizing biocatalysts, especially when targeting non-natural substrates. Using design tools we investigated Pseudomonas aeruginosa LipA, a lipase with a flexible lid domain crucial for substrate binding and turnover, aiming to enhance its hydrolysis of the industrially relevant substrate Roche ester. We generated an initial set of single-point mutations based on structural proximity to the active site and evaluated their effects using a computational pipeline integrating molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and ensemble-based energy scoring. While we identified several active variants, attempts to rank them by activity …

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Ai-Powered Multi-Omics Integration For Predictive Modeling Of Genotype-Environment-Phenotype Relationships, You Wu

Dissertations, Theses, and Capstone Projects

This dissertation presents a series of machine learning frameworks for modeling genotype–environment–phenotype relationships through integrative predictive modeling of multi-omics data. The work addresses three major axes of biological complexity: modeling biological information transmission cross-levels from genes to proteins to phenotypes, predicting molecular features cross-scale from cells to tissues to organisms, and translating phenotypes cross-species from model systems to humans. Each proposed method also tackles key machine learning (ML) challenges in the biomedical domain, including data scarcity, domain shift, out-of-distribution (OOD) generalization, and hierarchical modeling. Specifically, this dissertation introduces five novel deep learning algorithms: MultiDCP predicts drug-induced transcriptomic and viability responses …

Exploring The Role Of Ecological Traits In Shaping Spatial Patterns Of Genetic Diversity, Rilquer Mascarenhas Da Silva

Dissertations, Theses, and Capstone Projects

The field of comparative phylogeography aims at uncovering common underlying causes for shared patterns of diversity and diversification at the population level. However, it is now widely accepted that intraspecific genetic diversity patterns can differ considerably across co-occurring taxa due to ecological processes acting at the population level (and influencing individual movement and abundance). To model the processes underlying lineage diversification and demographic shifts in response to environmental changes, phylogeographers and population geneticists are now faced with the challenge of incorporating measurements of species ecological traits into their molecular studies. This dissertation aimed at investigating how different types of ecological …

Performance Analysis Of Computational Methods For Predicting Protein Function In Rare Diseases, Aichetou Mohamed Sidiya, Hanin Alzaher, Razan Almahdi, Tayeb Brahimi

Effat Undergraduate Research Journal

Protein function prediction is crucial for understanding the underlying mechanisms of rare diseases. With the increasing availability of computational methods including machine learning-based approaches, network-based methods, and sequence-based methods, predicting protein functions has become more accessible. However, it is not clear which of these methods performs better or how they compare to each other in terms of accuracy, efficiency, and scalability. In this study, we evaluate several computational methods for predicting protein functions in rare diseases using key performance indicators (KPIs). We analyze the strengths and weaknesses of each method and provide recommendations for researchers and clinicians interested in using …

The Swib Domain-Containing Dna Topoisomerase I Of Chlamydia Trachomatis Mediates Dna Relaxation, Li Shen, Abigail R. Swoboda, Caitlynn Diggs, Shomita Ferdous, Andrew Terrebonne, Amanda Santos, Noel Wolf, Luis Lorenzo Carvajal, Guangming Zhong, Scot P. Ouellette, Yuk Ching Tse-Dinh

School of Graduate Studies Faculty Publications

Chlamydia trachomatis has a DNA topoisomerase I with a unique C-terminal domain (CTD) homologous to eukaryotic SWIB domains. This study focused on determining the function of the SWIB domain-containing TopA from C. trachomatis (CtTopA). We demonstrated that, despite the lack of sequence similarity at the CTDs between CtTopA and TopA from Escherichia coli (EcTopA), full-length CtTopA removed negative DNA supercoils in vitro and complemented the growth defect of a topA mutant of E. coli. CtTopA is less processive in DNA relaxation than EcTopA in dose-response and time course studies. An antibody generated against the SWIB domain of CtTopA specifically recognized …

Quantile Index Predictors Using R Package Hyper.Gam, Tingting Zhan, Misung Yi, Inna Chervoneva

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

MOTIVATION: Evaluation of single-cell protein expression from immunohistochemistry images is used increasingly in biomedical research. Many proteins are used solely for phenotyping cells in the tumor microenvironment. Other proteins with meaningfully quantitative expression levels provide so-called functional protein biomarkers. There is still a limited number of methods and software tools available for utilizing the entire distributions of single-cell expression levels.

RESULTS: We present the R package hyper.gam, providing a supervised learning framework for deriving biomarkers based on single-cell distribution quantiles. The single-cell data are first converted into sample quantile functions, which are then used as predictors in scalar-on-function regression models …

Predicting Genetic Interactions Using Functional Interaction Networks, Iulia Veronica Gheorghe

Dissertations and Theses (Open Access)

Mapping genetic interactions is central to understanding cellular systems and identifying therapeutic vulnerabilities, particularly in the context of cancer. Among these interactions, synthetic lethality, where simultaneous loss of two genes is lethal but loss of either alone is tolerated, offers a powerful framework for selectively targeting tumor-specific dependencies. In model organisms like S. cerevisiae, comprehensive double-knockout screens have revealed detailed genetic interaction maps, enabling systems-level insights into pathway structure, gene function, and cellular organization. Replicating this achievement in human cells, however, is complicated by the scale and complexity of the human genome. Recent advances in genome-wide CRISPR knockout screening have …

An Integrative Genomics Approach For The Discovery Of Potential Clinically Actionable Diagnostic And Prognostic Biomarkers In Colorectal Cancer, Mark Fertel, Duaa Mohammad Alawad, Chindo Hicks

School of Graduate Studies Faculty Publications

Background: Despite remarkable progress in clinical management of patients and intensified screening, colorectal cancer remains the second most common cause of cancer-related death in the United States. The recent surge of next generation sequencing has enabled genomic analysis of colorectal cancer genomes. However, to date, there is little information about leveraging gene expression data and integrating it with somatic mutation information to discover potential biomarkers and therapeutic targets. Here, we integrated gene expression data with somatic mutation information to discover potential diagnostic and prognostic biomarkers and molecular drivers of colorectal cancer. Methods: We used publicly available gene expression and somatic …

Cazyme Gene Cluster Diversity In Human Gut Microbiome, Yi Xing

Department of Food Science and Technology: Dissertations, Theses, and Student Research

In gut microbiome research, carbohydrate-active enzyme gene clusters (CGCs) have emerged as key functional units for understanding microbial glycan degradation. Unlike taxonomic or broad pathway annotations, CGCs offer gene-cluster-level resolution and capture substrate-specific microbial functions. However, their diversity and distribution in relation to host metabolic phenotypes, such as obesity, remain poorly characterized. This study tests the hypothesis that the composition and abundance of fiber-targeting CGCs vary between obese and healthy human gut microbiomes, reflecting distinct microbial carbohydrate utilization strategies. To examine this, we constructed a high-quality reference CGC dataset comprising 94,019 clusters from the Unified Human Gastrointestinal Genome and profiled …

A Rubric For Assessing Conformance To The Ten Rules For Credible Practice Of Modeling And Simulation In Healthcare, Alexandra Manchel, Ahmet Erdemir, Lealem Mulugeta, Joy Ku, Bruno Rego, Marc Horner, William Lytton, Jerry Myers, Rajanikanth Vadigepalli

Computational Medicine Center Faculty Papers

The power of computational modeling and simulation (M&S) is realized when the results are credible, and the workflow generates evidence that supports credibility for the context of use. The Committee on Credible Practice of Modeling & Simulation in Healthcare was established to help address the need for processes and procedures to support the credible use of M&S in healthcare and biomedical research. Our community efforts have led to the Ten Rules (TR) for Credible Practice of M&S in life sciences and healthcare. This framework is an outcome of a multidisciplinary investigation from a wide range of stakeholders beginning in 2012. …

Topodino: Self-Supervised Topological Representation Learning For Neuronal Morphologies, Yasser Binbisher

Master's Theses

Neuronal cell types are categorized by transcriptomic identity, yet their morphological heterogeneity defies this classification. In response, researchers have adopted unsupervised graph representation learning as a tool to reveal morphological variation within single-class transcriptomic types. However, the complex geometry of neuronal morphology—especially long axons and dense dendrites—challenges graph neural networks, which struggle with message propagation across extended structures. To mitigate this, current approaches enforce sub-sampling on neuronal graphs and omit axons entirely, sacrificing critical biological features for computational efficiency. To overcome this trade-off, this thesis introduces TopoDINO, a self-supervised, topology-aware representation learning model designed to preserve the full hierarchical organization …

Understanding How Genetic Mutations Induce Oligodendrocyte Progenitors To Become Cancer Cells, Dennis Huang

Dissertations, Theses, and Capstone Projects

Gliomas are the most devastating adult brain tumors characterized by poor survival rate and limited options for treatment. Previous studies have shown that they are very heterogeneous and can be further sub-classified based on their transcriptional signature and the presence of specific mutations. One such subtype, is the “proneural glioma”, which is characterized by the enrichment in oligodendrocyte progenitor cell (OPC) transcripts and mutations in genes encoding for the tumor suppressor P53 (Trp53) and for Platelet Derived Grow Factor (PDGF) signaling. Since OPCs are the most abundant proliferative population in the adult brain, in this …

Analytical Approaches For Identification Of Essential Genomes Of Plasmodium Knowlesi And Babesia Divergens, Sida Ye

Graduate Doctoral Dissertations

Apicomplexa constitute a large phylum of single-celled, obligate intracellular protozoan parasites. Notably, Plasmodium spp. and Babesia spp. are apicomplexan parasites that infect red blood cells. Plasmodium species are the causative agent of malaria and are transmitted by Anopheles mosquitoes, affecting large human populations, whereas Babesia spp., transmitted through the bite of Ixodes ticks cause babesiosis.

In this dissertation, we investigate the essential genome of these parasites using high-throughput transposon mutagenesis. Identifying the essential genome is key to finding new drug targets and understanding resistance mechanisms, a crucial pursuit given the rising resistance to frontline antimalarial drugs and the challenges …

Integrating Radiogenomics And Machine Learning In Musculoskeletal Oncology Care, Rahul Kumar, Kyle Sporn, Akshay Khanna, Phani Paladugu, Chirag Gowda, Alex Ngo, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

Department of Medicine Faculty Papers

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learning in improving diagnostic accuracy for bone and soft tissue tumors. We examine integrating quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to enable non-invasive tumor profiling. AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation …

Network Analysis Of Antimicrobial Resistance In Staphylococcus Aureus: Characterization Of Hub Genes And Their Functional Implications, Md Imran Hasan, Davida Smyth, Jeong Yang, Ashley Teufel

Masters Theses (Archived)

Antimicrobial resistance is a major cause of morbidity and mortality in patients with S. aureus infections. In this study, we analyzed genes, molecular mechanisms, and pathways driving drug resistance in S. aureus using network analysis. Using whole-genome sequencing (WGS) data and systems biology approaches, we identified 229 AMR-associated genes and constructed a protein-protein interaction network among these genes. Through network topology and functional enrichment analyses, we not only confirmed their association with resistance, but also highlighted the central roles of these genes in resistance pathways, such as efflux, target replacement, and target protection, which are directly linked to multiple drug …

Investigating The Effects Of Transcription Factor Binding And Genetic Variants In The Striatum Of Post-Mortem Cohorts With Opioid Use Disorder, Rajashree Chakraborty

Theses & Dissertations

The opioid crisis has emerged as one of the most pressing public health challenges of our time, with Opioid Use Disorder (OUD) affecting millions of lives across the globe. Studying OUD is not merely an academic pursuit but a critical necessity in addressing this multifaceted epidemic. The urgency of this research is underscored by the staggering prevalence of OUD, with an estimated 3.7% of U.S. adults requiring treatment in 2022 alone. Despite the availability of effective medications for OUD, a significant treatment gap persists, with only a quarter of those in need receiving these life-saving interventions. The far-reaching consequences of …

Developing A Small Molecule To Inhibit Hsf1 Expression In Cancer And Evaluating Natural Genetic Variation In Small Molecule Toxicity., Michaela Kendal Foley

Theses and Dissertations

Each year cancer affects nearly 20 million people worldwide and genetic differences across populations can impact cancer onset and progression. Specifically, tumors with high levels of HSF1, the master regulator of the cytoprotective heat shock response (HSR), are correlated with poor patient outcomes in multiple cancers such as prostate, breast, and melanoma. Subsequently, the development of pharmacological inhibitors of HSF1 represents a promising strategy for anticancer therapeutics. Using a luciferase-based transcriptional reporter, two small molecule libraries were screened for inhibitors of HSF1 expression in human embryonic kidney cells, yielding ten compounds that decrease HSF1 expression. To identify if cancer lines …