Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Identification And Characterization Of Novel Immune Modulating Genes In Parkinson's Disease, Kathleen Chaundy Paul Murphy

Dartmouth College Ph.D Dissertations

Parkinson’s disease (PD) is a progressive age-related neurodegenerative disorder characterized by both motor and non-motor symptoms. The poorly understood prodromal period, decades-long progression, and disease-phenotype heterogeneity continue to impede the development of preventive and curative therapies. A growing appreciation of immune system changes during the progression of PD suggests that evaluating peripheral immune cells may help identify signatures relevant to disease etiology. We aimed to expand our understanding of the peripheral immune compartment in PD via two lines of investigation: (1) by employing single-cell RNA sequencing to profile the transcriptomes of peripheral blood mononuclear cells (PBMCs) from a cohort of …

From Fair To Cure: Guidelines For Computational Models Of Biological Systems, Herbert M. Sauro, Eran Agmon, Michael L. Blinov, John H. Gennari, Joseph L. Hellerstein, Adel Heydarabadipour, Bartholomew E. Jardine, Elebeoba May, David P. Nickerson, Lucian P. Smith, Gary D. Bader, Frank T. Bergmann, Patrick M. Boyle, Andreas Dräger, James R. Faeder, Song Feng, Juliana Freire, Fabian Fröhlich, James A. Glazier, Thomas E. Gorochowski, Tomas Helikar, Henning Hermjakob, Stefan Hoops, Peter Hunter, Princess I. Imoukhuede, Sarah M. Keating, Matthias König, Reinhard Laubenbacher, Leslie M. Loew, Carlos F. Lopez, William W. Lytton, Rahuman S. Malik-Sheriff, Andrew Mcculloch, Pedro Mendes, Lealem Mulugeta, Chris J. Myers, Jerry G. Myers, Anna Niarakis, David D. Van Niekerk, Brett G. Olivier, Alexander A. Patrie, Ellen M. Quardokus, Nicole Radde, Johann M. Rohwer, Sven Sahle, James C. Schaff, Falk Schreiber, T. J. Sego, Janis Shin, Jacky L. Snoep, Rajanikanth Vadigepalli, H. Steven Wiley, Dagmar Waltemath, Ion I. Moraru

Computational Medicine Center Faculty Papers

Guidelines for managing scientific data have been established under the FAIR principles, requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model …

A Framework For Characterizing The Peripheral Immune Isonome Using Long-Read Single-Cell Rna Sequencing And Its Relevance To Neurological Disease, Patricia Hayes Doyle

Theses and Dissertations--Neuroscience

Long-read single-cell RNA sequencing provides an opportunity to understand human health and disease at isoform resolution, revealing cellular diversity and disease mechanisms difficult to resolve with bulk or short-read methodologies.

Using a modified PIPseq workflow and computational pipeline adapted for Oxford Nanopore (ONT) sequencing, we profiled isoform usage across immune cells, integrating marker expression and isoform discovery, generating the largest long-read single-cell dataset of human immune cells from a single individual to date. We identified non-canonical protein-coding variants of GZMB and CD3G enriched in unexpected cell types. We also discovered novel transcripts from CMC1 and LYAR with cell-type-specific signatures that …

Computational And Ai Frameworks For Identifying Key Regulatory Genes And Their Target Genes In Plants And Humans, Md Khairul Islam

Dissertations, Master's Theses and Master's Reports

This dissertation presents computational and AI-driven frameworks for identifying key regulatory genes and their downstream targets across plant and human biological systems. Three studies address distinct challenges in genomic regulation using advanced machine learning and bioinformatics approaches.

The first study introduces DyGAF (Dynamic Gene Attention Focus), a dual-attention transformer framework that identifies and ranks disease-relevant biomarker genes by simultaneously modeling independent molecular responses and interdependent regulatory network behavior. Two attention models provide complementary perspectives on gene importance and are fused through a novel combination metric. Applied to COVID-19 nasopharyngeal swab profiles, the attention-weighted representations achieved 94.23% classification accuracy, high sensitivity, …

A Persistent Homology Framework For Scrna-Seq: Assessing Clustering Robustness And Quantifying Preprocessing And Integration Effects On Topological Features., Jonah Daneshmand

Electronic Theses and Dissertations

As single-cell RNA sequencing (scRNA-seq) data expands, robust methods for integrating diverse datasets are critical. This dissertation applies Persistent Homology (PH), a technique from Topological Data Analysis (TDA), to a collection of scRNA-seq datasets spanning eight tissue types to quantify how data integration affects topological features and biological interpretability. We assessed global topological structure using Betti curves, Euler characteristics, and persistence landscapes across raw, normalized, and integrated data representations. Our analysis revealed a performance inversion: while conventional methods excelled on unintegrated data, high-granularity topological methods, particularly those sensitive to global data structure, became superior after integration. This suggests a synergy …

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 …

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 …

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 …

Integration Of Multi-Omics Datasets Evaluating Structural And Functional Features Of The Gut Microbiome In People With Hiv (Pwh)., Aakarsha Vijayakumar Rao

Electronic Theses and Dissertations

Gut dysbiosis characterized by reduced abundance of beneficial butyrate-producing bacteria has been independently linked to HIV-1 infection and heavy alcohol drinking. Further, gut dysbiosis results in loss of gut barrier integrity, microbial translocation and host-specific systemic inflammation. Therefore, to evaluate the functional consequences of structural changes in the gut microbiome, integrated data analysis is imperative. In this dissertation, we perform integrated analyses using data from multi-omics platforms to examine the structural and functional features of the gut microbiome of PWH. Gut microbiome composition was evaluated by sequencing V4 region of 16S rDNA, concentrations of metabolites and host-specific immune markers were …

Hierarchical Lineage Tracing To Unravel Mechanisms Of Cancer Treatment Resistance, Rachel Danielle Saxe

Dartmouth College Ph.D Dissertations

Cancer cells adapt to treatment, leading to the emergence of clones that are more aggressive and resistant to anti-cancer therapies. We have a limited understanding of the development of treatment resistance as we lack technologies to map the evolution of cancer under the selective pressure of treatment. To address this, we developed a hierarchical, dynamic lineage tracing method called FLARE (Following Lineage Adaptation and Resistance Evolution). We use this technique to track the progression of acute myeloid leukemia (AML) cell lines through exposure to Cytarabine (AraC), a front-line treatment in AML, in vitro and in vivo. We map distinct cellular …

Taming Biological Complexity Through The Use Of Symmetries, Luis A. Álvarez-García

Dissertations, Theses, and Capstone Projects

The study of biological systems is, inherently, the study of very complex systems. This is essentially due to the fact that they are made up of numerous, often very complicated, interactions between an extensive number of components. Often necessitating an abundance of quantitative parameters and details for a precise description. The human brain for ex- ample, consisting of ∼ 80 billion neurons with ∼ 800 to 100 trillion connections between them, each of them depending on a large set of parameters. Even simpler examples such as bacterial organisms, such as E. coli and B. subtilis, which we focus on …

From Sampling To Simulating: Single-Cell Multiomics In Systems Pathophysiological Modeling, Alexandra Manchel, Michelle M. Gee, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be …

Timigp: A Computational Framework To Determine The Tumor Immune Microenvironment Associated With Prognosis And Immunotherapy Response, Chenyang Li

Dissertations and Theses (Open Access)

Accumulating evidence has suggested that the tumor immune microenvironment (TIME) drastically impacts cancer patients’ clinical outcomes, including prognosis and immunotherapy response. However, understanding TIME remains challenging due to its complexity and heterogeneity. In this dissertation, we introduce TimiGP (Tumor Immune Microenvironment Illustration based on Gene Pairs), a computational framework designed to address this challenge. Leveraging single-cell RNA-seq (scRNA-seq) and bulk gene expression data alongside clinical information, TimiGP constructs a cell-cell interaction network that elucidates the relationship between immune cell function and relevant clinical outcomes, such as prognosis and treatment response. With immunological insights, these cell-cell interactions also facilitate the development …

Early Onset Alzheimer’S Disease Markers In Mouse Hippocampus Unveiled By Single-Cell Transcriptomic Analysis Following Cranial Radiotherapy, Tuba Aksoy

Dissertations and Theses (Open Access)

Cranial radiation therapy plays an integral role in the treatment of brain tumors but can lead to progressive cognitive deficits in survivors by mechanisms that are poorly understood. To develop preventive or mitigative strategies, it is crucial to better understand the underlying pathogenesis of radiation-induced cognitive impairments. The study investigated single-cell transcriptomics and DNA methylation changes as potential drivers of persistent cellular dysfunction after radiation exposure, specifically concentrating on the CA1-3 regions of the hippocampus and the prefrontal cortex due to their role in cognitive functions. Thirteen-week-old mice underwent whole-brain radiation at clinically relevant doses. Following whole-brain radiation, an assessment …

Computational Drug Repositioning And 3d Skin-Like Tissues Identify Anti-Fibrotic Targets For Systemic Sclerosis (Ssc), Dillon Popovich

Dartmouth College Ph.D Dissertations

Systemic Sclerosis (SSc) is a rare autoimmune disease characterized by dermal and internal organ fibrosis, including heart, lungs, and gastrointestinal tract, and autoantibody formation. Although disease etiology is currently unknown, like other autoimmune diseases, SSc likely develops due to environmental factor exposure in genetically susceptible individuals. Fibrotic diseases are notoriously difficult to treat. Coupled with the autoimmune aspect, SSc is difficult to study scientifically due to the lack of complex disease models that can recapitulate the immune-fibrotic axis of the disease. Due to this, there are only two FDA approved medical treatments for SSc approved for symptomatic treatment of SSc …

Single Cell Pharmacodynamic Modeling Of Cancer Cell Lines, Arnab Mutsuddy

All Dissertations

Cancer is one of the leading causes of disease related death worldwide. Since the discovery of the genomic origins of cancer, targeted therapy has been developed towards specific mutations implicated for oncogenic transformation. However, current standard-of-care for mapping cancer patients to efficacious drug combination is often inadequate. The pathophysiology of tumor progression relies on the dysregulation of biomolecular pathways of which the topology and the dynamics challenge prognosis. Moreover, the overall genomic instability involved in disease states and the resulting inter-patient as well as intra-tumoral heterogeneity challenge rationalization of therapy and clinical decision-making. It highlights the need for the use …

Modeling Nonsegmented Negative-Strand Rna Virus (Nnsv) Transcription With Ejective Polymerase Collisions And Biased Diffusion, Felipe-Andres Piedra

Research Symposium

Background: The textbook model of NNSV transcription predicts a gene expression gradient. However, multiple studies show non-gradient gene expression patterns or data inconsistent with a simple gradient. Regarding the latter, several studies show a dramatic decrease in gene expression over the last two genes of the respiratory syncytial virus (RSV) genome (a highly studied NNSV). The textbook model cannot explain these phenomena.

Methods: Computational models of RSV and vesicular stomatitis virus (VSV – another highly studied NNSV) transcription were written in the Python programming language using the Scientific Python Development Environment. The model code is freely available on GitHub: …

Caribbean Reef-Building Coral-Symbiodiniaceae Network: Identifying Symbioses Critical For System Stability In A Changing Climate, Shaman Patel

All HCAS Student Capstones, Theses, and Dissertations

Increasing global ocean temperatures and frequency of marine heatwaves pose dire consequences for coral reefs. High temperatures often lead to disruptions in coral symbiosis resulting in coral bleaching, increasing the mortality of corals. However, corals can potentially avoid bleaching peril by associating with thermally tolerant symbionts. Here we provide a tool for understanding symbiosis network stability of Caribbean reef-building corals. We created a network of Caribbean hermatypic corals and their associated Symbiodiniaceae phylotypes. A bleaching model was applied to this network to test for resilience and robustness (R50) to thermal stress. It was also layered with trait data for coral …

Data-Driven Biomarker Panel Discovery In Ovarian Cancer Using Heterogenous Data Fusion On Exosomal And Non-Exosomal Microrna Expression Data, Paritra Mandal

All Dissertations

Ovarian cancer (OC) is an aggressive gynecological cancer and is currently the 5th leading cause of deaths due to cancer in women. High mortality rates are attributable to the vague pathogenesis and asymptomatic nature of the early stages. The development of a liquid biopsy for routine OC screening could help identify the disease at an earlier stage, making treatments more likely to be effective thereby increasing survival rates. Exosomes, small (~100nm) extracellular vesicles present in body fluids, have been shown to contain cancer-progression, onset, and related factors, making them good candidates for use in liquid biopsies. However, to date, only …

Methods And Tools To Improve Performance Of Plant Genome Analysis, Drew Ferrell

Theses and Dissertations

Multi -omics data analysis and integration facilitates hypothesis building toward an understanding of genes and pathway responses driven by environments. Methods designed to estimate and analyze gene expression, with regard to treatments or conditions, can be leveraged to understand gene-level responses in the cell. However, genes often interact and signal within larger structures such as pathways and networks. Complex studies guided toward describing dynamic genetic pathways and networks require algorithms or methods designed for inference based on gene interactions and related topologies. Classes of algorithms and methods may be integrated into generalized workflows for comparative genomics studies, as multi -omics …

Symmetry-Inspired Analysis Of Biological Networks, Ian Leifer

Dissertations, Theses, and Capstone Projects

The description of a complex system like gene regulation of a cell or a brain of an animal in terms of the dynamics of each individual element is an insurmountable task due to the complexity of interactions and the scores of associated parameters. Recent decades brought about the description of these systems that employs network models. In such models the entire system is represented by a graph encapsulating a set of independently functioning objects and their interactions. This creates a level of abstraction that makes the analysis of such large scale system possible. Common practice is to draw conclusions about …

Understanding Potassium Toxicity Stress Responses Of The Extremophyte Schrenkiella Parvula Using Systems Biology Approaches, Pramod Pantha

LSU Doctoral Dissertations

Schrenkiella parvula is an extremophyte model closely related to Arabidopsis thaliana and Brassica crops. Its natural habitat includes shores of saline lakes in the Irano-Turanian region. It has adapted to grow in soils rich in multiple salts including Na⁺ and K⁺. I have investigated the genetic basis for high K⁺ tolerance in plants using S. parvula as a stress tolerant model compared to the premier plant model, Arabidopsis thaliana which is highly sensitive to salt stresses using physiological, ionomic, transcriptomic, and metabolomic approaches. Under high K⁺ stress, root system architecture changes significantly compared to control …

Impact Of Intratumor Heterogeneity And The Tumor Microenvironment In Shaping Tumor Evolution And Response To Therapy, Akash Mitra

Dissertations and Theses (Open Access)

Intratumor heterogeneity (ITH) is a crucial challenge in cancer treatment. The genotypic and phenotypic heterogeneity underlying diverse cancer types leads to subclonal variation, which may result in mixed or failed response to therapy. The heterogeneity at the tumor level, along with the tumor microenvironment (TME), often shapes tumor evolution and ultimately clinical outcome. Given that modern treatment paradigms increasingly expose patients with metastatic disease to multiple treatment modalities through the course of their disease, there exists a need to characterize robust and predictive biomarkers of response to therapy. In order to accurately characterize tumor evolution, we need to account for …

Biases And Blind-Spots In Genome-Wide Crispr-Cas9 Knockout Screens, Merve Dede

Dissertations and Theses (Open Access)

Adaptation of the bacterial CRISPR-Cas9 system to mammalian cells revolutionized the field of functional genomics, enabling genome-scale genetic perturbations to study essential genes, whose loss of function results in a severe fitness defect. There are two types of essential genes in a cell. Core essential genes are absolutely required for growth and proliferation in every cell type. On the other hand, context-dependent essential genes become essential in an environmental or genetic context. The concept of context-dependent gene essentiality is particularly important in cancer, since killing cancer cells selectively without harming surrounding healthy tissue remains a major challenge. The toxicity of …

Evolution And Clinical Relevance Of Vaginal Bifidobacterium Breve In The Vaginal Environment, Nicole R. Jimenez

Theses and Dissertations

The vaginal microbiome is important in reproductive health and disease and is often dominated by lactobacilli. However, there are many other community state types dominated by single species that are still not well characterized. Bifidobacterium species such as Bifidobacterium breve and Bifidobacterium longum have been observed to dominate vaginal profiles. Bifidobacteria are important in infant gut development, but little is known about their role in vaginal health. Thus, we sought to use a multisystem approach to better characterize Bifidobacterium species present in the vaginal microbiome with an emphasis on B. breve. Eight strains of B. breve were isolated from vaginal …

Investigation Of Proliferation Suppressors In Genetic Fitness Screens, Walter Frank Lenoir Iv

Dissertations and Theses (Open Access)

Innovation of CRISPR gene-editing technology has provided scientists genome manipulation tools that allowed rapid advancement of scientific capabilities and thus improved our ability to systematically study mammalian genetic functional profiles. Genome-wide CRISPR knockout screens conducted in collections of human cell lines can knock out genes at multiple loci, and have provided new insights into functional roles for independent genes. This method has launched massive efforts in looking across genetic backgrounds for context specific genetic vulnerabilities within cancer. Much of the research effort thus far has been spent on optimizing phenotype distinctions between essential, genes required for cell fitness, and non-essential, …

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 …

Metabolic Network Analysis Of Filamentous Cyanobacteria, Daniel Alexis Norena-Caro

LSU Doctoral Dissertations

Cyanobacteria were the first organisms to use oxygenic photosynthesis, converting CO₂ into useful organic chemicals. However, the chemical industry has historically relied on fossil raw materials to produce organic precursors, which has contributed to global warming. Thus, cyanobacteria have emerged as sustainable stakeholders for biotechnological production. The filamentous cyanobacterium Anabaena sp. UTEX 2576 can metabolize multiple sources of Nitrogen and was studied as a platform for biotechnological production of high-value chemicals (i.e., pigments, antioxidants, vitamins and secondary metabolites). From a Chemical engineering perspective, the biomass generation in this organism was thoroughly studied by interpreting the cell as a microbial …