Systems Biology Commons^™

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 …

Drifting Neurons, Stable Geometry: Population Representations Of Instrumental Behavior In The Dorsomedial Striatum, Arati Sharma

Dartmouth College Ph.D Dissertations

The brain reliably supports learned behaviors over long periods of time despite continual biological changes that alter the activity of individual neurons, and the connections between them. For learned behaviors to persist, the information stored in neural populations must remain accessible even when the activity of individual neurons changes. Understanding how the brain achieves this is a fundamental challenge in neuroscience. The dorsomedial striatum (DMS) is critical for instrumental behavior, encoding information about actions, outcomes, and their relationships. Although extensive work has characterized the role of the DMS during learning and performance, less is known about how task-related information is …

Deconstructing Systemic Sclerosis Heterogeneity: From Patient Subtypes To Pathogenic Fibroblasts, Rezvan Parvizi

Dartmouth College Ph.D Dissertations

This thesis advances heterogeneity as the central barrier both to interpreting therapeutic response and to identifying the molecular regulators that drive fibrosis in systemic sclerosis (SSc). In SSc, heterogeneity is manifest at every level at which the disease can be measured: clinically, in the variable extent of skin and organ involvement; molecularly, in the distinct gene-expression programs that distinguish otherwise comparable patients; and cellularly, in the diverse fibroblast populations from which the fibrotic signal ultimately arises.

Leveraging longitudinal skin transcriptomics from a large cohort treated with mycophenolate mofetil (MMF), the first study demonstrates that intrinsic molecular subtypes are dynamic rather …

The Mayfly Newsletter, Donna Giberson

The Mayfly Newsletter

The Mayfly Newsletter is the official newsletter of the Permanent Committee of the International Conferences on Ephemeroptera.

Accuracy Of Parameter Estimation For A Simple Gene Regulatory Network Model Is Sensitive To Network Motif, Number Of Parameters Estimated, And Magnitude And Direction Of Regulatory Relationships, Nikki C. Chun, Kam Dahlquist

Honors Thesis

A gene regulatory network (GRN) is a set of transcription factors that regulate the expression of genes encoding other transcription factors. The dynamics of a GRN explain how gene expression changes over time. GRNmap is a MATLAB software package that uses ordinary differential equations to model dynamics of small-scale GRNs. We used the program to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in three related literature-derived GRNs based on yeast cold shock microarray data previously collected in the Dahlquist Lab. We noticed large differences in estimated weight values when 1-2% of the expression values were …

The Dynamics Of Synchrony On Replicating Biological Populations, Montse Torres Garcia, Kevin Mcgoff, Francis Motta, Breschine Cummins, Steve Haase

Biology and Medicine Through Mathematics Conference

No abstract provided.

Ai×Life 2026: Advances In Computational Biology Across Scales For Life & Health Sciences, Suma Mohan S., Devika N. T., Pranavathiyani G.

Conference Proceedings

Book of Abstracts of the International Conference, AIxLife 2026: Advances in Computational Biology Across Scales for Life & Health Sciences, held on 17–18 April 2026, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, INDIA 

About the Conference

Conventional computational biology laid the foundation for biological data analysis through statistical models, algorithms, and curated databases, enabling insights into genes, proteins, and pathways. Today, the integration of artificial intelligence, spanning machine learning, deep learning, and large language models (LLMs) is driving a transformative shift across biological scales, from atomic and molecular interactions to pathways, cellular systems, organisms, and populations.

AI-powered approaches …

Recent Wetland Elevation Dynamics In Coastal Louisiana, Usa, Elizabeth Harris

LSU Master's Theses

Coastal marshes globally are increasingly vulnerable to accelerating relative sea-level rise (RSLR), which threatens their capacity to maintain elevation through feedbacks among sediment supply, vegetation productivity, hydrology, and soil processes. Although many marshes can persist under moderate rates of sea-level rise through vertical accretion and belowground biomass production, this resilience is strongly constrained by sediment availability and subsurface processes such as autocompaction and organic matter decomposition. High accretion is often assumed to confer marsh resilience; however, subsurface processes, particularly autocompaction driven by surface loading, can substantially offset elevation gains. Coastal Louisiana experiences among the highest rates of RSLR worldwide due …

Optimal Control And Bifurcation Analysis Of A Predator–Prey Model With Self-Limiting Growth And Predator Disease, Dipo Aldila, Muhammad Akmal Fasya, Bevina D. Handari, Chidozie Williams Chukwu, Olumuyiwa James Peter

Mathematical Modelling and Numerical Simulation with Applications

We propose and analyze an eco-epidemiological predator–prey model that incorporates self-limitation and disease transmission within the predator population. The model is formulated as a system of ordinary differential equations describing logistic prey growth under the interspecific interaction with the predator. On the other hand, the predator population is divided into susceptible and infected classes, whose growth is constrained by prey availability. Three biologically relevant equilibria are identified: predator extinction, disease-free coexistence, and coexistence with endemic disease. The existence and stability of these equilibria are determined by key ecological and epidemiological thresholds, including the basic reproduction number. Using numerical continuation methods, …

Numerical Simulations And Hyers-Ulam Stability Of A Novel Nonlocal Anthropogenic Cutaneous Leishmaniasis Mathematical Model, Khalid Fanoukh Al Oweidi, Zakirullah -, Kamal Shah, Thabet Abdeljawad

Mathematical Modelling and Numerical Simulation with Applications

In this work, the fractal-fractional Atangana-Baleanu derivative with the Mittag-Leffler kernel is employed to capture the memory and hereditary effects inherent to anthropogenic cutaneous leishmaniasis transmission dynamics. The Banach fixed-point theorem and contraction mapping principle are used to prove the existence and uniqueness of solutions, while Hyers-Ulam stability of the system is analyzed to demonstrate the robustness of solutions with respect to small perturbations. Using a nonlinear least-squares approach, model parameters and fractional order are estimated using epidemiological data from the World Health Organization. The basic reproduction number $R_0 = 0.53$ indicates that the disease is under control after adding …

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, …

Differing Circulating Microrna Profiles Of Long-Lived Polish Populations, Fabrizzio Lijeron-Herrera

Honors Undergraduate Theses

Exceptional longevity, defined as a lower biological age relative to chronological age and maintenance of functional capacity or a slowing of functional decline, is a phenotype most readily observed in centenarians and nonagenarians. These individuals exhibit a phenomenon known as compressed morbidity, spending substantially less time in poor health compared to younger elderly populations, thus representing a unique human model of delayed aging. Circulating microRNAs (miRNAs), potent post-transcriptional regulators of molecular pathways, have emerged as important modulators of aging-related pathways; however, no study has comprehensively profiled miRNAs from serum in long-lived individuals from Central Europe. In particular, Polish populations present …