Epstein-Barr virus (EBV) infection drives a coordinated cascade of B-cell state transitions underlying both primary infection and EBV-associated malignancies \cite{sorelle_time-resolved_2022}. scRNA-seq provides high-dimensional snapshots of these transitions across thousands of individual cells, yet current clustering-based approaches capture only a fraction of the quantitative information...
Limitations of human data in cancer research poses significant challenges for accurately predicting tumor growth and treatment outcomes. To address this, virtual patient cohorts are created to facilitate in silico exploration of new treatments. We propose a novel framework that integrates mathematical modeling, statistical data augmentation, noise reduction, and neural network-based trajectory...
To date, the only instances of HIV โcureโ were the result of transplantation with stem cells containing mutated genes for the CCR5 coreceptor, a cell-surface protein required by most HIV strains to enter and infect a cell. Thus, researchers are pursuing different gene therapy methods to reduce CCR5 expression without the need for a stem cell transplant, but it is unclear what fraction of...
Once diagnosed, cancer requires a fast, inexpensive and reliable assessment of the current state and potential progression of the disease. A new method for estimating tumor cell diffusivity $D$ and proliferation rate $\gamma$ from single-point-in-time routine biopsies aims to deliver just that, and the ratio of its estimates $D/\gamma$ is a promising candidate for a new biomarker for...
Mathematical modeling in oncology frequently encounters a trade-off between the structural interpretability of mechanistic equations and the flexible predictive power of data-driven algorithms. While mechanistic models provide essential biological constraints, they are often subject to structural misspecification when faced with the high dimensionality of clinical heterogeneity. Conversely,...
B cell selection and evolution are key processes in regulating successful adaptive immune responses. Recent advances in single-cell sequencing and deep learning strategies have unlocked new potential to study affinity maturation of B cells at unprecedented scale and resolution. To unravel the complex dynamics of B cell repertoire evolution during immune responses and to facilitate Protein...
There is a 30-year history and leadership in verification, validation, and uncertainty quantification (VVUQ) established by the U.S. Department of Energy National Nuclear Security Administration (NNSA) Labs to evaluate the credibility of computational models used in high consequence applications. Today, the U.S. National Institute of Standards and Technology has established the building blocks...
Respiratory viruses, such as influenza and SARS-CoV-2, pose significant global health threats. Mathematical models have been instrumental in understanding epidemiological spread and within-host viral dynamics. These are typically compartmental models that neglect spatial structure. In contrast, agent-based models (ABMs) enable the representation of localized, single-cell interactions and...
There is significant interest in using machine learning to develop โAI virtual cellโ models that can serve as computational proxies for wet lab experiments. The central requirements for such models are inherently causal: they must accurately predict the effects of interventions such as gene knockouts or drug perturbations on cellular state, and they must support the generation of testable...
Human behavioral responses play a central role in shaping epidemic dynamics, yet they remain difficult to model mechanistically due to their heterogeneity and context dependence. We develop a hybrid, networked SEIR framework that integrates generative AI-driven agents to capture individualized protective behavior. Each agent is characterized by demographic and socioeconomic attributes, and a...
Influenza infections exhibit substantial heterogeneity in viral shedding and
immune responses, along with varying widely in severity and outcome. While viral load
dynamics and certain immune responses are often assumed to correlate with disease
severity, experimental observations suggest that these relationships are not always
straightforward and can change across the duration of...
Mitochondrial inheritance during cell division is a fundamental biological process that ensures daughter cell viability, yet its governing mechanisms remain incompletely understood. In budding yeast (Saccharomyces cerevisiae), experimental observations reveal substantial variability in how mitochondrial content is partitioned between mother and daughter cells, raising key questions about the...
The intersection of mathematical methods, machine learning, and scientific machine learning is paving the way for innovative solutions to complex biological challenges. This mini-symposium aims to showcase cutting-edge research that leverages these interdisciplinary approaches to enhance our understanding of biological systems and improve health outcomes. The session will explore...
