Speaker
Description
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 available. We present an equation learning pipeline that extracts the underlying dynamical structure directly from EBV-driven B-cell scRNA-seq data. First, the raw data is processed via UMAP-based dimensionality reduction and clustered to identify discrete cell states, then mapped onto trajectories using pseudotime. These trajectories serve as input to a hybrid scientific machine learning method in which a structured candidate library encodes known biological relationships alongside neural network terms that capture unknown dynamics. SINDy is applied to recover a parsimonious ODE system whose structure reflects both the data and the underlying biology of EBV-induced B-cell differentiation \cite{wu_data-driven_2025}.The learned system can identify known and unknown cell state transitions and generate mechanistic hypotheses. This method bridges single-cell transcriptomics and scientific machine learning, offering a principled framework for extracting mechanistic structure from high-dimensional omics data and advancing our understanding of EBV-driven B-cell fate decisions.
Bibliography
@article{wu_data-driven_2025,
title = {Data-driven model discovery and model selection for noisy biological systems},
volume = {21},
issn = {1553-7358},
doi = {10.1371/journal.pcbi.1012762},
language = {en},
number = {1},
urldate = {2026-03-28},
journal = {PLOS Computational Biology},
author = {Wu, Xiaojun and McDermott, MeiLu and MacLean, Adam L},
editor = {Alber, Mark},
month = jan,
year = {2025},
pages = {e1012762},
}
@article{sorelle_time-resolved_2022,
title = {Time-resolved transcriptomes reveal diverse {B} cell fate trajectories in the early response to {Epstein}-{Barr} virus infection},
volume = {40},
issn = {22111247},
doi = {10.1016/j.celrep.2022.111286},
language = {en},
number = {9},
urldate = {2026-03-28},
journal = {Cell Reports},
author = {SoRelle, Elliott D. and Dai, Joanne and Reinoso-Vizcaino, Nicolás M. and Barry, Ashley P. and Chan, Cliburn and Luftig, Micah A.},
month = aug,
year = {2022},
pages = {111286},
}
