Speakers
Description
Biological functions emerge from biomolecular interactions that are inherently stochastic, heterogeneous across cells, and only partially observable. This has motivated the development of stochastic models in mathematical biology that link mechanistic hypotheses to data and enable principled inference and prediction. In the context of gene expression, modern single-cell and live-cell measurement technologies have made this stochastic variability explicit, highlighting gene expression as a noisy and variable process governed by underlying biochemical reaction networks.
This mini-symposium brings together recent work on stochastic modelling and computational approaches for inference with gene expression data, spanning methodological developments \cite{b}, data-driven case studies, and complementary techniques such as stochastic simulation, differential equation–based models, and machine-learning-assisted inference \cite{m}. We encourage contributions addressing inference from stochastic dynamics—including parameter estimation, uncertainty quantification, model selection, and identifiability—and examining how model structure, data resolution, and computational constraints shape what can be learned from experiments \cite{f}. By connecting theory, computation, and data, this symposium aims to explore how current and developing methods can be used to infer interpretable and biologically meaningful gene expression dynamics from experimentally accessible measurements.
Bibliography
@article{b,
title={The Stochastic System Identification Toolkit (SSIT) to model, fit, predict, and design experiments},
url={https://www.biorxiv.org/content/10.64898/2026.02.20.707039v2},
DOI={10.64898/2026.02.20.707039},
publisher={bioRxiv},
author={Popinga, Alex and Forman, Jack and Svetlov, Dmitri and Vo, Huy and Munsky, Brian},
year={2026},
month=mar,
language={en}
}
@article{f,
title = {Trajectory inference from single-cell genomics data with a process time model},
volume = {21},
issn = {1553-7358},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012752},
doi = {10.1371/journal.pcbi.1012752},
language = {en},
number = {1},
urldate = {2026-01-31},
journal = {PLOS Computational Biology},
author = {Fang, Meichen and Gorin, Gennady and Pachter, Lior},
month = jan,
year = {2025},
pages = {e1012752},
}
@misc{m,
title = {{DART}: {Deep} learning for the {Analysis} and {Reconstruction} of {Transcriptional} dynamics from live-cell imaging data},
url = {https://www.biorxiv.org/content/10.1101/2025.09.02.673499v1},
doi = {10.1101/2025.09.02.673499},
language = {en},
urldate = {2026-01-31},
publisher = {bioRxiv},
author = {Ma, Muhan and Grima, Ramon},
month = sep,
year = {2025},
}
