Speaker
Description
Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for unknown or uncertain dynamics. When inferred from appropriate single-cell experiments, such as smFISH or immunocytochemistry (ICC), these models can quantitatively predict complex biological responses in new environments. However, many smFISH/ICC experiments are possible for different induction levels, measurement times, or observables, and each may be time consuming, expensive, or subject to labeling, imaging, or data processing errors. We introduce the Finite State Projection based Fisher Information Matrix (FSP-FIM) as a rigorous guide for the design of single-cell experiments \cite{b}. We extend the FSP-FIM with empirical probabilistic distortion operators to account for unavoidable measurement errors. By analyzing different combinations of models, experiment designs, and data distortions, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive ‘crappy’ imaging conditions. We validate the FSP-FIM approach in HeLa cells using ICC data for glucocorticoid receptor transport and smFISH data for DUSP1 gene regulation upon stimulation with a synthetic corticosteroid.
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},
}
