European Conference on Mathematical and Theoretical Biology (ECMTB 2026)

Biophysical generative modeling of cell fate decision-making with single-cell omics data

MS77-06

17 Jul 2026, 11:20

20m

11.02 - HS (University of Graz)

Minisymposium Talk Systems Biology and Biochemical Networks Mechanistic Model Inference for Stochastic Single-Cell Dynamics

Speaker

Victor Chardes (Harvard University)

Description

Inferring biophysical models of gene regulation from single-cell omics data remains a significant challenge because of the high dimensionality, stochasticity, and cross-sectional nature of the measurements. We aim to bridge this gap by combining flow-based generative modeling with the biophysics of transcriptional regulation to infer interpretable models of cell fate decision-making from single-cell omics data. Existing inference approaches often simplify or ignore the biophysics of gene regulation, compromising accuracy and interpretability for the sake of optimization ease. To address this issue, we recently developed Probability Flow Inference (PFI), a computational approach to infer the phase-space probability flow associated with arbitrary stochastic differential equations. This approach enables inference of detailed models of transcriptional regulation that naturally disentangle molecular noise and cellular proliferation from gene regulation, improving gene regulatory network inference and outperforming purely data-driven approaches in cell fate prediction. Overall, our results demonstrate that flow-based generative modeling offers an opportunity to test, at genome-wide scale, biophysical hypotheses about cell fate decision-making.

Bibliography

@article{Mmaddu2025learning,
title = {Learning Stochastic Processes with Intrinsic Noise from Cross-Sectional Biological Data},
author = {Maddu$^{\dagger,*}$, Suryanarayana and \textbf{V Chard{`e}s$^{\dagger,*}$} and Shelley, Michael J.},
year = {2025},
month = sep,
journal = {Proceedings of the National Academy of Sciences},
volume = {122},
number = {37},
pages = {e2420621122},
publisher = {Proceedings of the National Academy of Sciences},
doi = {10.1073/pnas.2420621122},
urldate = {2025-12-25},
}

@inproceedings{Mzhang2025inferring,
title={Inferring stochastic dynamics with growth from cross-sectional data},
author={Stephen Y. Zhang$^*$ and Suryanarayana Maddu and Xiaojie Qiu and \textbf{V Chard{`e}s$^*$}},
booktitle={Advances in Neural Information Processing Systems 39},
year={2025},
url={https://openreview.net/forum?id=MtdC1XS6RN}
}