Learning biophysical models of gene regulation with probability flow matching
Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcriptional landscape and mediate cellular responses to signals and perturbations. Although single-cell RNA sequencing provides quantitative snapshots of the transcriptome, current methods for inferring gene-regulatory dynamics often lack mechanistic interpretability and fail to generalize to unseen conditions. Here we introduce Probability Flow Matching (PFM), a scalable framework for learning biophysically consistent stochastic processes directly from time-resolved single-cell measurements. Applying PFM to three hematopoiesis datasets, we show that...
| Authors | Suryanarayana Maddu, Victor Chardès, Michael J. Shelley |
|---|---|
| Primary category | q-bio.MN |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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