Stem cells decide what to become by following strict physical laws that can now be reverse engineered using computer models.
The process of a cell transitioning from one state to another was long thought to be a chaotic or purely biological mystery. Probability Flow Matching shows that these transitions follow biophysically consistent stochastic processes. Only the models that adhere to the fundamental laws of physics are able to accurately predict how a cell will change its fate. This means that gene regulation is not a series of random events, but a structured journey governed by thermodynamic and physical constraints. Cracking this code allows scientists to program cells with a level of precision that was previously impossible.
Learning biophysical models of gene regulation with probability flow matching
arXiv · 2604.25062
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 (P