用概率流匹配学习基因调控的生物物理模型，让单细胞数据揭示细胞分化机制。
Learning biophysical models of gene regulation with probability flow matching
- 基于概率流匹配框架，从时序单细胞数据直接学习生物物理一致的随机过程。
- 在三个造血数据集上验证，仅生物物理一致的模型能准确捕捉谱系转变与基因扰动响应。
- 可处理不平衡细胞群体，同时推断增殖与死亡动态，适合研究发育与疾病机制。
细胞分化由基因调控网络控制，这类高维随机生化系统决定转录景观并介导细胞对信号和扰动的响应。尽管单细胞RNA测序提供了转录组的定量快照，现有推断基因调控动态的方法常缺乏机制可解释性，且难以推广到未见条件。本文提出概率流匹配（PFM），一种可扩展的框架，可直接从时序单细胞测量中学习生物物理一致的随机过程。将PFM应用于三个造血数据集，结果表明：具有相似插值精度的模型可能编码截然不同的动力学，唯有生物物理一致的模型能准确捕捉谱系转变、命运决定及基因扰动响应机制。此外，PFM可处理不平衡细胞群体，实现对细胞增殖与死亡动态的联合推断。这些结果确立了PFM作为整合机制建模与单细胞组学的灵活、可扩展框架。
原文摘要 · Abstract (English)
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 models with similar interpolation accuracy can encode fundamentally different dynamics, with only biophysically consistent formulations accurately capturing mechanisms of lineage transitions, fate specification, and gene perturbation responses. We further demonstrate that PFM accommodates unbalanced populations, enabling simultaneous inference of cellular proliferation and death dynamics. Together, these results establish PFM as a flexible, scalable framework for integrating mechanistic modeling with single-cell omics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。