从横断面数据中分离生物过程的内在噪声，精准推断动态机制。
Inferring biological processes with intrinsic noise from cross-sectional data
- 通过概率流建模，分离系统力与内在随机性。
- 在高维生化网络中实现参数与驱动力的精确估计。
- 适用于细胞分化等含分子噪声的动态推断，优于现有方法。
从数据推断动态模型仍是计算生物学的重大挑战，尤其在许多生物过程具有随机性的背景下。我们研究了组学中常见场景：仅在少数时间点获得统计独立的横断面样本，目标是推断生成这些数据的底层扩散过程。现有方法常简化或忽略系统内在噪声，以换取优化便利性，牺牲准确性。我们提出概率流推断（PFI），通过推断与底层随机过程具有相同时间依赖边际分布的相空间概率流，避免这一权衡。理论上，我们证明对于奥恩斯坦-乌伦贝克过程，在分布充分采样极限下，正则化PFI形式具有唯一解。实际应用中，PFI可准确估计高维随机反应网络的参数与作用力，并在含分子噪声的细胞分化动态推断中表现优于当前最优方法。
原文摘要 · Abstract (English)
Inferring dynamical models from data continues to be a significant challenge in computational biology, especially given the stochastic nature of many biological processes. We explore a common scenario in omics, where statistically independent cross-sectional samples are available at a few time points, and the goal is to infer the underlying diffusion process that generated the data. Existing inference approaches often simplify or ignore noise intrinsic to the system, compromising accuracy for the sake of optimization ease. We circumvent this compromise by inferring the phase-space probability flow that shares the same time-dependent marginal distributions as the underlying stochastic process. Our approach, probability flow inference (PFI), disentangles force from intrinsic stochasticity while retaining the algorithmic ease of ODE inference. Analytically, we prove that for Ornstein-Uhlenbeck processes the regularized PFI formalism yields a unique solution in the limit of well-sampled distributions. In practical applications, we show that PFI enables accurate parameter and force estimation in high-dimensional stochastic reaction networks, and that it allows inference of cell differentiation dynamics with molecular noise, outperforming state-of-the-art approaches.
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