ArticleProceedings of the National Academy of Sciences of the United States of America2025
Learning stochastic processes with intrinsic noise from cross-sectional biological data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Molecular noise modulates transitions in the cell-fate differentiation landscape.NPJ systems biology and applications · 2026Article
- Stochastic colonization and host-to-host transmission shape gut bacterial variability.bioRxiv : the preprint server for biology · 2026Article
- Simulation-based inference captures non-Markovian effects as exemplified in protein production kinetics through cell division.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Identifying stochastic dynamics from non-sequential data (DyNoSeD).Chaos (Woodbury, N.Y.) · 2026Article
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
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3 authors.
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Abstract
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 ordinary differential equation (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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