用动态系统模型解析单细胞时空基因表达数据
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
- 结合马尔可夫链与随机微分方程建模细胞轨迹
- 实现从静态数据推断细胞命运决策的动态过程
- 适合研究发育或疾病进展的生物学家参考
理解生物系统的动态特性是揭示细胞行为、发育过程和疾病进展的关键。单细胞RNA测序（scRNA-seq）提供了基因表达的静态快照，有助于分析单一时间点的细胞状态。近期，时间分辨scRNA-seq、空间转录组学（ST）以及时间序列空间转录组学（temporal-ST）的发展，进一步推动了对单个细胞时空动态的研究。这些技术结合马尔可夫链、随机微分方程（SDEs）及生成模型（如最优传输和薛定谔桥），可重建细胞动态轨迹与命运决定。本文综述了这些动态系统方法如何从系统视角提供建模与推断细胞动态的新机遇。
原文摘要 · Abstract (English)
Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved scRNA-seq, spatial transcriptomics (ST), and time-series spatial transcriptomics (temporal-ST) have further revolutionized our ability to study the spatiotemporal dynamics of individual cells. These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. This review discusses how these dynamical system approaches offer new opportunities to model and infer cellular dynamics from a systematic perspective.
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。