Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
This work addresses the challenge of modeling spatiotemporal dynamics in biology for researchers in computational biology and genomics, but it is incremental as it reviews existing methods rather than introducing new ones.
This review tackles the problem of understanding dynamic cellular behavior by integrating dynamical systems modeling with spatiotemporal single-cell RNA sequencing data, highlighting how computational frameworks like Markov chains and generative models enable the reconstruction of cellular trajectories and fate decisions.
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.