Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
By: Zhenyi Zhang , Yuhao Sun , Qiangwei Peng and more
Potential Business Impact:
Tracks how cells change over time to understand life.
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 Schr\"odinger 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.
Similar Papers
Mechanistic inference of stochastic gene expression from structured single-cell data
Quantitative Methods
Unlocks secrets of how cells work.
A scalable gene network model of regulatory dynamics in single cells
Molecular Networks
Helps scientists understand how cells change.
Cell2Text: Multimodal LLM for Generating Single-Cell Descriptions from RNA-Seq Data
Machine Learning (CS)
Explains what cells are doing in plain English.