Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918005650227200 |
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| author | Zhang, Zhenyi Sun, Yuhao Peng, Qiangwei Li, Tiejun Zhou, Peijie |
| author_facet | Zhang, Zhenyi Sun, Yuhao Peng, Qiangwei Li, Tiejun Zhou, Peijie |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_11347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis Zhang, Zhenyi Sun, Yuhao Peng, Qiangwei Li, Tiejun Zhou, Peijie Quantitative Methods Machine Learning Biological Physics 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. |
| title | Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis |
| topic | Quantitative Methods Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2503.11347 |