Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis

2025-03-14

AI Summary

This study addresses the challenge of modeling dynamic cellular state transitions from static single-cell multi-omics snapshots. Specifically, it tackles the low temporal resolution of scRNA-seq and the lack of spatial context in spatial transcriptomics (ST) data. To this end, we propose the first unified framework integrating stochastic differential equations (SDEs), optimal transport, Schrödinger bridges, and spatiotemporal graph neural networks—enabling continuous, differentiable modeling of cell fate dynamics. Our method reconstructs interpretable and predictive developmental or disease progression trajectories with improved accuracy and temporal consistency. It supports cross-platform dynamic inference across heterogeneous datasets (e.g., scRNA-seq and ST) and enables counterfactual perturbation simulations. Empirically, the framework demonstrates robust performance in embryonic development and neurodegenerative disease progression scenarios. By unifying mechanistic modeling with data-driven learning, it establishes a novel paradigm for deciphering regulatory principles and designing precise therapeutic interventions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Modeling dynamic cellular trajectories using spatiotemporal scRNA-seq data.
• Inferring cell fate decisions through computational frameworks and generative models.
• Understanding biological systems dynamics to decipher cellular behavior and disease progression.

Innovation

• Integrates dynamical systems modeling with scRNA-seq
• Uses Markov chains and stochastic differential equations
• Applies generative models for cellular trajectory reconstruction