用神经随机微分方程模拟细胞动态，实现对生物组织演化的高精度预测。
Learning noisy tissue dynamics across time scales
- 将细胞视为动态图边，结合图神经网络与波网络建模多细胞噪声行为。
- 在果蝇翅膀发育和ERK信号波实验中准确复现真实动态演化过程。
- 适合生物工程、临床模拟等需数字孪生的场景，数据需求显著低于传统模型。
组织动态在炎症到形态发生等多种生物过程中至关重要，但其多细胞噪声特性极难预测。本文提出一种仿生机器学习框架，可直接从实验视频中推断多细胞动态。该生成模型融合图神经网络、归一化流与WaveNet算法，将组织表示为神经随机微分方程，其中细胞作为动态图的边。细胞间相互作用通过双信号图编码，可处理信号级联。该双图架构模仿真实生物组织结构，大幅降低训练所需数据量，相比卷积或全连接网络有明显优势。以上皮组织实验为例，模型不仅捕捉了细胞的随机运动，还能预测其分裂周期中的状态演化。进一步验证表明，该方法能准确生成果蝇翅膀发育及由随机ERK波介导的细胞信号过程的实验动态，为生物工程与临床场景中的数字孪生应用开辟道路。
原文摘要 · Abstract (English)
Tissue dynamics play a crucial role in biological processes ranging from inflammation to morphogenesis. However, these noisy multicellular dynamics are notoriously hard to predict. Here, we introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies. This generative model combines graph neural networks, normalizing flows and WaveNet algorithms to represent tissues as neural stochastic differential equations where cells are edges of an evolving graph. Cell interactions are encoded in a dual signaling graph capable of handling signaling cascades. The dual graph architecture of our neural networks reflects the architecture of the underlying biological tissues, substantially reducing the amount of data needed for training, compared to convolutional or fully-connected neural networks. Taking epithelial tissue experiments as a case study, we show that our model not only captures stochastic cell motion but also predicts the evolution of cell states in their division cycle. Finally, we demonstrate that our method can accurately generate the experimental dynamics of developmental systems, such as the fly wing, and cell signaling processes mediated by stochastic ERK waves, paving the way for its use as a digital twin in bioengineering and clinical contexts.
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