用U-Net神经网络加速细胞自动机模型，590倍提速仍保关键生物行为。
Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture
- 用U-Net构建代理模型，直接预测100步后的细胞状态。
- 仿真速度提升590倍，且能准确复现血管芽生、连接等复杂行为。
- 适合需要快速模拟生物系统的大规模研究者使用。
细胞-庞特斯模型（Cellular-Potts model, CPM）是模拟复杂多细胞生物系统的强大通用框架，但因需显式建模大量个体与偏微分方程（PDE）描述的扩散场相互作用，计算成本高昂。本文提出一种基于U-Net架构的卷积神经网络（CNN）代理模型，可处理周期性边界条件。该模型用于加速一个曾用于研究体外血管生成的机制性CPM。训练后，模型能预测未来100个蒙特卡洛步（MCS），使仿真评估速度比原始CPM代码快590倍。在多次递归预测中，模型有效捕捉了原CPM展现的血管芽生、延伸、吻合及血管腔收缩等涌现行为。该方法展示了深度学习作为高效代理模型的潜力，使计算代价高的生物过程模拟可在更大时空尺度上实现快速评估。
原文摘要 · Abstract (English)
The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 590 times compared to CPM code execution. Over multiple recursive evaluations, our model effectively captures the emergent behaviors demonstrated by the original Cellular-Potts model of such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM of biological processes at greater spatial and temporal scales.
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