标题： 基于U-Net神经网络架构的细胞-Potts基于代理模型的代理建模作为分割任务 显示英文标题

标题： Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture

摘要： 细胞-玻尔兹曼模型是一种强大且普遍的框架，用于开发计算模型以模拟复杂的多细胞生物系统。 细胞-玻尔兹曼模型（CPMs）由于对大量个体模型代理和由偏微分方程（PDEs）描述的扩散场进行显式建模，通常计算成本很高。 在本工作中，我们开发了一个使用U-Net架构的卷积神经网络（CNN）代理模型，该模型考虑了周期性边界条件。 我们使用这个模型来加速之前用于研究体外血管生成的机制性CPM的评估。 代理模型被训练为预测100个计算步骤 ahead（蒙特卡洛步骤，MCS），与CPM代码执行相比，将模拟评估加速了590倍。 在多次递归评估中，我们的模型有效地捕捉了原始细胞-玻尔兹曼模型所表现出的涌现行为，如血管芽生、延伸和吻合，以及血管腔的收缩。 这种方法展示了深度学习作为CPM模拟高效代理模型的潜力，使得在更大空间和时间尺度上更快地评估计算成本高的CPM生物过程成为可能。 显示英文摘要

摘要： 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.