Agent-based modelling is gaining recognition as a powerful approach for simulating complex cellular pathways, owing to its ability to reproduce emergent biological behaviours without requiring extensive kinetic parameterisation. In this article, we present a GPU-accelerated agent-based simulator specifically designed to model and analyse signalling pathways involved in cancer progression, and to evaluate therapeutic interventions. Our approach leverages the computing capabilities of FLAME GPU 2, a GPU-accelerated agent-based modelling framework, to efficiently manage simulations involving millions of molecules interacting within a three-dimensional environment. Each molecule is represented as an autonomous agent with defined physical properties, capable of binding, releasing reaction products, migrating between compartments, and interacting based on spatial proximity. An intuitive graphical interface supports model construction, parameter setup, and real-time modification of treatment strategies. As the primary focus of this paper, we validate the simulator on the MAPK/ERK cascade affected by the BRAFV600E mutation, demonstrating that it accurately reproduces dose-response trends observed in clinical data and outperforms both deterministic models and our prior agent-based implementations. A second case study extends the approach to nuclear signalling by reproducing the dynamics of cFos expression and phosphorylation. This demonstrates the simulator's ability to capture compartmentalised regulation, reproducing transient mRNA responses and protein accumulation, including the effect of an unresolved negative transcriptional regulator. Together, these results show that GPU-accelerated ABM can faithfully replicate both drug response and emergent gene expression dynamics, providing a scalable and biologically grounded computational tool for supporting precision oncology.

翻译：基于智能体的建模方法因其能够在不依赖大量动力学参数化的前提下再现涌现性生物行为，正逐渐被公认为模拟复杂细胞信号通路的强大工具。本文提出了一种专为建模和分析癌症进展相关信号通路、评估治疗干预措施而设计的GPU加速智能体模拟器。我们的方法利用FLAME GPU 2（一种GPU加速的智能体建模框架）的计算能力，高效管理涉及数百万个分子在三维环境中相互作用的模拟。每个分子被建模为具有明确物理属性的自主智能体，能够结合、释放反应产物、在区室间迁移，并基于空间邻近性进行交互。直观的图形界面支持模型构建、参数设置以及治疗策略的实时修改。作为本文的核心焦点，我们在受BRAFV600E突变影响的MAPK/ERK级联反应上验证了该模拟器，结果表明它能准确再现临床数据中观察到的剂量-反应趋势，并优于确定性模型及我们之前的智能体实现。第二个案例研究通过再现cFos表达和磷酸化的动力学过程，将该方法扩展至核信号转导领域。这展示了模拟器捕捉区室化调控的能力，能再现瞬时mRNA反应和蛋白质积累，包括未解决的负转录调控因子的影响。综上所述，这些结果表明GPU加速的智能体建模方法能够忠实复现药物响应和涌现性基因表达动力学，为支持精准肿瘤学提供了一种可扩展且具有生物学基础的计算工具。