Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK.
Brief Bioinform. 2010 May;11(3):334-47. doi: 10.1093/bib/bbp073. Epub 2010 Feb 1.
Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with 'bottom-up' simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.
受实验数据可用性和模拟生物规模的能力的推动，细胞规模正在迅速成为中尺度建模的理想候选者。与“自底向上”的模拟方法一样，细胞水平的模拟需要高度的计算能力，而在大规模模拟中，只有通过并行计算才能实现。灵活的大规模代理建模环境 (FLAME) 是一种基于代理的建模 (ABM) 的模板驱动框架，非常适合于细胞系统的模拟。它适用于高性能计算集群 (www.flame.ac.uk) 和 GPU 硬件 (www.flamegpu.com)，并使用一种正式的规范技术作为通用建模格式。这不仅与底层硬件架构形成了抽象，而且避免了与编程相关的陡峭学习曲线。在高级细胞系统的基准测试和模拟中，FLAME GPU 报告了在性能方面相对于更传统的 ABM 框架的巨大改进。这使得建模的开发和测试阶段所花费的时间大大减少，并为简单的视觉确认提供了实时可视化的可能性。
