Department of Computer Science, University of Sheffield, Sheffield, UK.
Department of Mathematics and Computer Science, University of Catania, Catania, Italy.
BMC Bioinformatics. 2019 Dec 10;20(Suppl 6):579. doi: 10.1186/s12859-019-3181-y.
In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches.
This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment.
Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware.
近年来，使用自底向上的方法（基于代理的建模，ABM）研究免疫反应行为引起了相当大的关注。由于需要大规模的分析工具来收集和解释信息以解决生物学问题，因此 ABM 方法在生物学领域是一种非常常见的技术。模拟大规模多代理系统（即包含大量代理/实体的模拟）需要大量的计算工作，只有通过使用并行计算方法才能实现。
本文探讨了在 ABM 模型中对生物和免疫系统模型的关键组件进行并行化的不同方法：成对交互。本文的重点是在连续和离散空间中代理/实体相互竞争以在并行环境中相互交互的情况下，细胞相互作用的性能和算法设计选择。
我们的性能结果证明了这些方法在表现出典型细胞间相互作用的更广泛的生物学系统中的适用性。讨论了每种实现方式的优缺点，表明每种方法都可以用作在并行硬件上开发完整免疫系统模型的基础。
