论文标题
用于大型生化网络的随机模拟算法的确切并行化
Exact Parallelization of the Stochastic Simulation Algorithm for Scalable Simulation of Large Biochemical Networks
论文作者
论文摘要
对整个细胞生物化学的全面模拟具有巨大的潜力,可以帮助医生治疗疾病并帮助工程师设计生物机器。但是,这样的模拟必须建模数百万个分子物种和反应的网络。 随机模拟算法(SSA)广泛用于模拟生物化学,尤其是物种种群的系统,足够小,离散性和随机性起着重要作用。但是,对于全面的网络而言,现有的串行SSA方法非常缓慢,现有的并行SSA方法,这些方法使用定期同步,牺牲准确性。 为了启用生物化学的快速,准确和可扩展的模拟,我们提出了SSA的精确并行算法,该算法将生物化学网络划分为许多在并行模拟的SSA过程中。我们的平行SSA算法准确地协调了这些SSA过程之间的相互作用以及它们通过构造算法作为平行离散事件仿真(DES)应用以及使用乐观的平行模拟器来同步相互作用来共享的物种状态。我们预计我们的方法将实现前所未有的生化模拟。
Comprehensive simulations of the entire biochemistry of cells have great potential to help physicians treat disease and help engineers design biological machines. But such simulations must model networks of millions of molecular species and reactions. The Stochastic Simulation Algorithm (SSA) is widely used for simulating biochemistry, especially systems with species populations small enough that discreteness and stochasticity play important roles. However, existing serial SSA methods are prohibitively slow for comprehensive networks, and existing parallel SSA methods, which use periodic synchronization, sacrifice accuracy. To enable fast, accurate, and scalable simulations of biochemistry, we present an exact parallel algorithm for SSA that partitions a biochemical network into many SSA processes that simulate in parallel. Our parallel SSA algorithm exactly coordinates the interactions among these SSA processes and the species state they share by structuring the algorithm as a parallel discrete event simulation (DES) application and using an optimistic parallel DES simulator to synchronize the interactions. We anticipate that our method will enable unprecedented biochemical simulations.