大型生化网络可扩展随机模拟算法的精确化
Exact Parallelization of the Stochastic Simulation Algorithm for Scalable Simulation of Large Biochemical Networks
摘要：
对细胞整个生物化学过程的全面模拟具有巨大的潜力，可以帮助医生治疗疾病，帮助工程师设计生物机器。但这样的模拟必须模拟数百万种分子种类和反应的网络。随机模拟算法（SSA）被广泛用于模拟生物化学过程，特别是那些种群数量很小，离散性和随机性起着重要作用的系统。然而，现有的串行SSA方法对于全面的网络来说是非常慢的，并且现有的并行SSA方法使用周期性同步，牺牲了精度。为了实现快速，准确和可扩展的生物化学模拟，我们提出了一个精确的并行算法SSA的生化网络划分成许多SSA的过程，并行模拟。我们的并行SSA算法正是协调这些SSA过程和物种之间的相互作用，他们共享的状态，通过构造算法作为一个并行离散事件模拟（DES）的应用程序，并使用一个乐观的并行DES模拟器同步的相互作用。我们预计，我们的方法将实现前所未有的生化模拟。
英文摘要：
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.
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