Department of Computer Science, Aalto University, Espoo, Finland.
The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Bull Math Biol. 2019 Aug;81(8):3053-3073. doi: 10.1007/s11538-018-0462-y. Epub 2018 Jul 6.
The rejection-based simulation technique has been applying to improve the computational efficiency of the stochastic simulation algorithm (SSA) in simulating large reaction networks, which are required for a thorough understanding of biological systems. We compare two recently proposed simulation methods, namely the composition-rejection algorithm (SSA-CR) and the rejection-based SSA (RSSA), aiming for this purpose. We discuss the right interpretation of the rejection-based technique used in these algorithms in order to make an informed choice when dealing with different aspects of biochemical networks. We provide the theoretical analysis as well as the detailed runtime comparison of these algorithms on concrete biological models. We highlight important factors that are omitted in previous analysis of these algorithms. The numerical comparison shows that for reaction networks where the search cost is expensive then SSA-CR is more efficient, and for reaction networks where the update cost is dominant, often the case in practice, then RSSA should be the choice.
基于拒绝的模拟技术已被应用于提高随机模拟算法 (SSA) 在模拟大型反应网络方面的计算效率，这对于深入理解生物系统是必要的。我们比较了两种最近提出的模拟方法，即组合拒绝算法 (SSA-CR) 和基于拒绝的 SSA (RSSA)，旨在实现这一目标。我们讨论了这些算法中使用的基于拒绝的技术的正确解释，以便在处理生化网络的不同方面时做出明智的选择。我们提供了这些算法的理论分析以及在具体生物模型上的详细运行时比较。我们强调了在这些算法的先前分析中被忽略的重要因素。数值比较表明，对于搜索成本昂贵的反应网络，SSA-CR 更有效，而对于更新成本占主导地位的反应网络，通常在实际情况下，RSSA 应该是更好的选择。
