Stochastic simulation algorithms are extensively used for exploring stochastic behavior of biochemical pathways/networks. Computational cost of these algorithms is high in simulating real biochemical systems due to their large size, complex structure and stiffness. In order to reduce the computational cost, several algorithms have been developed. It is observed that these algorithms are basically fast in simulating weakly coupled networks. In case of strongly coupled networks, they become slow as their computational cost become high in maintaining complex data structures. Here, we develop Block Search Stochastic Simulation Algorithm (BlSSSA). BlSSSA is not only fast in simulating weakly coupled networks but also fast in simulating strongly coupled and stiff networks. We compare its performance with other existing algorithms using two hypothetical networks, viz., linear chain and colloidal aggregation network, and three real biochemical networks, viz., B cell receptor signaling network, FceRI signaling network and a stiff 1,3-Butadiene Oxidation network. It has been shown that BlSSSA is faster than other algorithms considered in this study.

中文翻译：

---

块搜索随机模拟算法 (BlSSSA)：一种用于大型生化网络建模的快速随机模拟算法

随机模拟算法广泛用于探索生化途径/网络的随机行为。由于这些算法的尺寸大、结构复杂且刚度大，因此在模拟真实生化系统时计算成本很高。为了降低计算成本，已经开发了几种算法。可以观察到，这些算法在模拟弱耦合网络时基本上很快。在强耦合网络的情况下，由于维护复杂数据结构的计算成本变高，它们会变得很慢。在这里，我们开发了块搜索随机模拟算法 ( BlSSSA )。BLSSSA不仅在模拟弱耦合网络方面速度很快，而且在模拟强耦合和刚性网络方面也很快。我们使用两个假设网络（即线性链和胶体聚集网络）和三个真实生化网络（即 B 细胞受体信号网络、FceRI 信号网络和刚性 1,3-丁二烯氧化）将其性能与其他现有算法进行比较网络。已经表明，BlSSSA比本研究中考虑的其他算法更快。