IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul-Aug;19(4):2111-2123. doi: 10.1109/TCBB.2021.3070123. Epub 2022 Aug 8.
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.
随机模拟算法被广泛用于探索生化途径/网络的随机行为。由于其规模大、结构复杂和刚性，这些算法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较高，它们变得很慢。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时很快，而且在模拟强耦合和刚性网络时也很快。我们使用两个假设网络（即线性链和胶态聚集网络）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-丁二烯氧化网络）比较了它的性能与其他现有算法。结果表明，BlSSSA 比本研究中考虑的其他算法更快。
