高效实现生物化学系统随机模拟的混合方法
Efficient implementation of the hybrid method for stochastic simulation of biochemical systems
AI总结：
本文提出高效实现混合方法的方案，结合传统ODE求解器，提升生物化学系统随机模拟效率，并比较三种ODE求解器的性能。
摘要：
细胞系统中的随机效应是系统生物学中的重要课题。随机建模和仿真方法是研究随机效应的重要工具。由于随机仿真算法效率低下，混合方法通过将常微分方程（ODE）系统与随机化学反应系统结合，在生物化学系统建模和仿真中展现出独特优势。混合方法的效率通常受限于随机子系统中的反应，这些反应使用Gillespie框架建模和仿真，经常中断ODE子系统的积分。本文开发了一种高效实现混合方法的方案，结合传统ODE求解器。我们还比较了混合方法与三种广泛使用的ODE求解器RADAU5、DASSL和DLSODAR的效率。三个生物化学模型的数值实验被呈现。对三种ODE求解器性能进行了详细讨论。
英文摘要：
Stochastic effect in cellular systems has been an important topic in systems biology. Stochastic modeling and simulation methods are important tools to study stochastic effect. Given the low efficiency of stochastic simulation algorithms, the hybrid method, which combines an ordinary differential equation (ODE) system with a stochastic chemically reacting system, shows its unique advantages in the modeling and simulation of biochemical systems. The efficiency of hybrid method is usually limited by reactions in the stochastic subsystem, which are modeled and simulated using Gillespie's framework and frequently interrupt the integration of the ODE subsystem. In this paper we develop an efficient implementation approach for the hybrid method coupled with traditional ODE solvers. We also compare the efficiency of hybrid methods with three widely used ODE solvers RADAU5, DASSL, and DLSODAR. Numerical experiments with three biochemical models are presented. A detailed discussion is presented for the performances of three ODE solvers.
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