Chatterjee Abhijit, Vlachos Dionisios G, Katsoulakis Markos A
Department of Chemical Engineering and Center for Catalytic Science and Technology, University of Delaware, Newark, Delaware 19716, USA.
J Chem Phys. 2005 Jan 8;122(2):024112. doi: 10.1063/1.1833357.
Recently, Gillespie introduced the tau-leap approximate, accelerated stochastic Monte Carlo method for well-mixed reacting systems [J. Chem. Phys. 115, 1716 (2001)]. In each time increment of that method, one executes a number of reaction events, selected randomly from a Poisson distribution, to enable simulation of long times. Here we introduce a binomial distribution tau-leap algorithm (abbreviated as BD-tau method). This method combines the bounded nature of the binomial distribution variable with the limiting reactant and constrained firing concepts to avoid negative populations encountered in the original tau-leap method of Gillespie for large time increments, and thus conserve mass. Simulations using prototype reaction networks show that the BD-tau method is more accurate than the original method for comparable coarse-graining in time.
最近，吉莱斯皮提出了用于充分混合反应体系的τ跳跃近似加速随机蒙特卡罗方法[《化学物理杂志》115, 1716 (2001)]。在该方法的每次时间增量中，从泊松分布中随机选择若干反应事件来执行，以实现长时间模拟。在此，我们引入一种二项分布τ跳跃算法（简称为BD - τ方法）。该方法将二项分布变量的有界性与限制反应物和约束激发概念相结合，以避免在吉莱斯皮原始的τ跳跃方法中，当时间增量较大时出现的负种群情况，从而守恒质量。使用原型反应网络进行的模拟表明，对于时间上可比的粗粒化，BD - τ方法比原始方法更精确。
