Hellander Andreas
Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala, Sweden.
J Chem Phys. 2008 Apr 21;128(15):154109. doi: 10.1063/1.2897976.
A quasi-Monte Carlo method for the simulation of discrete time Markov chains is applied to the simulation of biochemical reaction networks. The continuous process is formulated as a discrete chain subordinate to a Poisson process using the method of uniformization. It is shown that a substantial reduction of the number of trajectories that is required for an accurate estimation of the probability density functions (PDFs) can be achieved with this technique. The method is applied to the simulation of two model problems. Although the technique employed here does not address the typical stiffness of biochemical reaction networks, it is useful when computing the PDF by replication. The method can also be used in conjuncture with hybrid methods that reduce the stiffness.
一种用于离散时间马尔可夫链模拟的拟蒙特卡罗方法被应用于生化反应网络的模拟。利用均匀化方法，将连续过程表述为从属于泊松过程的离散链。结果表明，使用该技术可以显著减少准确估计概率密度函数（PDF）所需的轨迹数量。该方法被应用于两个模型问题的模拟。尽管这里采用的技术没有解决生化反应网络典型的刚性问题，但在通过复制计算PDF时很有用。该方法也可以与降低刚性的混合方法结合使用。
