论文标题
大基因调节网络的混合随机模拟算法的精确推导和实际应用
Exact derivation and practical application of a hybrid stochastic simulation algorithm for large gene regulatory networks
论文作者
论文摘要
我们提出了一种高效，准确的混合随机模拟算法（HSSA），目的是模拟大基因调节网络（GRN）的生化反应子集。该算法依赖于GRN分解为两组A和B组的可分离性，以便可以通过随机模拟算法（SSA）模拟A中的反应，而B组中的反应可以通过普通微分方程来确定性描述。首先，我们得出采样下一个反应时间和反应类型所需的精确表达式，然后给出两个示例，说明如何分区GRN。尽管此处介绍的方法可以应用于GRN中的各种不同随机系统，但我们特别致力于模拟mRNA。为了证明该算法的准确性和效率，我们将其应用于三基因振荡器，首先在一个细胞中，然后在一个通过分子扩散相互作用的细胞（最多64个细胞）中，并将其性能与吉尔斯皮算法（GA）进行比较。根据系统参数的特定数值和分区本身的不同，我们表明我们的算法比GA快11至445倍。
We present a highly efficient and accurate hybrid stochastic simulation algorithm (HSSA) for the purpose of simulating a subset of biochemical reactions of large gene regulatory networks (GRN). The algorithm relies on the separability of a GRN into two groups of reactions, A and B, such that the reactions in A can be simulated via a stochastic simulation algorithm (SSA), while those in group B can yield to a deterministic description via ordinary differential equations. First, we derive exact expressions needed to sample the next reaction time and reaction type, and then give two examples of how a GRN can be partitioned. Although the methods presented here can be applied to a variety of different stochastic systems within GRN, we focus on simulating mRNAs in particular. To demonstrate the accuracy and efficiency of this algorithm, we apply it to a three-gene oscillator, first in one cell, and then in an array of cells (up to 64 cells) interacting via molecular diffusion, and compare its performance to the Gillespie algorithm (GA). Depending on the particular numerical values of the system parameters, and the partitioning itself, we show that our algorithm is between 11 and 445 times faster than the GA.
