Zhang Jingwei, Watson Layne T, Cao Yang
Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0106, USA.
Int J Comput Biol Drug Des. 2009;2(2):134-48. doi: 10.1504/IJCBDD.2009.028825. Epub 2009 Oct 3.
One important aspect of biological systems such as gene regulatory networks and protein-protein interaction networks is the stochastic nature of interactions between chemical species. Such stochastic behaviour can be accurately modelled by the Chemical Master Equation (CME). However, the CME usually imposes intensive computational requirements when used to characterise molecular biological systems. The major challenge comes from the curse of dimensionality, which has been tackled by a few research papers. The essential goal is to aggregate the system efficiently with limited approximation errors. This paper presents an adaptive way to implement the aggregation process using information collected from Monte Carlo simulations. Numerical results show the effectiveness of the proposed algorithm.
生物系统（如基因调控网络和蛋白质 - 蛋白质相互作用网络）的一个重要方面是化学物质之间相互作用的随机性。这种随机行为可以通过化学主方程（CME）进行精确建模。然而，当用于表征分子生物学系统时，CME通常会带来密集的计算需求。主要挑战来自维度诅咒，已有一些研究论文对此进行了探讨。基本目标是以有限的近似误差有效地聚合系统。本文提出了一种利用从蒙特卡罗模拟收集的信息来实现聚合过程的自适应方法。数值结果表明了所提算法的有效性。
