细胞谱系随机演化的并行算法研究

项目名称： 细胞谱系随机演化的并行算法研究

项目编号： No.11301294

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 数理科学和化学

项目作者： 胡煜成

作者单位： 清华大学

项目金额： 22万元

中文摘要： 随机建模和数值模拟在系统生物学研究中已成为不可或缺的重要工具。随着系统空间和时间尺度的增加，传统的串行计算已无法满足巨大的计算量的需求。在实际问题和高性能计算技术的推动下，用于模拟大规模随机系统演化的并行算法得到了大力发展。动力学蒙特卡洛（KMC）算法是一个模拟连续时间随机过程的重要算法，在物理、化学、材料和信息科学等领域有着广泛的应用。如何实现KMC算法的并行化是当前的研究热点。本项目的主旨是设计一个可靠高效的并行KMC算法，并用它来模拟空间格点模型中大量细胞复制与分化形成的细胞谱系的随机演化，着重研究负反馈调控下细胞群体的时空动力学行为及其生物意义。本项目强调运用大规模科学计算来解决系统生物学中的前沿问题，体现了交叉学科和合作创新的特点，是研究者本人在计算系统生物学方向的重要起步。

中文关键词： 蒙特卡洛算法；并行计算；细胞谱系；群体动力学；液晶

英文摘要： In the past few decades, there has been a dramatic increase in the use of stochastic modeling and simulation in system biology. The standard kinetic Monte Carlo (KMC) algorithm is an extremely efficient method to carry out serial simulations of stochastic dynamical processes. As the system size and simulation time extends, it is desirable to develop efficient parallel KMC algorithms in order to take advantage of existing and upcoming super computing capabilities. The main goal of this project is to implement an efficient semi-rigorous sub-lattice algorithm for parallel KMC simulations. This algorithm is particularly suited for shared memory parallel computing and can be easily carried out using OpenMP. The key problem we face is to optimize the strategy for selecting the time step-size, which plays an important role in determine the accuracy and efficiency of the algorithm. The practical motivation of developing this parallel algorithm is to study the stochastic evolution of a large scale cellular automata lattice model, which describes the dynamical behavior of a cell lineage system under negative control. In system biology, cell lineage is considered as the fundamental units of tissue and organ development, maintenance and regeneration. In particular we want to understand the spatial effect of negative feedbac

英文关键词： Monte Carlo method；parallel computing；cell lineage；population dynamics；liquid crystals