Drawert Brian, Trogdon Michael, Toor Salman, Petzold Linda, Hellander Andreas
Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA 93106.
Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, CA 93106.
SIAM J Sci Comput. 2016;38(3):C179-C202. doi: 10.1137/15M1014784. Epub 2016 Jun 1.
Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.
使用空间随机模拟的计算实验带来了重要的新生物学见解,但由于与蒙特卡罗计算工作流程相关的巨大计算成本,它们需要专门的工具和复杂的软件栈,以及大规模且可扩展的计算和数据分析资源。对于系统生物学的从业者来说,设置和管理一个大规模分布式计算环境以支持高效且可重复的建模的复杂性可能令人望而却步。这导致了空间随机模拟工具采用的障碍,有效地限制了定量建模所解决的生物学问题的类型。在本文中,我们展示了PyURDME,一个全新的、用户友好的空间建模和模拟包,以及MOLNs,一种用于随机反应扩散模型分布式模拟的云计算设备。MOLNs基于IPython,并为开发可共享且可重复的分布式并行计算实验提供了一个交互式编程平台。