| Literature DB >> 28190948 |
Brian Drawert1, Michael Trogdon2, Salman Toor3, Linda Petzold4, Andreas Hellander1.
Abstract
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.Entities:
Keywords: cloud computing; computational experiments; simulation software; spatial stochastic simulation; systems biology
Year: 2016 PMID: 28190948 PMCID: PMC5302863 DOI: 10.1137/15M1014784
Source DB: PubMed Journal: SIAM J Sci Comput ISSN: 1064-8275 Impact factor: 2.373