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Title: MOLNs: A Cloud Platform for Interactive, Reproducible, and Scalable Spatial Stochastic Computational Experiments in Systems Biology Using PyURDME

Journal Article · · SIAM Journal on Scientific Computing
DOI:https://doi.org/10.1137/15M1014784· OSTI ID:1466785
 [1];  [1];  [2];  [1];  [3]
  1. Univ. of California, Santa Barbara, Santa Barbara, CA (United States)
  2. Univ. of Helsinki, Helsinki (Finland); Uppsala Univ., Uppsala (Sweden)
  3. Uppsala Univ., Uppsala (Sweden)
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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. In conclusion, MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

Research Organization:
Univ. of California, Santa Barbara, Santa Barbara, CA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
SC0008975
OSTI ID:
1466785
Journal Information:
SIAM Journal on Scientific Computing, Vol. 38, Issue 3; ISSN 1064-8275
Publisher:
SIAMCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 22 works
Citation information provided by
Web of Science

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Cited By (4)

Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning journal December 2017
A framework for discrete stochastic simulation on 3D moving boundary domains journal November 2016
Simulating biological processes: stochastic physics from whole cells to colonies journal April 2018
Towards reproducible computational drug discovery journal January 2020

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97 MATHEMATICS AND COMPUTING
simulation software
spatial stochastic simulation
systems biology
computational experiments
cloud computing