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

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.
Authors:
 [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)
Publication Date:
Grant/Contract Number:
SC0008975
Type:
Accepted Manuscript
Journal Name:
SIAM Journal on Scientific Computing
Additional Journal Information:
Journal Volume: 38; Journal Issue: 3; Journal ID: ISSN 1064-8275
Publisher:
SIAM
Research Org:
Univ. of California, Santa Barbara, Santa Barbara, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; simulation software; spatial stochastic simulation; systems biology; computational experiments; cloud computing
OSTI Identifier:
1466785

Drawert, Brian, Trogdon, Michael, Toor, Salman, Petzold, Linda, and Hellander, Andreas. MOLNs: A Cloud Platform for Interactive, Reproducible, and Scalable Spatial Stochastic Computational Experiments in Systems Biology Using PyURDME. United States: N. p., Web. doi:10.1137/15M1014784.
Drawert, Brian, Trogdon, Michael, Toor, Salman, Petzold, Linda, & Hellander, Andreas. MOLNs: A Cloud Platform for Interactive, Reproducible, and Scalable Spatial Stochastic Computational Experiments in Systems Biology Using PyURDME. United States. doi:10.1137/15M1014784.
Drawert, Brian, Trogdon, Michael, Toor, Salman, Petzold, Linda, and Hellander, Andreas. 2016. "MOLNs: A Cloud Platform for Interactive, Reproducible, and Scalable Spatial Stochastic Computational Experiments in Systems Biology Using PyURDME". United States. doi:10.1137/15M1014784. https://www.osti.gov/servlets/purl/1466785.
@article{osti_1466785,
title = {MOLNs: A Cloud Platform for Interactive, Reproducible, and Scalable Spatial Stochastic Computational Experiments in Systems Biology Using PyURDME},
author = {Drawert, Brian and Trogdon, Michael and Toor, Salman and Petzold, Linda and Hellander, Andreas},
abstractNote = {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.},
doi = {10.1137/15M1014784},
journal = {SIAM Journal on Scientific Computing},
number = 3,
volume = 38,
place = {United States},
year = {2016},
month = {6}
}