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Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

Author

Listed:
  • Brian Drawert
  • Andreas Hellander
  • Ben Bales
  • Debjani Banerjee
  • Giovanni Bellesia
  • Bernie J Daigle Jr.
  • Geoffrey Douglas
  • Mengyuan Gu
  • Anand Gupta
  • Stefan Hellander
  • Chris Horuk
  • Dibyendu Nath
  • Aviral Takkar
  • Sheng Wu
  • Per Lötstedt
  • Chandra Krintz
  • Linda R Petzold

Abstract

We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.

Suggested Citation

  • Brian Drawert & Andreas Hellander & Ben Bales & Debjani Banerjee & Giovanni Bellesia & Bernie J Daigle Jr. & Geoffrey Douglas & Mengyuan Gu & Anand Gupta & Stefan Hellander & Chris Horuk & Dibyendu Na, 2016. "Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-15, December.
    • Handle: RePEc:plo:pcbi00:1005220
    DOI: 10.1371/journal.pcbi.1005220
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    References listed on IDEAS

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