ArticleSIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics2016
MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME.
Article in SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Programmatic modeling for biological systems.Current opinion in systems biology · 2021Article
- Coordinating cell polarization and morphogenesis through mechanical feedback.PLoS computational biology · 2021Article
- Towards reproducible computational drug discovery.Journal of cheminformatics · 2020Review
- Hierarchical algorithm for the reaction-diffusion master equation.The Journal of chemical physics · 2020Article
- Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning.Bioinformatics (Oxford, England) · 2019Article
- The effect of cell geometry on polarization in budding yeast.PLoS computational biology · 2018Article
- Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning.The Journal of chemical physics · 2017Article
- The Role of Chromatin Density in Cell Population Heterogeneity during Stem Cell Differentiation.Scientific reports · 2017Article
- Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist.PLoS computational biology · 2016Article
- A framework for discrete stochastic simulation on 3D moving boundary domains.The Journal of chemical physics · 2016Article
- Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion.PLoS computational biology · 2016Article
- GillesPy: A Python Package for Stochastic Model Building and Simulation.IEEE life sciences letters · 2016Article
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5 authors.
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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.
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Registered trials
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