MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN...

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Spatial stochastic simulations provide crucial biological insights.
• High computational costs and complex software stacks hinder adoption.
• Barriers limit the scope of quantitative modeling in systems biology.

Purpose of the Study:

• Introduce PyURDME, a user-friendly spatial modeling package.
• Present MOLNs, a cloud appliance for distributed simulation.
• Facilitate reproducible and scalable computational experiments.

Main Methods:

• Developed PyURDME for spatial modeling and simulation.
• Created MOLNs, a cloud-based platform using IPython.
• Enabled distributed parallel computation for stochastic reaction-diffusion models.

Main Results:

• PyURDME and MOLNs reduce the complexity of setting up simulations.
• The tools support sharable and reproducible distributed parallel experiments.
• Increased accessibility to advanced computational tools for systems biology.

Conclusions:

• PyURDME and MOLNs lower the barrier to entry for spatial stochastic simulations.
• These tools enhance the productivity and reproducibility of computational biology research.
• Expanded capabilities for addressing complex biological questions through quantitative modeling.