MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

TL;DR

This paper introduces MOLNs, a cloud platform that simplifies the setup and execution of large-scale, reproducible spatial stochastic simulations in systems biology using PyURDME, making advanced modeling more accessible.

Contribution

It presents MOLNs, a cloud-based environment integrated with IPython, enabling scalable, reproducible, and interactive spatial stochastic simulations with a user-friendly interface.

Findings

Enables large-scale stochastic simulations on cloud infrastructure.

Facilitates reproducibility and sharing of computational experiments.

Reduces complexity for practitioners in systems biology.

Abstract

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, 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.