Rapid community-driven development of a SARS-CoV-2 tissue simulator

Yafei Wang; Randy Heiland; Morgan Craig; Courtney L. Davis; Ashlee N Ford Versypt; Adrianne Jenner; Jonathan Ozik; Nicholson Collier; Chase Cockrell; Andrew Becker; Gary An; James A. Glazier; Aarthi Narayanan; Amber M Smith; Paul Macklin

The concurrent growth and advancements in the three areas of 1) mechanistic simulation modeling, 2) advanced, AI-driven model exploration algorithms, and 3) high-performance computing (HPC) provides the opportunity for large-scale exploration of the complex design spaces in detailed dynamical simulation models. However, if we don't take deliberate efforts to formally facilitate this intersection across our research communities, we risk producing a series of disparate individual efforts, limited in interoperability, transparency, reproducibility and scalability. The EMEWS (extreme model exploration with Swift) framework 100 was developed to directly address this issue and to provide a broadly applicable cyberinfrastructure to lower the barriers for utilization of advanced, large-scale model exploration on HPC resources. The EMEWS paradigm allows for the direct exploitation of cutting edge statistical and machine learning algorithms that make up the vibrant ecosystem of free and open source libraries that are continually added to and updated as research frontiers are expanded, all while controlling simulation workflows that can be run anywhere from desktops to campus clusters and to the largest HPC resources.