Author: 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
Title: Rapid community-driven development of a SARS-CoV-2 tissue simulator Document date: 2020_4_5
ID: lq4tcyh4_97
Snippet: We have utilized EMEWS for learning-accelerated exploration of the parameter spaces of agent-based models of immunosurveillance against heterogeneous tumors 91, 92 . The approach allowed for iterative and efficient discovery of optimal control and regression regions within biological and clinical constraints of the multi-scale biological systems. We have applied EMEWS across multiple science domains [101] [102] [103] [104] and developed large-sca.....
Document: We have utilized EMEWS for learning-accelerated exploration of the parameter spaces of agent-based models of immunosurveillance against heterogeneous tumors 91, 92 . The approach allowed for iterative and efficient discovery of optimal control and regression regions within biological and clinical constraints of the multi-scale biological systems. We have applied EMEWS across multiple science domains [101] [102] [103] [104] and developed large-scale algorithms to improve parameter estimation through approximate Bayesian computation (ABC) approaches 105 . These approaches, applied to the multi-scale modeling of SARS-CoV-2 dynamics, will provide the ability to robustly characterize model behaviors and produce improved capabilities for their interpretation.
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