Ashlee Ford Versypt, Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York. Title: Multiscale Simulation of Lung Fibrosis Induced by SARS-CoV-2 Infection and Acute Respiratory Distress Syndrome.
Ashlee Ford Versypt, Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York.
March 18, 2021, IMAG/MSM WG on Multiscale Modeling for Viral Pandemics
Yannis Kevrekidis Slides
Yannis Kevrekidis Video
Ashlee Ford Versypt Slides
Ashlee Ford Versypt Video
Yannis Kevrekidis Abstract: I will discuss some current developments on obtaining data driven models of agent-based systems. The focus will be on (a) finding good latent spaces (good "observables") from complicated, disorganized measurements and (b) learning dynamical equations for the evolution of these observables (through different ML tools). In particular, I will discuss the idea of creating useful embedding spaces for problems that involve dynamics on (possibly evolving) networks.
Ashlee Ford Versypt Abstract: The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge about immune system-virus-tissue interactions and how these can result in low-level infections in some cases and acute respiratory distress syndrome (ARDS) and other tissue damage in others is limited. We are developing an open-source, multi-scale tissue simulator that can be used to investigate mechanisms of intracellular viral replication, infection of epithelial cells, host immune response, and tissue damage. Our model can simulate fibroblast-mediated collagen deposition to account for the fibrosis at the damaged site in response to immune-response-induced tissue injury. The severity of infection and collagen deposition depends on the anti-inflammatory cytokine secretion rate, multiplicity of infection, and contact time for a CD8+ T cell to kill an infected cell. Additionally, the change in the ACE2 receptor concentration from the multiscale model has been used in a separate model of renin-angiotensin system to predict the change in ANGII, which is a biomarker for hypertension, pro-inflammation, and pro-fibrosis.