Biophysical Modeling of the SARS-CoV-2 Viral Cycle Reveals Ideal Antiviral Targets

University of Minnesota Effective therapies for COVID-19 are urgently needed. Presently there are thousands of COVID-19 clinical trials globally, many with drug combinations, resulting in an empirical process with an enormous number of possible combinations. To identify the most promising potential therapies, we developed a biophysical model for the SARS-CoV-2 viral cycle and performed a sensitivity analysis for individual model parameters and all possible pairwise parameter changes (162 = 256 possibilities). We found that model-predicted virion production is fairly insensitive to changes in most viral entry, assembly, and release parameters, but highly sensitive to some viral transcription and translation parameters. Furthermore, we found a cooperative benefit to pairwise targeting of transcription and translation, predicting that combined targeting of these processes will be especially effective in inhibiting viral production. I will discuss how the model has fared in light of clinical trial results, and current applications. Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility Jacob Barhak A white paper draft by the Model Reproducibility, Credibility and Standardization subgroup and the Integration subgroup. If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/