Multi-scale Modelling Reveals that Early Super-spreader Events are a Likely Contributor to Novel Variant Predominance

SARS-CoV-2 variants of concern have been characterized to varying degrees by higher transmissibility, worse infection outcomes and evasion of vaccine and infection-induced immunologic memory. Here we present a multi-scale model of SARS-CoV-2 dynamics that describes population spread through individuals whose viral loads and numbers of contacts (drawn from an over-dispersed distribution) are both time-varying. This stochastic framework allows us to explore how super-spreader events contribute to variant emergence. To learn more see: Goyal, Ashish, Daniel B. Reeves, and Joshua T. Schiffer. "Multi-scale modelling reveals that early super-spreader events are a likely contributor to novel variant predominance." Journal of the Royal Society Interface 19, no. 189 (2022): 20210811. https://royalsocietypublishing.org/doi/full/10.1098/rsif.2021.0811 To view the slides in this video, visit: https://drive.google.com/file/d/1EP4DAu081s1NhoPpthJpiSRV_2Bj5I44/ Quantitative Prediction of Conditional Vulnerabilities in Regulatory and Metabolic Networks Using EGRIN and PRIME Nitin Baliga Institute for System Biology The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. I will present two predictive models called EGRIN and PRIME to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. (Time permitting) I will also show how the models were used to uncover how combinatorial gene regulation enables C. difficile growth relative to commensal colonization in the mouse gut. To learn more see: Immanuel, Selva Rupa Christinal, Mario L. Arrieta-Ortiz, Rene A. Ruiz, Min Pan, Adrian Lopez Garcia de Lomana, Eliza JR Peterson, and Nitin S. Baliga. "Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using PRIME." npj Systems Biology and Applications 7, no. 1 (2021): 43. https://www.nature.com/articles/s41540-021-00205-6 To view the slides in this video, visit: https://docs.google.com/presentation/d/1Mv5Ba-T6J08qTwsYjLZ3ylpra67LZ3L4/ Moderator: James A. Glazier, PhD, Indiana University, Bloomington *Contents* 0:00 - Introduction by James Glazier 05:44 - Daniel Reeves: Multi-scale Modelling Reveals that Early Super-spreader Events are a Likely Contributor to Novel Variant Predominance 24:59 - Nitin Baliga: Quantitative Prediction of Conditional Vulnerabilities in Regulatory and Metabolic Networks Using EGRIN and PRIME 55:40 - Discussion If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://tinyurl.com/GLIMPRINTVideos 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/