MS01 - IMMU-1
Brutus Buckeye Room (#3044) in The Ohio Union

Within-host SARS-CoV-2 viral and immune dynamics

Monday, July 17 at 10:30am

SMB2023 Follow Monday during the "MS01" time block.
Room assignment: Brutus Buckeye Room (#3044) in The Ohio Union.

Esteban A. Hernandez-Vargas, Hana Dobrovolny

Description:

The COVID-19 pandemic has received significant attention from the mathematical biology community to understand the transmission of the virus at the populational level. However, too little has been dedicated to the virus's host interactions and the host's immune system. Mathematical immunology offers qualitative and quantitative analyses of various immunological processes across many scales and in multiple settings. Mathematical models at the within-host level are central to understanding the dynamics, organization, and control of the immune system in patients with COVID-19. Modeling the interactions between SARS-CoV-2 and the immune system and the differences in disease severity will be the focus of discussion at this mini-symposium on computational models. The ground-breaking goal will be to bring experts to develop and maturate a within-host modeling approach as a new paradigm for a better preparedness against COVID-19. The mini-symposium is composed of 11 speakers. We suggest dividing PART I and II. We give the flexibility to the organizing committee to decide if they split the sessions or keep a long-running session.

Nora Heitzman-Breen

Virginia Tech (Mathematics)

"Modeling within-host and aerosol dynamics of SARS-CoV-2: the relationship with infectiousness"

The relationship between the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a host’s upper respiratory tract and in surrounding aerosols is key in understanding SARS-CoV-2 transmission and informing intervention strategies. We developed a within-host and aerosol mathematical model, accounting for both total RNA and infectious RNA, and used it to determine the relationship between viral kinetics in the upper respiratory track, viral kinetics in the aerosols, and new transmissions in golden hamsters challenged with SARS-CoV-2. We determined that infectious virus shedding early in infection correlates with transmission events, shedding of infectious virus diminishes late in the infection, and high viral RNA levels late in the infection are a poor indicator of transmission. We further showed that viral infectiousness increases in a density dependent manner with viral RNA and that their relative ratio is time-dependent. Such information is useful for designing interventions.
Additional authors: Stanca M. Ciupe

Melanie Moses

University of New Mexico (Computer Science)

"Spatial Immune Model of Coronavirus (SIMCoV) in the lung"

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. We further validate the branching model of the lung by showing that SIMCoV simulations of the spread of inflammation have similar growth rates and shapes to CT scans of SARS-CoV-2 infected lungs. We additionally model spread in the nasal cavity and compare to viral dynamics in that compartment. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.
Additional authors: Akil Andrews, University of New Mexico; Judy Cannon, University of New Mexico; Stephanie Forrest, Arizona State University; Alan Friedman, Purdue University; Steven Hofmeyr, Lawrence Berkeley National Laboratory; Ellie Larence, University of New Mexico; Kirtus Leyba, Arizona State University; Humayra Tasnim, University of New Mexico

Lars Kaderali

Univesity Medicine Greifswald (Institute of Bioinformatics)

"Modelling the intracellular replication of SARS-CoV-2 and related RNA viruses"

Positive-stranded RNA viruses are the largest group of viruses, and include human pathogens such as Dengue virus, haptitis C virus and coronaviruses, including SARS-CoV-2. They share many similarities in their lifecycle, albeit the diseases they cause show a wide spectrum of manifestations, from mild acute infections over long-term chronic infection to vigorous, life-threatening acute disease. We have developed detailed mathematical models of several positive stranded RNA viruses, and use these models to understand their within-host replication strategies, pan-viral similarities as well as virus-specific differences. In the talk, I will present our models on hepatitis C virus, dengue virus and coxsackievirus B3, and will compare these models to our ongoing work on modeling SARS-CoV-2, including detailed kinetic data and first results we have obtained in modeling the SARS-CoV-2 replication dynamics.

Hwayeon Ryu

Elon University (Mathematics)

"Mathematical Modeling of Immune Response to SARS-CoV-2"

Despite a tremendous volume of research in understanding the transmission of SARS-CoV-2 virus during the pandemic, how the human immune system responds to SARS-CoV-2 has not been yet fully understood due to limited analysis of the experimental or clinical information to date. In this work, we develop and analyze an in-host model to understand the role of various molecular pathways in successful viral clearance and to identify the key mechanisms responsible for disease severity exhibited by some patients. Our model explicitly represents the virus, innate immune cells, selected cytokines, and their interactions, which is formulated in a system of coupled ordinary and delay differential equations. With calibrated parameters against experimental data and literature we conduct numerical and sensitive analysis to determine the implications of variation of parameters. Our model demonstrates key aspects of immune response to SARS-CoV-2, specifically its sensitive pathways, which might be responsible for differences in disease severity exhibited by COVID-19 patients. Our results of the mechanisms involved in COVID-19 pathology could identify several therapeutic targets that would provide hypotheses to be tested clinically, thus, serving as a foundation for the development of evidence-based therapeutic strategies.

Mélanie Prague

Univ. Bordeaux, Inria, Inserm, Bordeaux Population Health, France (Statistics in Immunology and translational medicine)

"Joint modeling of viral and humoral response in Non-human primates to define mechanistic correlates of protection for SARS-CoV-2"

Determining correlates of protection is critical to the development of next-generation SARS-CoV-2 vaccines. And even when a correlate of protection has been identified, it is important to understand what level of that correlate needs to be achieved to provide protection from an event (which may be infection, transmission, or symptom severity...). In Alexandre et al. (eLife, 2022), we proposed a model-based approach to identify mechanistic correlates of protection based on dynamic modeling of viral dynamics and data mining of immunological markers using non-human primates studies (NHP). We have shown that RBD/ACE2 binding inhibition is a potent mechanism of protection against infection. Based on the analysis of the reproductive number in the animals, we propose a quantitative method to define a threshold for this correlate of protection against infection. We also extend the model to jointly describe the viral dynamics and the dynamics of the humoral response in naive, convalescent, and vaccinated NHP infected with SARS-CoV-2. We apply the method to three different studies in NHP investigating SARS-CoV-2 vaccines based on CD40 targeting, two-component spike nanoparticles, and mRNA.
Additional authors: Marie Alexandre Univ. Bordeaux, Inria, Inserm, Bordeaux Population Health, France; R. Marlin Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France; Roger le Grand Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France; Y. Levy AP-HP Hopital Mondor, Vaccine Research Institute, Creteil, France. R. Thiébaut Univ. Bordeaux, Inria, Inserm, Bordeaux Population Health, France

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Annual Meeting for the Society for Mathematical Biology, 2023.