In Silico Modeling of Virus Particle Propagation and Infectivity along the Respiratory Tract: A Case Study for SARS-COV-2

Abstract

Respiratory viruses including Respiratory syncytial virus (RSV), influenza virus and cornaviruses such as Middle Eastern respiratory virus (MERS) and SARS-CoV-2 infect and cause serious and sometimes fatal disease in thousands of people annually. It is critical to understand virus propagation dynamics within the respiratory system because new insights will increase our understanding of virus pathogenesis and enable infection patterns to be more predictable in vivo, which will enhance targeting of vaccines and drug delivery. This study presents a computational model of virus propagation within the respiratory tract network. The model includes the generation network branch structure of the respiratory tract, biophysical and infectivity properties of the virus, as well as air flow models that aid the circulation of the virus particles. The model can also consider the impact of the immune response aim to inhibit virus replication and spread. The model was applied to the SARS-CoV-2 virus by integrating data on its life-cycle, as well as density of Angiotensin Converting Enzyme (ACE2) expressing cells along the respiratory tract network. Using physiological data associated with the respiratory rate and virus load that is inhaled, the model can improve our understanding of the concentration and spatiotemporal dynamics of virus

How Physical Factors Coordinate Virus Infection: A Perspective From Mechanobiology

“…This demonstrated the conjecture that the nasopharynx served as the seeding region for the contamination of the lower airway via aspiration of SARS-CoV-2-laden boluses of nasopharyngeal fluids (Basu and Chakravarty, 2020). It is noted that a few previous studies associating virus infection with in silico models have been reported and summarized (Daun and Clermont, 2007;Vaziri and Gopinath, 2008;Ciupe and Heffernan, 2017;Vimalajeewa et al, 2020). It is not hard to see that there is still a long way to go to unravel the mysteries of biomechanics in virus infection by in silico models.…”

Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions

“…Many approaches do consider k c in the steadystate, but we like to draw attention by the community that it can have non-linear relationships with the infected humans, so it should also be associated with different disease stages over time that is bounded by transition probabilities of stage changes (severe stage to mild, and vice versa). Moreover, as shown in both (20) and (21), that non-linearities do exist, for example, the relationship V r (t) P v (t) is added to the model but is currently non-existing in the literature. The initial virus concentration V 0 can be directly obtained from values in Table I.…”

“…There are three main blocks in the model: the droplets entrance rate, the mouth-lung propagation, and the virus replication and deposition rates. The general model of virus propagation is based on a governing equation based on the Fick's second law for advection-diffusion investigated in [20]. This model accounts for the concentration of the virus in a particular branch of the respiratory tract G i , and can be represented as…”

“…Secondly, there is the infection process of internal progression. The model for the viral entry can be found in [20] , which accounts for virus dispersion along the respiratory tract impacted by factors such as the respiratory rate, viral exposure levels (i.e., the quantity of virus that is inhaled), and the virus particle size. The authors developed a computational model to account for the changes in the virus propagation as it enters and propagates in the respiratory tract.…”

“…Even though there are models for molecular communications for bacterial infection [15] , [16] , there have not been any surveys proposed for molecular communications models of viral infections. To date, molecular communications models for viral infection includes multi-hop transfer of genetic content through diffusion over extracellular channels [17] , viral propagation in the air [18] , [19] , propagation within the respiratory tract [20] , as well as interactions with host cells [21] . Even though these models are very interesting and provide a formidable representation of biology through the glasses of a molecular communications researcher, the issue of virus propagation and the infection itself are much more complex.…”