Sensitivity of SARS-CoV-2 Life Cycle to IFN Effects and ACE2 Binding Unveiled with a Stochastic Model.
Markov Chain Monte Carlo method
SARS-Cov-2
mathematical model
sensitivity analysis
stochastic processes
the ACE2 receptor
type I interferon (IFN)
virus dynamics
Journal
Viruses
ISSN: 1999-4915
Titre abrégé: Viruses
Pays: Switzerland
ID NLM: 101509722
Informations de publication
Date de publication:
15 02 2022
15 02 2022
Historique:
received:
17
12
2021
revised:
27
01
2022
accepted:
04
02
2022
entrez:
26
2
2022
pubmed:
27
2
2022
medline:
9
3
2022
Statut:
epublish
Résumé
Mathematical modelling of infection processes in cells is of fundamental interest. It helps to understand the SARS-CoV-2 dynamics in detail and can be useful to define the vulnerability steps targeted by antiviral treatments. We previously developed a deterministic mathematical model of the SARS-CoV-2 life cycle in a single cell. Despite answering many questions, it certainly cannot accurately account for the stochastic nature of an infection process caused by natural fluctuation in reaction kinetics and the small abundance of participating components in a single cell. In the present work, this deterministic model is transformed into a stochastic one based on a Markov Chain Monte Carlo (MCMC) method. This model is employed to compute statistical characteristics of the SARS-CoV-2 life cycle including the probability for a non-degenerate infection process. Varying parameters of the model enables us to unveil the inhibitory effects of IFN and the effects of the ACE2 binding affinity. The simulation results show that the type I IFN response has a very strong effect on inhibition of the total viral progeny whereas the effect of a 10-fold variation of the binding rate to ACE2 turns out to be negligible for the probability of infection and viral production.
Identifiants
pubmed: 35215996
pii: v14020403
doi: 10.3390/v14020403
pmc: PMC8875829
pii:
doi:
Substances chimiques
Interferon Type I
0
Angiotensin-Converting Enzyme 2
EC 3.4.17.23
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
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