Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model

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...

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Autores: Sazonov, Igor, Grebennikov, Dmitry, Meyerhans, Andreas, Bocharov, Gennady A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/52729
Acceso en línea:http://hdl.handle.net/10230/52729
http://dx.doi.org/10.3390/v14020403
Access Level:acceso abierto
Palabra clave:Markov Chain Monte Carlo method
SARS-Cov-2
Mathematical model
Sensitivity analysis
Stochastic processes
The ACE2 receptor
Type I interferon (IFN)
Virus dynamics
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spelling Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic modelSazonov, IgorGrebennikov, DmitryMeyerhans, AndreasBocharov, Gennady A.Markov Chain Monte Carlo methodSARS-Cov-2Mathematical modelSensitivity analysisStochastic processesThe ACE2 receptorType I interferon (IFN)Virus dynamicsMathematical 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.This research was funded by the Russian Science Foundation (grant number 18-11-00171) and partly by the Russian Foundation for Basic Research according to the research project numbers 20-04-60157 and 20-01-00352. A.M. is also supported by the Spanish Ministry of Science and Innovation grant no. PID2019-106323RB-I00(AEI/MINEICO/FEDER, UE) and “Unidad de Excelencia María de Maeztu” funded by the AEI (CEX2018-000792-M). D.G. was partly supported by the Moscow Center for Fundamental and Applied Mathematics (agreement with the Ministry of Education and Science of the Russian Federation No. 075-15-2019-1624).MDPI202220222022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/52729http://dx.doi.org/10.3390/v14020403reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésViruses. 2022 Feb 15;14(2):403info:eu-repo/grantAgreement/ES/2PE/PID2019-106323RB-I00© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/527292026-05-29T05:05:01Z
dc.title.none.fl_str_mv Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model
title Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model
spellingShingle Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model
Sazonov, Igor
Markov Chain Monte Carlo method
SARS-Cov-2
Mathematical model
Sensitivity analysis
Stochastic processes
The ACE2 receptor
Type I interferon (IFN)
Virus dynamics
title_short Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model
title_full Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model
title_fullStr Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model
title_full_unstemmed Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model
title_sort Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model
dc.creator.none.fl_str_mv Sazonov, Igor
Grebennikov, Dmitry
Meyerhans, Andreas
Bocharov, Gennady A.
author Sazonov, Igor
author_facet Sazonov, Igor
Grebennikov, Dmitry
Meyerhans, Andreas
Bocharov, Gennady A.
author_role author
author2 Grebennikov, Dmitry
Meyerhans, Andreas
Bocharov, Gennady A.
author2_role author
author
author
dc.subject.none.fl_str_mv Markov Chain Monte Carlo method
SARS-Cov-2
Mathematical model
Sensitivity analysis
Stochastic processes
The ACE2 receptor
Type I interferon (IFN)
Virus dynamics
topic Markov Chain Monte Carlo method
SARS-Cov-2
Mathematical model
Sensitivity analysis
Stochastic processes
The ACE2 receptor
Type I interferon (IFN)
Virus dynamics
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/52729
http://dx.doi.org/10.3390/v14020403
url http://hdl.handle.net/10230/52729
http://dx.doi.org/10.3390/v14020403
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Viruses. 2022 Feb 15;14(2):403
info:eu-repo/grantAgreement/ES/2PE/PID2019-106323RB-I00
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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