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...
| Autores: | ; ; ; |
|---|---|
| Tipo de documento: | Artículo |
| Estado: | Published version |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/52729 |
| Acceso en línea: | http://hdl.handle.net/10230/52729 http://dx.doi.org/10.3390/v14020403 |
| Nivel de acceso: | 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 |
| _version_ | 1878732657971429376 |
|---|---|
| author | Sazonov, Igor |
| author2 | Grebennikov, Dmitry Meyerhans, Andreas Bocharov, Gennady A. |
| author2_role | author author author |
| author_browse | Bocharov, Gennady A. Grebennikov, Dmitry Meyerhans, Andreas Sazonov, Igor |
| author_facet | Sazonov, Igor Grebennikov, Dmitry Meyerhans, Andreas Bocharov, Gennady A. |
| author_role | author |
| collection | Repositorio Digital de la UPF |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_4737f05f6a4e7b077e2daa8cee96c8b9 |
| institution | Universitat Pompeu Fabra |
| instname_str | Universitat Pompeu Fabra |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:repositori.upf.edu:10230/52729 |
| publishDate | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| reponame_str | Repositorio Digital de la UPF |
| spelling | Sensitivity of SARS-CoV-2 life cycle to IFN effects and ACE2 binding unveiled with a stochastic model Sazonov, Igor Grebennikov, Dmitry Meyerhans, Andreas Bocharov, Gennady A. Markov Chain Monte Carlo method SARS-Cov-2 Mathematical model Sensitivity analysis Stochastic processes The ACE2 receptor Type I interferon (IFN) Virus dynamics 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. 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). MDPI http://hdl.handle.net/10230/52729 http://dx.doi.org/10.3390/v14020403 |
| 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 |
| status_str | publishedVersion |
| title | 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_short | 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 |
| topic | Markov Chain Monte Carlo method SARS-Cov-2 Mathematical model Sensitivity analysis Stochastic processes The ACE2 receptor Type I interferon (IFN) Virus dynamics |
| topic_facet | Markov Chain Monte Carlo method SARS-Cov-2 Mathematical model Sensitivity analysis Stochastic processes The ACE2 receptor Type I interferon (IFN) Virus dynamics |
| url | http://hdl.handle.net/10230/52729 http://dx.doi.org/10.3390/v14020403 |