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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/14/2/403 |
| _version_ | 1797476027696414720 |
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| author | Igor Sazonov Dmitry Grebennikov Andreas Meyerhans Gennady Bocharov |
| author_facet | Igor Sazonov Dmitry Grebennikov Andreas Meyerhans Gennady Bocharov |
| author_sort | Igor Sazonov |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T20:52:06Z |
| format | Article |
| id | doaj.art-f27005e441494372a893a97ca1c5b0e6 |
| institution | Directory Open Access Journal |
| issn | 1999-4915 |
| language | English |
| last_indexed | 2024-03-09T20:52:06Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj.art-f27005e441494372a893a97ca1c5b0e62023-11-23T22:32:20ZengMDPI AGViruses1999-49152022-02-0114240310.3390/v14020403Sensitivity of SARS-CoV-2 Life Cycle to IFN Effects and ACE2 Binding Unveiled with a Stochastic ModelIgor Sazonov0Dmitry Grebennikov1Andreas Meyerhans2Gennady Bocharov3Faculty of Science and Engineering, Swansea University, Bay Campus, Fabian Way, Swansea SA1 8EN, UKMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, RussiaInstitució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, SpainMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, RussiaMathematical 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.https://www.mdpi.com/1999-4915/14/2/403SARS-Cov-2type I interferon (IFN)the ACE2 receptorvirus dynamicsmathematical modelstochastic processes |
| spellingShingle | Igor Sazonov Dmitry Grebennikov Andreas Meyerhans Gennady Bocharov Sensitivity of SARS-CoV-2 Life Cycle to IFN Effects and ACE2 Binding Unveiled with a Stochastic Model Viruses SARS-Cov-2 type I interferon (IFN) the ACE2 receptor virus dynamics mathematical model stochastic processes |
| 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 | SARS-Cov-2 type I interferon (IFN) the ACE2 receptor virus dynamics mathematical model stochastic processes |
| url | https://www.mdpi.com/1999-4915/14/2/403 |
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