Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling
SARS-CoV-2 infection represents a global threat to human health. Various approaches were employed to reveal the pathogenetic mechanisms of COVID-19. Mathematical and computational modelling is a powerful tool to describe and analyze the infection dynamics in relation to a plethora of processes contr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/13/9/1735 |
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| author | Dmitry Grebennikov Ekaterina Kholodareva Igor Sazonov Antonina Karsonova Andreas Meyerhans Gennady Bocharov |
| author_facet | Dmitry Grebennikov Ekaterina Kholodareva Igor Sazonov Antonina Karsonova Andreas Meyerhans Gennady Bocharov |
| author_sort | Dmitry Grebennikov |
| collection | DOAJ |
| description | SARS-CoV-2 infection represents a global threat to human health. Various approaches were employed to reveal the pathogenetic mechanisms of COVID-19. Mathematical and computational modelling is a powerful tool to describe and analyze the infection dynamics in relation to a plethora of processes contributing to the observed disease phenotypes. In our study here, we formulate and calibrate a deterministic model of the SARS-CoV-2 life cycle. It provides a kinetic description of the major replication stages of SARS-CoV-2. Sensitivity analysis of the net viral progeny with respect to model parameters enables the identification of the life cycle stages that have the strongest impact on viral replication. These three most influential parameters are (i) degradation rate of positive sense vRNAs in cytoplasm (negative effect), (ii) threshold number of non-structural proteins enhancing vRNA transcription (negative effect), and (iii) translation rate of non-structural proteins (positive effect). The results of our analysis could be used for guiding the search for antiviral drug targets to combat SARS-CoV-2 infection. |
| first_indexed | 2024-03-10T07:09:01Z |
| format | Article |
| id | doaj.art-c4974f391f3a41ec97dad98019809c1f |
| institution | Directory Open Access Journal |
| issn | 1999-4915 |
| language | English |
| last_indexed | 2024-03-10T07:09:01Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj.art-c4974f391f3a41ec97dad98019809c1f2023-11-22T15:37:13ZengMDPI AGViruses1999-49152021-08-01139173510.3390/v13091735Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical ModellingDmitry Grebennikov0Ekaterina Kholodareva1Igor Sazonov2Antonina Karsonova3Andreas Meyerhans4Gennady Bocharov5Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, RussiaMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, RussiaCollege of Engineering, Swansea University, Bay Campus, Fabian Way, Swansea SA1 8EN, UKDepartment of Clinical Immunology and Allergology, Sechenov First Moscow State Medical University, 119991 Moscow, RussiaInfection Biology Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, SpainMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, RussiaSARS-CoV-2 infection represents a global threat to human health. Various approaches were employed to reveal the pathogenetic mechanisms of COVID-19. Mathematical and computational modelling is a powerful tool to describe and analyze the infection dynamics in relation to a plethora of processes contributing to the observed disease phenotypes. In our study here, we formulate and calibrate a deterministic model of the SARS-CoV-2 life cycle. It provides a kinetic description of the major replication stages of SARS-CoV-2. Sensitivity analysis of the net viral progeny with respect to model parameters enables the identification of the life cycle stages that have the strongest impact on viral replication. These three most influential parameters are (i) degradation rate of positive sense vRNAs in cytoplasm (negative effect), (ii) threshold number of non-structural proteins enhancing vRNA transcription (negative effect), and (iii) translation rate of non-structural proteins (positive effect). The results of our analysis could be used for guiding the search for antiviral drug targets to combat SARS-CoV-2 infection.https://www.mdpi.com/1999-4915/13/9/1735SARS-CoV-2intracellular replicationmathematical modelsensitivity analysistargets for drugs |
| spellingShingle | Dmitry Grebennikov Ekaterina Kholodareva Igor Sazonov Antonina Karsonova Andreas Meyerhans Gennady Bocharov Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling Viruses SARS-CoV-2 intracellular replication mathematical model sensitivity analysis targets for drugs |
| title | Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling |
| title_full | Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling |
| title_fullStr | Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling |
| title_full_unstemmed | Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling |
| title_short | Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling |
| title_sort | intracellular life cycle kinetics of sars cov 2 predicted using mathematical modelling |
| topic | SARS-CoV-2 intracellular replication mathematical model sensitivity analysis targets for drugs |
| url | https://www.mdpi.com/1999-4915/13/9/1735 |
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