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Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling

Dmitry Grebennikov, Ekaterina Kholodareva, Igor Sazonov Orcid Logo, Antonina Karsonova, Andreas Meyerhans, Gennady Bocharov

Viruses, Volume: 13, Issue: 9, Start page: 1735

Swansea University Author: Igor Sazonov Orcid Logo

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DOI (Published version): 10.3390/v13091735

Abstract

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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Published in: Viruses
ISSN: 1999-4915
Published: MDPI AG 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58314
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first_indexed 2021-10-13T09:48:05Z
last_indexed 2021-11-10T04:25:37Z
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spelling 2021-11-09T12:33:41.5784700 v2 58314 2021-10-13 Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling 05a507952e26462561085fb6f62c8897 0000-0001-6685-2351 Igor Sazonov Igor Sazonov true false 2021-10-13 AERO 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. Journal Article Viruses 13 9 1735 MDPI AG 1999-4915 intracellular replication, mathematical model, sensitivity analysis, targets for drugs, SARS-CoV-2 31 8 2021 2021-08-31 10.3390/v13091735 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University Russian Foundation for Basic Research Grant: 20-04-60157 Grant: 20-01-00352 Ministry of Education and Science of the Russian Federation Grant: 075-15-2019-1624 2021-11-09T12:33:41.5784700 2021-10-13T10:46:04.7310193 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Dmitry Grebennikov 1 Ekaterina Kholodareva 2 Igor Sazonov 0000-0001-6685-2351 3 Antonina Karsonova 4 Andreas Meyerhans 5 Gennady Bocharov 6 58314__21165__b2b123056de04deb82df23b8a97d1ee7.pdf 58314.pdf 2021-10-13T10:48:25.3773951 Output 648388 application/pdf Version of Record true © 2021 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling
spellingShingle Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling
Igor Sazonov
title_short 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_sort Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling
author_id_str_mv 05a507952e26462561085fb6f62c8897
author_id_fullname_str_mv 05a507952e26462561085fb6f62c8897_***_Igor Sazonov
author Igor Sazonov
author2 Dmitry Grebennikov
Ekaterina Kholodareva
Igor Sazonov
Antonina Karsonova
Andreas Meyerhans
Gennady Bocharov
format Journal article
container_title Viruses
container_volume 13
container_issue 9
container_start_page 1735
publishDate 2021
institution Swansea University
issn 1999-4915
doi_str_mv 10.3390/v13091735
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
active_str 0
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.
published_date 2021-08-31T04:14:45Z
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score 11.016235

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