Welcome to the UPF Digital Repository

Intracellular life cycle kinetics of SARS-CoV-2 predicted using mathematical modelling

Show simple item record

dc.contributor.author Grebennikov, Dmitry
dc.contributor.author Kholodareva, Ekaterina
dc.contributor.author Sazonov, Igor
dc.contributor.author Karsonova, Antonina
dc.contributor.author Meyerhans, Andreas
dc.contributor.author Bocharov, Gennady A.
dc.date.accessioned 2021-10-20T06:29:34Z
dc.date.available 2021-10-20T06:29:34Z
dc.date.issued 2021
dc.identifier.citation Grebennikov D, Kholodareva E, Sazonov I, Karsonova A, Meyerhans A, Bocharov G. Intracellular life cycle kinetics of SARS-CoV-2 predicted using mathematical modelling. Viruses. 2021;13(9):1735. DOI: 10.3390/v13091735
dc.identifier.issn 1999-4915
dc.identifier.uri http://hdl.handle.net/10230/48715
dc.description.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 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.
dc.description.sponsorship The reported study was funded by RFBR according to the research project numbers 20-04-60157 and 20-01-00352. AM 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). G.B. and D.G. were partly supported by Moscow Center for Fundamental and Applied Mathematics (agreement with the Ministry of Education and Science of the Russian Federation No. 075-15-2019-1624).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI
dc.relation.ispartof Viruses. 2021;13(9):1735
dc.rights © 2021 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 (http://creativecommons.org/licenses/by/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Intracellular life cycle kinetics of SARS-CoV-2 predicted using mathematical modelling
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3390/v13091735
dc.subject.keyword SARS-CoV-2
dc.subject.keyword Intracellular replication
dc.subject.keyword Mathematical model
dc.subject.keyword Sensitivity analysis
dc.subject.keyword Targets for drugs
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-106323RB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search


My Account


Compliant to Partaking