Evidence map›Paper›PMID 34578317›Full record

ArticleViruses2021

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

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

Open access · goldAbstract read
In one paragraph

Article in Viruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 3 countries.

Dmitry GrebennikovMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, Russia.ORCID 0000-0002-7315-193X
Ekaterina KholodarevaMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, Russia.
Igor SazonovCollege of Engineering, Swansea University, Bay Campus, Fabian Way, Swansea SA1 8EN, UK.ORCID 0000-0001-6685-2351
Antonina KarsonovaDepartment of Clinical Immunology and Allergology, Sechenov First Moscow State Medical University, 119991 Moscow, Russia.ORCID 0000-0002-8697-9816
Andreas MeyerhansInfection Biology Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain.ORCID 0000-0003-0620-5317
Gennady BocharovMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), 119333 Moscow, Russia.ORCID 0000-0002-5049-0656
Russian Academy of Sciences · RUInstitució Catalana de Recerca i Estudis Avançats · ESSechenov University · RUSwansea University · GB

Funding

Ministry of Education and Science of the Russian Federation 075-15-2019-1624Russian Foundation for Basic Research 20-01-00352Russian Foundation for Basic Research 20-04-60157
6 · The paper itself

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.

Indexed as

Host-Pathogen InteractionsModels, BiologicalVirus ReplicationAlgorithmsAntiviral AgentsCOVID-19HumansLife Cycle StagesModels, TheoreticalReproducibility of ResultsSARS-CoV-2SoftwareAntiviral Agentsintracellular replicationmathematical modelSARS-CoV-2sensitivity analysistargets for drugs

Identifiers

PMID34578317
PMCPMC8473439
OpenAlexW3196556854

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.