Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
02 2019
Historique:
received: 30 08 2018
accepted: 22 01 2019
revised: 01 03 2019
pubmed: 20 2 2019
medline: 14 3 2019
entrez: 20 2 2019
Statut: epublish

Résumé

Influenza A viruses (IAV) are commonly used to infect animal cell cultures for research purposes and vaccine production. Their replication is influenced strongly by the multiplicity of infection (MOI), which ranges over several orders of magnitude depending on the respective application. So far, mathematical models of IAV replication have paid little attention to the impact of the MOI on infection dynamics and virus yields. To address this issue, we extended an existing model of IAV replication in adherent MDCK cells with kinetics that explicitly consider the time point of cell infection. This modification does not only enable the fitting of high MOI measurements, but also the successful prediction of viral release dynamics of low MOI experiments using the same set of parameters. Furthermore, this model allows the investigation of defective interfering particle (DIP) propagation in different MOI regimes. The key difference between high and low MOI conditions is the percentage of infectious virions among the total virus particle release. Simulation studies show that DIP interference at a high MOI is determined exclusively by the DIP content of the seed virus while, in low MOI conditions, it is predominantly controlled by the de novo generation of DIPs. Overall, the extended model provides an ideal framework for the prediction and optimization of cell culture-derived IAV manufacturing and the production of DIPs for therapeutic use.

Identifiants

pubmed: 30779733
doi: 10.1371/journal.pcbi.1006819
pii: PCOMPBIOL-D-18-01507
pmc: PMC6396949
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1006819

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Daniel Rüdiger (D)

Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

Sascha Young Kupke (SY)

Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

Tanja Laske (T)

Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

Pawel Zmora (P)

Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

Udo Reichl (U)

Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Chair of Bioprocess Engineering, Institute of Process Engineering, Faculty of Process & Systems Engineering, Otto-von-Guericke University, Magdeburg, Germany.

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Classifications MeSH