tetano
Editor, Senior Moderator
PLoS Comput Biol. 2019 Feb 19;15(2):e1006819. doi: 10.1371/journal.pcbi.1006819. [Epub ahead of print]
[h=1]Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions.[/h] R?diger D[SUP]1[/SUP], Kupke SY[SUP]1[/SUP], Laske T[SUP]1[/SUP], Zmora P[SUP]1[/SUP], Reichl U[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
PMID: 30779733 DOI: 10.1371/journal.pcbi.1006819
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[h=1]Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions.[/h] R?diger D[SUP]1[/SUP], Kupke SY[SUP]1[/SUP], Laske T[SUP]1[/SUP], Zmora P[SUP]1[/SUP], Reichl U[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
PMID: 30779733 DOI: 10.1371/journal.pcbi.1006819
Free full text