Team Leader (MOD)

Prof. Dr.-Ing. Udo Reichl
Prof. Dr.-Ing. Udo Reichl
Phone: +49 391 6110 201
Fax: +49 391 6110 203
Room: N0.19

Researcher

Daniel Rüdiger, M. Sc.
Daniel Rüdiger, M. Sc.
Phone: +49 391 6110 206
Room: N0.05

Stochastic Multiscale Modeling of Influenza Virus Replication in Cell Cultures

Stochastic Multiscale Modeling of Influenza Virus Replication in Cell Cultures

Motivation

Human influenza vaccines are mainly produced in embryonated chicken eggs. This process is restricted by complex logistics and a limited production capacity. Recently, cell culture-based vaccine production strategies have been developed to address these limitations. The optimization of these novel production processes is supported by an explicit understanding of the regulatory mechanisms during influenza virus replication in cell cultures.
Influenza virus production in cell cultures is normally initiated at a low multiplicity of infection (MOI) to reach high virus yields [1]. Thus, during the initial phase of virus production only few cells are infected. Typically by just one virus particle. The genome of this single virion has to be successfully replicated to enable the generation and release of infectious progeny virus particles. Under such conditions stochastic effects can exert a considerable influence on virus replication dynamics and yields.

Variability of maximum virus yield and the time delay before cells start to release considerable amounts of progeny virus particles. Zoom Image

Variability of maximum virus yield and the time delay before cells start to release considerable amounts of progeny virus particles.

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Aim of the project

To improve the quantitative understanding of cell culture-based influenza vaccine production our group developed mathematical models that describe the dynamics of influenza virus replication in single cells and in cell populations [2,3].

The main focus of this project is the development of a mathematical model that describes the effects of random fluctuations during the initial phase of host cell infection on the delay of virus release and the final yield of virus production. We incorporate this variability by applying a stochastic approach for modeling the dynamics of intra- and extracellular virus propagation. The results of these investigations could optimize vaccine production as well as advance medical strategies against influenza.

References

1.
Gallo Ramirez, L. E.; Nikolay, A.; Genzel, Y.; Reichl, U.: Bioreactor concepts for cell culture-based viral vaccine production. Expert Review of Vaccines 14 (9), pp. 1181 - 1191 (2015)
2.
Heldt, F. S.; Frensing, T.; Reichl, U.: Modeling the intracellular dynamics of influenza virus replication to understand the control of viral RNA synthesis. Journal of Virology 86 (15), pp. 7806 - 7817 (2012)
3.
Heldt, S.; Frensing, T.; Pflugmacher, A.; Gröpler, R.; Peschel, B.; Reichl, U.: Multiscale Modeling of Influenza A Virus Infection Supports the Development of Direct-Acting Antivirals. PLoS Computational Biology 9 (11), p. e1003372 (2013)
 
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