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Abstract

Vaccination against influenza contributes to counteracting pandemic outbreaks and is the principal measure for reducing the impact of annual epidemics. Hence, optimization of vaccine production is crucial for preventing influenza but requires a comprehensive understanding of infection dynamics in host cells. Combining experimental data and computational analysis to obtain quantitative models for influenza virus replication constitutes a major step toward such understanding. With respect to state-of-the-art human vaccine production, our lab investigates influenza replication in adherently growing Madin Darby canine kidney (MDCK) cells on different levels of the infection process. On the single cell level, copy numbers of viral genome segments (vRNAs), replicative intermediates (cRNAs) and messenger RNAs (vmRNAs) are monitored by a special real-time RT-qPCR method. Additionally, flow cytometry allows to track the infection status and apoptosis induction by different virus strains in a population of host cells. Here we present a multiscale modeling approach that integrates both levels to analyze influenza infection. In particular, the molecular basis of virus strain-specific differences in replication dynamics and host cell response (status of infection and apoptosis induction) are investigated. The model also contributes to the verification of hypotheses on how influenza switches from transcription to genome replication. By gaining a system-level understanding of influenza infection, we intend to pioneer novel process optimization strategies for large-scale vaccine production.