Identification of growth phases and influencing factors in cultivations with AGE1.HN cells using set-based methods.

Production of bio-pharmaceuticals in cell culture, such as mammalian cells, is challenging. Mathematical models can provide support to the analysis, optimization, and the operation of production processes. In particular, unstructured models are suited for these purposes, since they can be tailored to particular process conditions. To this end, growth phases and the most relevant factors influencing cell growth and product formation have to be identified. Due to noisy and erroneous experimental data, unknown kinetic parameters, and the large number of combinations of influencing factors, currently there are only limited structured approaches to tackle these issues. We outline a structured set-based approach to identify different growth phases and the factors influencing cell growth and metabolism. To this end, measurement uncertainties are taken explicitly into account to bound the time-dependent specific growth rate based on the observed increase of the cell concentration. Based on the bounds on the specific growth rate, we can identify qualitatively different growth phases and (in-)validate hypotheses on the factors influencing cell growth and metabolism. We apply the approach to a mammalian suspension cell line (AGE1.HN). We show that growth in batch culture can be divided into two main growth phases. The initial phase is characterized by exponential growth dynamics, which can be described consistently by a relatively simple unstructured and segregated model. The subsequent phase is characterized by a decrease in the specific growth rate, which, as shown, results from substrate limitation and the pH of the medium. An extended model is provided which describes the observed dynamics of cell growth and main metabolites, and the corresponding kinetic parameters as well as their confidence intervals are estimated. The study is complemented by an uncertainty and outlier analysis. Overall, we demonstrate utility of set-based methods for analyzing cell growth and metabolism under conditions of uncertainty.

Metabolic effects of influenza virus infection in cultured animal cells: Intra- and extracellular metabolite profiling.

BACKGROUND: Many details in cell culture-derived influenza vaccine production are still poorly understood and approaches for process optimization mainly remain empirical. More insights on mammalian cell metabolism after a viral infection could give hints on limitations and cell-specific virus production capacities. A detailed metabolic characterization of an influenza infected adherent cell line (MDCK) was carried out based on extracellular and intracellular measurements of metabolite concentrations. RESULTS: For most metabolites the comparison of infected (human influenza A/PR/8/34) and mock-infected cells showed a very similar behavior during the first 10-12 h post infection (pi). Significant changes were observed after about 12 h pi: (1) uptake of extracellular glucose and lactate release into the cell culture supernatant were clearly increased in infected cells compared to mock-infected cells. At the same time (12 h pi) intracellular metabolite concentrations of the upper part of glycolysis were significantly increased. On the contrary, nucleoside triphosphate concentrations of infected cells dropped clearly after 12 h pi. This behaviour was observed for two different human influenza A/PR/8/34 strains at slightly different time points. CONCLUSIONS: Comparing these results with literature values for the time course of infection with same influenza strains, underline the hypothesis that influenza infection only represents a minor additional burden for host cell metabolism. The metabolic changes observed after 12 h pi are most probably caused by the onset of apoptosis in infected cells. The comparison of experimental data from two variants of the A/PR/8/34 virus strain (RKI versus NIBSC) with different productivities and infection dynamics showed comparable metabolic patterns but a clearly different timely behavior. Thus, infection dynamics are obviously reflected in host cell metabolism.