# Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Results and Discussion

#### 2.1. Simulation of Cell Growth and Virus Production

^{5}cells/mL, cell concentrations increased exponentially in both shake flask cultivations (Figure 1), reaching a maximum cell growth rate (µ

_{max}) of 0.0025 h

^{−1}(Figure 1(A1,A2)). The first cultivation (Cultivation 1, mock-infected) reached a maximum cell concentration of 9.47 × 10

^{6}cells/mL at around 130 h before cells began to die due to substrate depletion. The second cultivation (Cultivation 2, infected) reached a cell concentration of 2.1 × 10

^{6}cells/mL at around 48 h and was infected with IAV at moi = 10 (infectivity based on TCID

_{50}assay). As soon as 3 hpi, cell concentrations, mean diameter of cells and consequently viable cell volumes started to decrease. In comparison, the mean cell diameters of Cultivation 1 decreased only gradually from a maximum of 14 µm (around 22 h post inoculation) to 11 µm (end of cultivation, Figure 1B). Similar findings for changes in the mean cell diameter of mock-infected cultures have been reported for adherent MDCK cells [45,53,54] and other suspension cell lines [52], though to a lesser degree. Overall, model simulations effectively reproduced the dynamics of cell concentrations, mean cell diameters and viable cell volumes for both mock-infected and infected cells. Accordingly, it can be safely assumed that the segregated model and the structured model were linked with good accuracy. The model simulations for both cultivations cover changes in the mean cell diameter of about 20%, resulting in up to 50% variation in the mean cell-specific volume (${V}_{s}^{c}$, Equation (9) in Supplementary File S1) and up to a 40% variation in volumetric enzyme activities (Equation (2) in Supplementary File S1). These changes are consistent with previous findings for lower volumetric enzyme activities during exponential cell growth compared to later cultivation phases for adherent MDCK cells [45,55] and other suspension cell lines [56].

_{10}(virions/mL) at 24 hpi (Figure 2C). The percentage of apoptotic cells started to increase at around 12 hpi (Figure 2D). Model simulations accurately describe the increase in number of infected cells and increase in the total number of virions.

#### 2.2. Simulation of Substrate and Metabolic By-Product Dynamics

^{+}to maintain a high ATP generation rate [59,60]. The previous model used lumped reaction to describe extracellular lactate production directly from intracellular pyruvate [52]. However, in this study, to better describe the extracellular lactate dynamics, lactate metabolism had to be considered in greater detail. In particular, it was assumed that intracellular lactate is produced via lactate dehydrogenase (LDH) in a reversible reaction, and an equation was added (Equation (61) in Supplementary File S1) to connect intracellular lactate to its extracellular form. LDH is a highly regulated enzyme with a very fast turnover. Additionally, depending on the metabolic state of the cell, it can favor either lactate production or lactate consumption. Lactate metabolism is complex and different theories exist regarding the control of lactate production and consumption [61,62,63,64,65,66,67,68]. Here, a reversible hill kinetic with two modifiers (Equation (61) in Supplementary File S1) was used and was sufficient to account for the inherent complexity. Furthermore, for the uptake of lactate, a reversible hill equation was used (Equation (45) in Supplementary File S1), which considers a minor lactate consumption after glucose depletion (Figure 3(B1) and ${r}_{La{c}_{trans}^{x}}$ in Figure S4). As a result, model simulations accurately reproduced the lactate dynamics in mock-infected and infected cells. Note that the accumulation of lactate in the bioreactor supernatant was estimated directly from the intracellular rates.

#### 2.3. Simulation of Intracellular Metabolism

#### 2.3.1. Glycolysis, Pentose Phosphate Cycle and Uridine Diphosphate Sugar Metabolism

#### 2.3.2. TCA Cycle

#### 2.3.3. Energy Metabolism

#### 2.3.4. Analysis of Intracellular Rates

## 3. Materials and Methods

#### 3.1. Shake Flask Cultivations

^{®}, New York city, NY, USA) with 50 mL working volume (wv), in a Multitron Pro incubator (Infors HT, Bottmingen, Switzerland) at 37 °C and 5% CO

_{2}atmosphere with a shaking frequency of 180 rpm. Cells were passaged every 3–4 days with a seeding density of 0.5–0.8 × 10

^{6}cells/mL.

^{9}TCID

_{50}/mL.

^{6}cells/mL) and the high moi used, neither medium replacement nor trypsin addition (for virus activation) was necessary. In both cultivations, sterile Milli-Q water was added before sampling to compensate for water evaporation (1–2 mL/day) since the experiment was performed in a non-hydrated incubator.

#### 3.2. Analytics

#### 3.2.1. Cell Count and Cell Volume

#### 3.2.2. Hemagglutination Activity Assay

^{1–12}and 2

^{0.5–12}) in the wells of a 96-round-bottom-well plate (of 100 µL) with PBS. Afterwards, a chicken erythrocyte solution (100 µL) was added with a concentration of 2 × 10

^{7}erythrocytes/mL (Ery, erythrocytes/mL) and incubated for 3–8 h at RT. The erythrocyte agglutination was evaluated using a plate reader (Infinite

^{®}M200 microplate reader, Tecan Group, Männedorf, Switzerland) measuring the extinction at 700 nm. A curve function was fitted to the data and used to determine the dilution at which the agglutination stops, which corresponds to the HA activity. The virus titer is commonly expressed as logarithm of the hemagglutination units (HAU) per analysis volume: log

_{10}(HAU/100 µL). Assuming that the virus and erythrocyte concentration are equal at the highest diluted sample showing agglutination, the concentration of the total number of virus particles (${V}_{p}$, virions per mL) can be calculated using Equation (1). The standard deviation of this assay is 0.08 log

_{10}(HAU/100 µL) [96].

#### 3.2.3. Imaging Flow Cytometry

#### 3.2.4. Extracellular Metabolites

#### 3.2.5. Intracellular Metabolites

#### 3.3. Model Definition

#### 3.3.1. Segregated Cell Growth and Infection Model

_{max}is the maximum specific cell growth rate and ${k}_{Gl{c}^{x}}^{m}$ is the Monod constant. A time dependent sigmoidal step function (Φ

_{1}and Φ

_{2}) are used to take into account the decrease in µ and increase in the death rate (k

_{d}, Equation (6)), respectively, after virus infection. This function was previously used to describe the transition of viable cell to apoptosis for IAV infected cells [102], and the smoothness of the transition depends on a constant (${\rho}_{1}$, manually adjusted) and the number of hours post infection ($hpi$).

#### 3.3.2. Structured Model of the Central Carbon Metabolism

^{x}), extracellular glutamate (Glu

^{x}), extracellular ammonium (NH4

^{x}), Cit and Keto, the model described by Ramos et al. [52] was updated in a few cases. Whether this was because parts of the central carbon metabolism of suspension MDCK cells differ from metabolism of suspension AGE1.HN cells or that metabolism of suspension cells differs for cultivations performed in shaker flasks (MDCK cells) and stirred tank reactor (AGE1.HN cells) or both cannot be decided for now. The main changes concern few enzymes or transporter kinetics such as enolase (ENO), aldolase (ALD), pyruvate kinase (PK), pyruvate carboxylase (PC), glutaminase (Glnase), ammonium transporter ($NH{4}_{trans}^{x}$), citrate lyase (CL) and aspartate transaminase (AspTA) according to the literature [70]. The equations for the structured intracellular metabolism states (ODEs and kinetics) along with the symbols used are provided in the Supplementary Files S1 and S2, respectively. The MATLAB version of the model for simulation is provided in Supplementary File S3.

#### 3.4. Parameter Fitting and Model Simulation

## 4. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Genzel, Y.; Rödig, J.; Rapp, E.; Reichl, U. Vaccine Production: Upstream Processing with Adherent or Suspension Cell Lines. In Animal Cell Biotechnology; Pörtner, R., Ed.; Methods in Molecular Biology; Humana Press: Totowa, NJ, USA, 2014; Volume 1104, pp. 371–393. ISBN 978-1-62703-732-7. [Google Scholar]
- Robertson, J.S.; Cook, P.; Attwell, A.M.; Williams, S.P. Replicative advantage in tissue culture of egg-adapted influenza virus over tissue-culture derived virus: Implications for vaccine manufacture. Vaccine
**1995**, 13, 1583–1588. [Google Scholar] [CrossRef] - Govorkova, E.A.; Kodihalli, S.; Alymova, I.V.; Fanget, B.; Webster, R.G. Growth and immunogenicity of influenza viruses cultivated in Vero or MDCK cells and in embryonated chicken eggs. Dev. Biol. Stand.
**1999**, 98, 39–51; discussion 73–74. [Google Scholar] [PubMed] - Tree, J.A.; Richardson, C.; Fooks, A.R.; Clegg, J.C.; Looby, D. Comparison of large-scale mammalian cell culture systems with egg culture for the production of influenza virus A vaccine strains. Vaccine
**2001**, 19, 3444–3450. [Google Scholar] [CrossRef] - Hussain, A.I.; Cordeiro, M.; Sevilla, E.; Liu, J. Comparison of egg and high yielding MDCK cell-derived live attenuated influenza virus for commercial production of trivalent influenza vaccine: In vitro cell susceptibility and influenza virus replication kinetics in permissive and semi-permissive cells. Vaccine
**2010**, 28, 3848–3855. [Google Scholar] [CrossRef] [PubMed] - Gregersen, J.-P.; Schmitt, H.-J.; Trusheim, H.; Bröker, M. Safety of MDCK cell culture-based influenza vaccines. Future Microbiol.
**2011**, 6, 143–152. [Google Scholar] [CrossRef] [PubMed] - Ambrozaitis, A.; Groth, N.; Bugarini, R.; Sparacio, V.; Podda, A.; Lattanzi, M. A novel mammalian cell-culture technique for consistent production of a well-tolerated and immunogenic trivalent subunit influenza vaccine. Vaccine
**2009**, 27, 6022–6029. [Google Scholar] [CrossRef] [PubMed] - Tzeng, T.-T.; Chen, P.-L.; Weng, T.-C.; Tsai, S.-Y.; Lai, C.-C.; Chou, H.-I.; Chen, P.-W.; Lu, C.-C.; Liu, M.-T.; Sung, W.-C.; et al. Development of high-growth influenza H7N9 prepandemic candidate vaccine viruses in suspension MDCK cells. J. Biomed. Sci.
**2020**, 27, 47. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Lowy, R.J. Influenza virus induction of apoptosis by intrinsic and extrinsic mechanisms. Int. Rev. Immunol.
**2003**, 22, 425–449. [Google Scholar] [CrossRef] [PubMed] - Santos, L.A.; Solá, S.; Rodrigues, C.M.P.; Rebelo-de-Andrade, H. Distinct kinetics and pathways of apoptosis in influenza A and B virus infection. Virus Res.
**2015**, 205, 33–40. [Google Scholar] [CrossRef] [PubMed] - Ludwig, S.; Pleschka, S.; Planz, O.; Wolff, T. Ringing the alarm bells: Signalling and apoptosis in influenza virus infected cells. Cell. Microbiol.
**2006**, 8, 375–386. [Google Scholar] [CrossRef] - de Vries, W.; Haasnoot, J.; van der Velden, J.; van Montfort, T.; Zorgdrager, F.; Paxton, W.; Cornelissen, M.; van Kuppeveld, F.; de Haan, P.; Berkhout, B. Increased virus replication in mammalian cells by blocking intracellular innate defense responses. Gene Ther.
**2008**, 15, 545–552. [Google Scholar] [CrossRef] [PubMed] - Young, D.F.; Andrejeva, L.; Livingstone, A.; Goodbourn, S.; Lamb, R.A.; Collins, P.L.; Elliott, R.M.; Randall, R.E. Virus Replication in Engineered Human Cells That Do Not Respond to Interferons. J. Virol.
**2003**, 77, 2174–2181. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Schulze-Horsel, J.; Schulze, M.; Agalaridis, G.; Genzel, Y.; Reichl, U. Infection dynamics and virus-induced apoptosis in cell culture-based influenza vaccine production-Flow cytometry and mathematical modeling. Vaccine
**2009**, 27, 2712–2722. [Google Scholar] [CrossRef] [PubMed] - Majors, B.S.; Betenbaugh, M.J.; Chiang, G.G. Links between metabolism and apoptosis in mammalian cells: Applications for anti-apoptosis engineering. Metab. Eng.
**2007**, 9, 317–326. [Google Scholar] [CrossRef] [PubMed] - Kim, J.W.; Dang, C.V. Multifaceted roles of glycolytic enzymes. Trends Biochem. Sci.
**2005**, 30, 142–150. [Google Scholar] [CrossRef] [PubMed] - Pastorino, J.; Hoek, J. Hexokinase II: The Integration of Energy Metabolism and Control of Apoptosis. Curr. Med. Chem.
**2005**, 10, 1535–1551. [Google Scholar] [CrossRef] [PubMed] - Shaw, M.L.; Stertz, S. Role of Host Genes in Influenza Virus Replication. In Cellular and Molecular Immunology; Springer: Cham, Switzerland, 2017; pp. 151–189. ISBN 9783030053697. [Google Scholar]
- Fernandes, P.; Santiago, V.M.; Rodrigues, A.F.; Tomás, H.; Kremer, E.J.; Alves, P.M.; Coroadinha, A.S. Impact of E1 and Cre on Adenovirus Vector Amplification: Developing MDCK CAV-2-E1 and E1-Cre Transcomplementing Cell Lines. PLoS ONE
**2013**, 8, e60342. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Laske, T.; Bachmann, M.; Dostert, M.; Karlas, A.; Wirth, D.; Frensing, T.; Meyer, T.F.; Hauser, H.; Reichl, U. Model-based analysis of influenza A virus replication in genetically engineered cell lines elucidates the impact of host cell factors on key kinetic parameters of virus growth. PLoS Comput. Biol.
**2019**, 15, e1006944. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Ritter, J.B.; Wahl, A.S.; Freund, S.; Genzel, Y.; Reichl, U. Metabolic effects of influenza virus infection in cultured animal cells: Intra- and extracellular metabolite profiling. BMC Syst. Biol.
**2010**, 4, 61. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Genzel, Y.; Behrendt, I.; König, S.; Sann, H.; Reichl, U. Metabolism of MDCK cells during cell growth and influenza virus production in large-scale microcarrier culture. Vaccine
**2004**, 22, 2202–2208. [Google Scholar] [CrossRef] [PubMed] - Silva, A.C.; Teixeira, A.P.; Alves, P.M. Impact of Adenovirus infection in host cell metabolism evaluated by 1 H-NMR spectroscopy. J. Biotechnol.
**2016**, 231, 16–23. [Google Scholar] [CrossRef] [PubMed] - Vastag, L.; Koyuncu, E.; Grady, S.L.; Shenk, T.E.; Rabinowitz, J.D. Divergent Effects of Human Cytomegalovirus and Herpes Simplex Virus-1 on Cellular Metabolism. PLoS Pathog.
**2011**, 7, e1002124. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bernal, V.; Monteiro, F.; Carinhas, N.; Ambrósio, R.; Alves, P.M. An integrated analysis of enzyme activities, cofactor pools and metabolic fluxes in baculovirus-infected Spodoptera frugiperda Sf9 cells. J. Biotechnol.
**2010**, 150, 332–342. [Google Scholar] [CrossRef] [PubMed] - Sanchez, E.L.; Lagunoff, M. Viral activation of cellular metabolism. Virology
**2015**, 479, 609–618. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Cvijovic, M.; Almquist, J.; Hagmar, J.; Hohmann, S.; Kaltenbach, H.M.; Klipp, E.; Krantz, M.; Mendes, P.; Nelander, S.; Nielsen, J.; et al. Bridging the gaps in systems biology. Mol. Genet. Genomics
**2014**, 289, 727–734. [Google Scholar] [CrossRef] [PubMed] - Batt, B.C.; Kompala, D.S. A structured kinetic modeling framework for the dynamics of hybridoma growth and monoclonal antibody production in continuous suspension cultures. Biotechnol. Bioeng.
**1989**, 34, 515–531. [Google Scholar] [CrossRef] [PubMed] - Bailey, J.E. Mathematical Modeling and Analysis in Biochemical Engineering: Past Accomplishments and Future Opportunities. Biotechnol. Prog.
**1998**, 14, 8–20. [Google Scholar] [CrossRef] - van Riel, N.A.W. Dynamic modelling and analysis of biochemical networks: Mechanism-based models and model-based experiments. Brief. Bioinform.
**2006**, 7, 364–374. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Sidoli, F.R.; Mantalaris, A.; Asprey, S.P. Modelling of mammalian cells and cell culture processes. Cytotechnology
**2004**, 44, 27–46. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Miskovic, L.; Tokic, M.; Fengos, G.; Hatzimanikatis, V. Rites of passage: Requirements and standards for building kinetic models of metabolic phenotypes. Curr. Opin. Biotechnol.
**2015**, 36, 146–153. [Google Scholar] [CrossRef] [Green Version] - Le Novère, N. Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet.
**2015**, 16, 146–158. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Almquist, J.; Cvijovic, M.; Hatzimanikatis, V.; Nielsen, J.; Jirstrand, M. Kinetic models in industrial biotechnology—Improving cell factory performance. Metab. Eng.
**2014**, 24, 38–60. [Google Scholar] [CrossRef] [PubMed] - Macklin, D.N.; Ruggero, N.A.; Covert, M.W. The future of whole-cell modeling. Curr. Opin. Biotechnol.
**2014**, 28, 111–115. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Strutz, J.; Martin, J.; Greene, J.; Broadbelt, L.; Tyo, K. Metabolic kinetic modeling provides insight into complex biological questions, but hurdles remain. Curr. Opin. Biotechnol.
**2019**, 59, 24–30. [Google Scholar] [CrossRef] [PubMed] - Fröhlich, F.; Loos, C.; Hasenauer, J. Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes. In Methods in Molecular Biology; Springer: New York, NY, USA, 2019; Volume 1883, pp. 385–422. ISBN 9781493988822. [Google Scholar]
- Nielsen, J. Systems Biology of Metabolism. Annu. Rev. Biochem.
**2017**, 86, 245–275. [Google Scholar] [CrossRef] [PubMed] - von Stosch, M.; Peres, J.; de Azevedo, S.F.; Oliveira, R. Modelling biochemical networks with intrinsic time delays: A hybrid semi-parametric approach. BMC Syst. Biol.
**2010**, 4, 8–12. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Karr, J.R.; Sanghvi, J.C.; MacKlin, D.N.; Gutschow, M.V.; Jacobs, J.M.; Bolival, B.; Assad-Garcia, N.; Glass, J.I.; Covert, M.W. A whole-cell computational model predicts phenotype from genotype. Cell
**2012**, 150, 389–401. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Khodayari, A.; Zomorrodi, A.R.; Liao, J.C.; Maranas, C.D. A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Metab. Eng.
**2014**, 25, 50–62. [Google Scholar] [CrossRef] [PubMed] - Theobald, U.; Mailinger, W.; Baltes, M.; Rizzi, M.; Reuss, M. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: I. Experimental observations. Biotechnol. Bioeng.
**1997**, 55, 305–316. [Google Scholar] [CrossRef] - König, M.; Bulik, S.; Holzhütter, H.G. Quantifying the contribution of the liver to glucose homeostasis: A detailed kinetic model of human hepatic glucose metabolism. PLoS Comput. Biol.
**2012**, 8, e1002577. [Google Scholar] [CrossRef] [Green Version] - Noguchi, R.; Kubota, H.; Yugi, K.; Toyoshima, Y.; Komori, Y.; Soga, T.; Kuroda, S. The selective control of glycolysis, gluconeogenesis and glycogenesis by temporal insulin patterns. Mol. Syst. Biol.
**2013**, 9, 664. [Google Scholar] [CrossRef] [PubMed] - Rehberg, M.; Ritter, J.B.; Reichl, U. Glycolysis Is Governed by Growth Regime and Simple Enzyme Regulation in Adherent MDCK Cells. PLoS Comput. Biol.
**2014**, 10, e1003885. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bazil, J.N.; Buzzard, G.T.; Rundell, A.E. Modeling Mitochondrial Bioenergetics with Integrated Volume Dynamics. PLoS Comput. Biol.
**2010**, 6, e1000632. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Nazaret, C.; Heiske, M.; Thurley, K.; Mazat, J.P. Mitochondrial energetic metabolism: A simplified model of TCA cycle with ATP production. J. Theor. Biol.
**2009**, 258, 455–464. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Sidorenko, Y.; Reichl, U. Structured model of influenza virus replication in MDCK cells. Biotechnol. Bioeng.
**2004**, 88, 1–14. [Google Scholar] [CrossRef] [PubMed] - Martinez, V.; Gerdtzen, Z.P.; Andrews, B.A.; Asenjo, J.A. Viral vectors for the treatment of alcoholism: Use of metabolic flux analysis for cell cultivation and vector production. Metab. Eng.
**2010**, 12, 129–137. [Google Scholar] [CrossRef] [PubMed] - Carinhas, N.; Koshkin, A.; Pais, D.A.M.; Alves, P.M.; Teixeira, A.P. 13 C-metabolic flux analysis of human adenovirus infection: Implications for viral vector production. Biotechnol. Bioeng.
**2017**, 114, 195–207. [Google Scholar] [CrossRef] [PubMed] - Carinhas, N.; Pais, D.A.M.; Koshkin, A.; Fernandes, P.; Coroadinha, A.S.; Carrondo, M.J.T.; Alves, P.M.; Teixeira, A.P. Metabolic flux profiling of MDCK cells during growth and canine adenovirus vector production. Sci. Rep.
**2016**, 6, 23529. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Ramos, J.R.C.; Rath, A.G.; Genzel, Y.; Sandig, V.; Reichl, U. A dynamic model linking cell growth to intracellular metabolism and extracellular by-product accumulation. Biotechnol. Bioeng.
**2020**, 117, 1533–1553. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Rehberg, M.; Ritter, J.B.; Genzel, Y.; Flockerzi, D.; Reichl, U. The relation between growth phases, cell volume changes and metabolism of adherent cells during cultivation. J. Biotechnol.
**2013**, 164, 489–499. [Google Scholar] [CrossRef] [PubMed] - Rehberg, M.; Wetzel, M.; Ritter, J.B.; Reichl, U. The regulation of glutaminolysis and citric acid cycle activity during mammalian cell cultivation. IFAC Proc. Vol.
**2013**, 12, 48–53. [Google Scholar] [CrossRef] [Green Version] - Janke, R.; Genzel, Y.; Händel, N.; Wahl, A.; Reichl, U. Metabolic adaptation of MDCK cells to different growth conditions: Effects on catalytic activities of central metabolic enzymes. Biotechnol. Bioeng.
**2011**, 108, 2691–2704. [Google Scholar] [CrossRef] [PubMed] - Rath, A.G.; Rehberg, M.; Janke, R.; Genzel, Y.; Scholz, S.; Noll, T.; Rose, T.; Sandig, V.; Reichl, U. The influence of cell growth and enzyme activity changes on intracellular metabolite dynamics in AGE1.HN.AAT cells. J. Biotechnol.
**2014**, 178, 43–53. [Google Scholar] [CrossRef] [PubMed] - Rehberg, M. Dynamics in Growth and Metabolism of Adherent MDCK Cells Unraveled by an Integrated Modeling Approach; Otto-von-Guericke-Universität: Magdeburg, Germany, 2015. [Google Scholar]
- Rehberg, M.; Rath, A.; Ritter, J.B.; Genzel, Y.; Reichl, U. Changes in intracellular metabolite pools during growth of adherent MDCK cells in two different media. Appl. Microbiol. Biotechnol.
**2014**, 98, 385–397. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Lopez-Lazaro, M. The Warburg Effect: Why and How Do Cancer Cells Activate Glycolysis in the Presence of Oxygen? Anticancer. Agents Med. Chem.
**2008**, 8, 305–312. [Google Scholar] [CrossRef] [PubMed] - Pelicano, H.; Martin, D.S.; Xu, R.-H.; Huang, P. Glycolysis inhibition for anticancer treatment. Oncogene
**2006**, 25, 4633–4646. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Martínez, V.S.; Dietmair, S.; Quek, L.-E.; Hodson, M.P.; Gray, P.; Nielsen, L.K. Flux balance analysis of CHO cells before and after a metabolic switch from lactate production to consumption. Biotechnol. Bioeng.
**2013**, 110, 660–666. [Google Scholar] [CrossRef] [PubMed] - Xie, J.; Wu, H.; Dai, C.; Pan, Q.; Ding, Z.; Hu, D.; Ji, B.; Luo, Y.; Hu, X. Beyond Warburg effect—dual metabolic nature of cancer cells. Sci. Rep.
**2015**, 4, 4927. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mulukutla, B.C.; Yongky, A.; Grimm, S.; Daoutidis, P.; Hu, W.S. Multiplicity of steady states in glycolysis and shift of metabolic state in cultured mammalian cells. PLoS ONE
**2015**, 10, 1–20. [Google Scholar] [CrossRef] - Ryll, T.; Valley, U.; Wagner, R. Biochemistry of growth inhibition by ammonium ions in mammalian cells. Biotechnol. Bioeng.
**1994**, 44, 184–193. [Google Scholar] [CrossRef] [PubMed] - Schmid, G.; Blanch, H.W. Extra- and intracellular metabolite concentrations for murine hybridoma cells. Appl. Microbiol. Biotechnol.
**1992**, 36, 621–625. [Google Scholar] [CrossRef] [PubMed] - Genzel, Y.; Fischer, M.; Reichl, U. Serum-free influenza virus production avoiding washing steps and medium exchange in large-scale microcarrier culture. Vaccine
**2006**, 24, 3261–3272. [Google Scholar] [CrossRef] [PubMed] - Hartley, F.; Walker, T.; Chung, V.; Morten, K. Mechanisms driving the lactate switch in Chinese hamster ovary cells. Biotechnol. Bioeng.
**2018**, 115, 1890–1903. [Google Scholar] [CrossRef] [PubMed] - Im, D.-K.; Cheong, H.; Lee, J.S.; Oh, M.-K.; Yang, K.M. Protein kinase CK2-dependent aerobic glycolysis-induced lactate dehydrogenase A enhances the migration and invasion of cancer cells. Sci. Rep.
**2019**, 9, 5337. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bröer, S.; Bröer, A. Amino acid homeostasis and signalling in mammalian cells and organisms. Biochem. J.
**2017**, 474, 1935–1963. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Sauro, H.M. Enzyme Kinetics for Systems Biology; Future Skill Software (Ambrosius Publishing): Washington, DC, USA, 2012; ISBN 9780982477311. [Google Scholar]
- Sonnewald, U. Glutamate synthesis has to be matched by its degradation—Where do all the carbons go? J. Neurochem.
**2014**, 131, 399–406. [Google Scholar] [CrossRef] [PubMed] - Bissinger, T. Evaluation of MDCK Suspension Cell Lines for Influenza A Virus Production: Media, Metabolism, and Process Conditions; Otto-von-Guericke-Universität: Magdeburg, Germany, 2020. [Google Scholar]
- Lohr, V.; Hädicke, O.; Genzel, Y.; Jordan, I.; Büntemeyer, H.; Klamt, S.; Reichl, U. The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis. BMC Biotechnol.
**2014**, 14, 72. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Tanner, L.B.; Goglia, A.G.; Wei, M.H.; Sehgal, T.; Parsons, L.R.; Park, J.O.; White, E.; Toettcher, J.E.; Rabinowitz, J.D. Four Key Steps Control Glycolytic Flux in Mammalian Cells. Cell Syst.
**2018**, 7, 49–62.e8. [Google Scholar] [CrossRef] [PubMed] - Yalcin, A.; Telang, S.; Clem, B.; Chesney, J. Regulation of glucose metabolism by 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatases in cancer. Exp. Mol. Pathol.
**2009**, 86, 174–179. [Google Scholar] [CrossRef] [PubMed] - Sola-Penna, M.; Da Silva, D.; Coelho, W.S.; Marinho-Carvalho, M.M.; Zancan, P. Regulation of mammalian muscle type 6-phosphofructo-1-kinase and its implication for the control of the metabolism. IUBMB Life
**2010**, 62, 791–796. [Google Scholar] [CrossRef] [PubMed] - Eprintsev, A.T.; Wu, T.L.; Selivanova, N.V.; Khasan Khamad, A. Obtaining homogenous preparations of succinate dehydrogenase isoforms from the D-507 strain of Sphaerotilus natans. Appl. Biochem. Microbiol.
**2012**, 48, 541–545. [Google Scholar] [CrossRef] - Manhas, N.; Duong, Q.V.; Lee, P.; Richardson, J.D.; Robertson, J.D.; Moxley, M.A.; Bazil, J.N. Computationally modeling mammalian succinate dehydrogenase kinetics identifies the origins and primary determinants of ROS production. J. Biol. Chem.
**2020**, 295, 15262–15279. [Google Scholar] [CrossRef] [PubMed] - Cairns, R.A.; Harris, I.S.; Mak, T.W. Regulation of cancer cell metabolism. Nat. Rev. Cancer
**2011**, 11, 85–95. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Cantor, J.R.; Sabatini, D.M. Cancer cell metabolism: One hallmark, many faces. Cancer Discov.
**2012**, 2, 881–898. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Grüning, N.M.; Lehrach, H.; Ralser, M. Regulatory crosstalk of the metabolic network. Trends Biochem. Sci.
**2010**, 35, 220–227. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Hyde, R.; Taylor, P.M.; Hundal, H.S. Amino acid transporters: Roles in amino acid sensing and signalling in animal cells. Biochem. J.
**2003**, 373, 1–18. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Yuan, H.-X.; Xiong, Y.; Guan, K.-L. Nutrient Sensing, Metabolism, and Cell Growth Control. Mol. Cell
**2013**, 49, 379–387. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Barabási, A.L.; Oltvai, Z.N. Network biology: Understanding the cell’s functional organization. Nat. Rev. Genet.
**2004**, 5, 101–113. [Google Scholar] [CrossRef] - Zu, X.L.; Guppy, M. Cancer metabolism: Facts, fantasy, and fiction. Biochem. Biophys. Res. Commun.
**2004**, 313, 459–465. [Google Scholar] [CrossRef] - Wagner, B.A.; Venkataraman, S.; Buettner, G.R. The rate of oxygen utilization by cells. Free Radic. Biol. Med.
**2011**, 51, 700–712. [Google Scholar] [CrossRef] [Green Version] - Herst, P.M.; Berridge, M.V. Cell surface oxygen consumption: A major contributor to cellular oxygen consumption in glycolytic cancer cell lines. Biochim. Biophys. Acta—Bioenerg.
**2007**, 1767, 170–177. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mahmoudabadi, G.; Milo, R.; Phillips, R. Energetic cost of building a virus. Proc. Natl. Acad. Sci. USA
**2017**, 114, E4324–E4333. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Klemperer, H. Glucose breakdown in chick embryo cells infected with influenza virus. Virology
**1961**, 13, 68–77. [Google Scholar] [CrossRef] - Petch, D.; Butler, M. Profile of energy metabolism in a murine hybridoma: Glucose and glutamine utilization. J. Cell. Physiol.
**1994**, 161, 71–76. [Google Scholar] [CrossRef] [PubMed] - Bonarius, H.P.J.; Özemre, A.; Timmerarends, B.; Skrabal, P.; Tramper, J.; Schmid, G.; Heinzle, E. Metabolic-flux analysis of continuously cultured hybridoma cells using 13CO2 mass spectrometry in combination with 13C-lactate nuclear magnetic resonance spectroscopy and metabolite balancing. Biotechnol. Bioeng.
**2001**, 74, 528–538. [Google Scholar] [CrossRef] [PubMed] - Goudar, C.; Biener, R.; Boisart, C.; Heidemann, R.; Piret, J.; de Graaf, A.; Konstantinov, K. Metabolic flux analysis of CHO cells in perfusion culture by metabolite balancing and 2D [13C, 1H] COSY NMR spectroscopy. Metab. Eng.
**2010**, 12, 138–149. [Google Scholar] [CrossRef] [PubMed] - Dean, J.; Reddy, P. Metabolic analysis of antibody producing CHO cells in fed-batch production. Biotechnol. Bioeng.
**2013**, 110, 1735–1747. [Google Scholar] [CrossRef] [PubMed] - DeBerardinis, R.J.; Lum, J.J.; Hatzivassiliou, G.; Thompson, C.B. The Biology of Cancer: Metabolic Reprogramming Fuels Cell Growth and Proliferation. Cell Metab.
**2008**, 7, 11–20. [Google Scholar] [CrossRef] [Green Version] - Lohr, V.; Genzel, Y.; Behrendt, I.; Scharfenberg, K.; Reichl, U. A new MDCK suspension line cultivated in a fully defined medium in stirred-tank and wave bioreactor. Vaccine
**2010**, 28, 6256–6264. [Google Scholar] [CrossRef] - Kalbfuss, B.; Knöchlein, A.; Kröber, T.; Reichl, U. Monitoring influenza virus content in vaccine production: Precise assays for the quantitation of hemagglutination and neuraminidase activity. Biologicals
**2008**, 36, 145–161. [Google Scholar] [CrossRef] - Frensing, T.; Kupke, S.Y.; Bachmann, M.; Fritzsche, S.; Gallo-Ramirez, L.E.; Reichl, U. Influenza virus intracellular replication dynamics, release kinetics, and particle morphology during propagation in MDCK cells. Appl. Microbiol. Biotechnol.
**2016**, 100, 7181–7192. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Momose, F.; Kikuchi, Y.; Komase, K.; Morikawa, Y. Visualization of microtubule-mediated transport of influenza viral progeny ribonucleoprotein. Microbes Infect.
**2007**, 9, 1422–1433. [Google Scholar] [CrossRef] [PubMed] - Sellick, C.A.; Croxford, A.S.; Maqsood, A.R.; Stephens, G.; Westerhoff, H.V.; Goodacre, R.; Dickson, A.J. Metabolite profiling of recombinant CHO cells: Designing tailored feeding regimes that enhance recombinant antibody production. Biotechnol. Bioeng.
**2011**, 108, 3025–3031. [Google Scholar] [CrossRef] [PubMed] - Ritter, J.B.; Genzel, Y.; Reichl, U. High-performance anion-exchange chromatography using on-line electrolytic eluent generation for the determination of more than 25 intermediates from energy metabolism of mammalian cells in culture. J. Chromatogr. B Anal. Technol. Biomed. Life Sci.
**2006**, 843, 216–226. [Google Scholar] [CrossRef] [PubMed] - Ritter, J.B.; Genzel, Y.; Reichl, U. Simultaneous extraction of several metabolites of energy metabolism and related substances in mammalian cells: Optimization using experimental design. Anal. Biochem.
**2008**, 373, 349–369. [Google Scholar] [CrossRef] [PubMed] - Rüdiger, D.; Kupke, S.Y.; Laske, T.; Zmora, P.; Reichl, U. Multiscale modeling of influenza a virus replication in cell cultures predicts infection dynamics for highly different infection conditions. PLoS Comput. Biol.
**2019**, 15, 1–22. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Schmidt, H.; Jirstrand, M. Systems Biology Toolbox for MATLAB: A computational platform for research in systems biology. Bioinformatics
**2006**, 22, 514–515. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Cohen, S.D.; Hindmarsh, A.C. CVODE, a stiff/nonstiff ODE solver in C. Comput. Phys.
**1996**, 10, 138–143. [Google Scholar] [CrossRef] [Green Version] - Hansen, N.; Kern, S. Evaluating the CMA Evolution Strategy on Multimodal Test Functions. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2004; pp. 282–291. ISBN 9783540302179. [Google Scholar]
- Hansen, N.; Ostermeier, A. Completely Derandomized Self-Adaptation in Evolution Strategies. Evol. Comput.
**2001**, 9, 159–195. [Google Scholar] [CrossRef]

**Figure 1.**Dynamics of cell growth in mock-infected and infected suspension MDCK cells. (

**A1**,

**A2**) Viable cell concentration, (

**B1**,

**B2**) mean cell diameter and (

**C1**,

**C2**) total volume of viable cells. Data and error bars represent the mean and standard deviation of technical triplicates for two independent experiments (mock-infected and infected ). Lines: model simulations. Vertical blue lines correspond to 0, 12 and 24 h post infection. Experimental data used for parameter estimation:

**A1**,

**B1**,

**C1**(see Supplementary Files S4 and S5).

**Figure 2.**Dynamics of influenza A virus replication in suspension MDCK cells after synchronous infection at 48 h post inoculation. (

**A**) Percentage of infected cells, (

**B**) percentage of viral ribonucleoproteins (vRNP) in the cell nucleus, (

**C**) virus titer, and (

**D**) percentage of apoptotic cells. Vertical blue lines correspond to 0, 12 and 24 h post infection, respectively. Data and error bars represent the mean and standard deviation of technical triplicates for one experiment (infected ).

**Figure 3.**Dynamics of extracellular substrates and metabolic by-products in mock-infected and infected suspension MDCK cells. (

**A1**,

**A2**) Glucose, (

**B1**,

**B2**) lactate, (

**C1**,

**C2**) glutamine, (

**D1**,

**D2**) ammonium, (

**E1**,

**E2**) pyruvate, and (

**F1**,

**F2**) glutamate. Data and error bars represent the mean and standard deviation of technical triplicates for two independent experiments (mock-infected and infected ). Lines: model simulations. Vertical blue lines correspond to 0, 12 and 24 h post infection, respectively. The grey dashed lines indicate the limit of quantification for each metabolite and grey data points are under the limit of quantification. Experimental data used for parameter estimation:

**A1**,

**B1**,

**C1**,

**D1**,

**E1**and

**F1**(see Supplementary Files S4 and S5).

**Figure 4.**Dynamics of metabolites from glycolysis and pentose phosphate pathway in mock-infected and infected suspension MDCK cells (insert: 48–72 h of infected cultivation). (

**A1**,

**A2**) Uridine diphosphate glucose, (

**B1**,

**B2**) ribose–5-phosphate, (

**C1**,

**C2**) glucose-6-phosphate, (

**D1**,

**D2**) fructose-6-phosphate, (

**E1**,

**E2**) fructose-1,6-biphosphate, (

**F1**,

**F2**) 3-phosphoglutarate and (

**G1**,

**G2**) phosphoenolpyruvate. Data and error bars represent the mean and standard deviation of technical triplicates for two independent experiments (mock-infected and infected ). Lines: model simulations. Vertical blue lines correspond to (0, 12 and 24 h post infection, respectively). The grey lines indicate the limit of quantification for each metabolite and the grey data points are under the limit of quantification. Experimental data used for parameter estimation:

**A1**,

**B1**,

**C1**,

**D1**,

**E1**,

**F1**and

**G1**(see Supplementary Files S4 and S5).

**Figure 5.**Dynamics of metabolites from the citric acid cycle in mock-infected and infected suspension MDCK cells (insert: 48–72 h of infected cultivation). (

**A1**,

**A2**) Citrate, (

**B1**,

**B2**) cis-aconitate, (

**C1**,

**C2**) iso-citrate, (

**D1**,

**D2**) alpha-ketoglutarate, (

**E1**,

**E2**) succinate, (

**F1**,

**F2**) fumarate and (

**G1**,

**G2**) malate. Data and error bars represent the mean and standard deviation of technical triplicates for two independent experiments (mock-infected and infected ). Lines: model simulations. Vertical blue lines correspond to (0, 12 and 24 h post infection, respectively). The grey lines indicate the limit of quantification for each metabolite and the grey data points are under the limit of quantification. Experimental data used for parameter estimation:

**A1**,

**B1**,

**C1**,

**D1**,

**E1**,

**F1**and

**G1**(see Supplementary Files S4 and S5).

**Figure 6.**Dynamics of ATP in mock-infected and infected suspension MDCK cells. (

**A1**,

**A2**) Adenosine tri-phosphate (insert: 48–72 h of infected cultivation). Data and error bars represent the mean and standard deviation of technical triplicates for two independent experiments (mock-infected and infected ). Lines: model simulations. Vertical blue lines correspond to (0, 12 and 24 h post infection, respectively). The grey lines indicate the limit of quantification for each metabolite and the grey data points are under the limit of quantification. Experimental data used for parameter estimation:

**A1**(see Supplementary Files S4 and S5).

**Figure 7.**Box-and-whisker plot for selected intracellular rates of glycolysis and pentose phosphate pathway estimated from model simulations for mock-infected and infected suspension MDCK cells. (

**A**) Hexokinase, (

**B**) ribose-5-phosphate, (

**C**) enolase, and (

**D**) lactate dehydrogenase. Calculated from model simulations of the exponential growth phase of Cultivation 1 ( , 6–108 h), the death phase of Cultivation 1 ( , 146–169 h), the exponential growth phase of Cultivation 2 ( , 6–48 h) and the virus replication phase of Cultivation 2 ( , 49.9–107 h). The bar represents the median, the box is the first and third quartile, and the whisker the minimum and maximum of the rates from the model simulations of the corresponding cultivation phase.

**Figure 8.**Box-and-whisker plot for selected intracellular rates of citric acid cycle, glutaminolysis and transamination estimated from model simulations for mock-infected and infected suspension MDCK cells. (

**A**) pyruvate carboxylase, (

**B**) glutaminase, (

**C**) amino acid degradation, (

**D**) glutamate dehydrogenase, (

**E**) isocitrate dehydrogenase and (

**F**) aspartate transaminase. Calculated from model simulations of the exponential growth phase of Cultivation 1 ( , 6–108 h), the death phase of Cultivation 1 ( , 146–169 h), the exponential growth phase of Cultivation 2 ( , 6–48 h) and the virus replication phase of Cultivation 2 ( , 49.9–107 h). The bar represents the median, the box is the first and third quartile, and the whisker the minimum and maximum of the rates from the model simulations of the corresponding cultivation phase.

**Figure 9.**Simplified model of the central carbon and energy metabolism of MDCK cells, modified from [52] (changes to the previous model in orange). In green: metabolites and product measured experimentally; in grey: metabolites not measured. Ellipsoids: enzymes considered in the model. Arrows: reactions or transport, with the arrowhead indicating the reaction or transport direction (for simplification, reversible reactions have an arrow for both directions). Grey rectangles: sinks or metabolites not accounted for in the model. Red triangles: all the reactions included in the energy balance. Abbreviations of metabolites and product: 3PG: 3-phosphoglycerate, AcCoA: acetyl coenzyme A, ATP: adenosine tri-phosphate, cAc: cis-Aconitate, Cit: citrate, F16P: fructose 1,6-biphosphate, F6P: fructose-6-phosphate, Fum: fumarate, G6P: glucose-6-phosphate, Glc: glucose (intracellular), Glc

^{x}: glucose (extracellular), Gln: glutamine (intracellular), Gln

^{x}: glutamine (extracellular), Glu: glutamate (intracellular), Glu

^{x}: glutamate (extracellular), IsoCit: iso-citrate, Keto: alpha-ketoglutarate, Lac

^{x}: lactate (extracellular), Mal: malate, NH4: ammonium (intracellular), NH4

^{x}: ammonium (extracellular), OAA: oxaloacetate, PEP: phosphoenolpyruvate, Pyr: pyruvate (intracellular), Pyr

^{x}: pyruvate (extracellular), R5P: ribose-5-phosphate, SUC: succinate, UDPGlc: uridine diphosphate Glucose. Abbreviations of enzymes and transport rates: HK: hexokinase, G6PDH: glucose-6-phosphate dehydrogenase, UT: uridyl transferase, GLYS: glycogen synthetase, GPI: glucose-6-phosphate isomerase, TATKF6P: transaldolase and transketolase, TATK3PG: transaldolase and transketolase, PFK: phosphofructokinase, ALD: aldolase, ENO: Enolase, ${\mathrm{r}}_{\mathrm{CCM}}$: reaction rate with overall ATP production, r

_{dATP}: reaction rate with overall ATP consumption, PK: pyruvate kinase, PEPCK: phosphoenolpyruvate-kinase, LDH: lactate dehydrogenase, PC: pyruvate carboxylase, PDH: pyruvate dehydrogenase, AlaTA: alanine transaminase, ME: malic enzyme, CS: citrate synthetase, CL: citrate lyase, ACO: aconitase, ICDH: isocitrate dehydrogenase, KDH: ketoglutarate dehydrogenase, AspTA: aspartate transaminase, SDH: succinate dehydrogenase, FMA: fumarase, MDH: malate dehydrogenase, GLDH: glutamate dehydrogenase, GS: glutamine synthetase, GLNase: glutaminase, ${\mathrm{r}}_{\mathrm{AAex}}$: amino acids degradation, in the following reaction rates are listed as “reaction rate accounting for”: ${\mathrm{r}}_{\mathrm{dR}5\mathrm{P}}$: ribose-5-phosphate consumption, ${\mathrm{r}}_{dN\mathrm{H}4}$: ammonium consumption, ${\mathrm{r}}_{\mathrm{uGLC}}$: other uridine diphosphate glucose consumption, ${\mathrm{r}}_{\mathrm{GLUT}}$: extracellular glucose consumption, ${\mathrm{P}}_{y{r}_{\mathrm{trans}}^{\mathrm{x}}}$: extracellular pyruvate consumption, ${\mathrm{r}}_{\mathrm{NH}{4}_{\mathrm{trans}}^{\mathrm{x}}}$: extracellular ammonium production from intracellular rates, ${\mathrm{r}}_{{\mathrm{Gln}}_{\mathrm{trans}}^{\mathrm{x}}}$: extracellular glutamine consumption, ${\mathrm{r}}_{La{c}_{\mathrm{trans}}^{\mathrm{x}}}$: extracellular lactate production/consumption from intracellular rates and ${\mathrm{r}}_{{\mathrm{Glu}}_{\mathrm{trans}}^{\mathrm{x}}}$: extracellular glutamate production from intracellular rates.

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**MDPI and ACS Style**

Ramos, J.R.C.; Bissinger, T.; Genzel, Y.; Reichl, U.
Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model. *Metabolites* **2022**, *12*, 239.
https://doi.org/10.3390/metabo12030239

**AMA Style**

Ramos JRC, Bissinger T, Genzel Y, Reichl U.
Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model. *Metabolites*. 2022; 12(3):239.
https://doi.org/10.3390/metabo12030239

**Chicago/Turabian Style**

Ramos, João Rodrigues Correia, Thomas Bissinger, Yvonne Genzel, and Udo Reichl.
2022. "Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model" *Metabolites* 12, no. 3: 239.
https://doi.org/10.3390/metabo12030239