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• Abbate, T., Dewasme, L., Bogaerts, P., Wouwer, A.V., Adaptive flux variability analysis: a tool to deal with uncertainties. IFAC-PapersOnLine 52:1 (2019), 70–75.
• Bastin, G., Quantitative analysis of metabolic networks and design of minimal bioreaction models. Rev. Afr. Rech. Inf. Math. Appl., 9, 2016.
• Bogaerts, P., Gziri, K.M., Richelle, A., From MFA to FBA: defining linear constraints accounting for overflow metabolism in a macroscopic FBA-based dynamical model of cell cultures in bioreactor. J. Process Control 60 (2017), 34–47.
• Duarte, N.C., Becker, S.A., Jamshidi, N., Thiele, I., Mo, M.L., Vo, T.D., Srivas, R., Palsson, B.Ø., Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. 104:6 (2007), 1777–1782, 10.1073/pnas.0610772104.
• Fernandes, S., Robitaille, J., Bastin, G., Jolicoeur, M., Vande Wouwer, A., Application of dynamic metabolic flux convex analysis to CHO-DXB11 cell fed-batch cultures. IFAC-PapersOnLine 49:7 (2016), 466–471.
• Follstad, B.D., Balcarcel, R.R., Stephanopoulos, G., Wang, D.I., Metabolic flux analysis of hybridoma continuous culture steady state multiplicity. Biotechnol. Bioeng. 63:6 (1999), 675–683.
• Heath, C., Kiss, R., Cell culture process development: advances in process engineering. Biotechnol. Prog. 23:1 (2007), 46–51.
• Henry, O., Jolicoeur, M., Kamen, A., Unraveling the metabolism of HEK-293 cells using lactate isotopomer analysis. Bioprocess Biosyst. Eng. 34:3 (2011), 263–273.
• Henry, O., Perrier, M., Kamen, A., Metabolic flux analysis of HEK-293 cells in perfusion cultures for the production of adenoviral vectors. Metab. Eng. 7:5–6 (2005), 467–476.
• Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., Morishima, K., Kegg: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45:D1 (2016), D353–D361.
• Karengera, E., Durocher, Y., De Crescenzo, G., Henry, O., Combining metabolic and process engineering strategies to improve recombinant glycoprotein production and quality. Appl. Microbiol. Biotechnol. 101:21 (2017), 7837–7851, 10.1007/s00253-017-8513-0.
• Kauffman, K.J., Prakash, P., Edwards, J.S., Advances in flux balance analysis. Curr. Opin. Biotechnol. 14:5 (2003), 491–496.
• Klamt, S., Stelling, J., Two approaches for metabolic pathway analysis?. Trends Biotechnol. 21:2 (2003), 64–69.
• Leighty, R.W., Antoniewicz, M.R., Dynamic metabolic flux analysis (DMFA): a framework for determining fluxes at metabolic non-steady state. Metab. Eng. 13:6 (2011), 745–755.
• Llaneras, F., Picó, J., An interval approach for dealing with flux distributions and elementary modes activity patterns. J. Theor. Biol. 246:2 (2007), 290–308.
• Llaneras, F., Picó, J., Stoichiometric modelling of cell metabolism. J. Biosci. Bioeng. 105:1 (2008), 1–11.
• Llaneras, F., Sala, A., Picó, J., Dynamic estimations of metabolic fluxes with constraint-based models and possibility theory. J Process Control 22:10 (2012), 1946–1955.
• Mahadevan, R., Edwards, J.S., Doyle III, F.J., Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys. J. 83:3 (2002), 1331–1340.
• Mahadevan, R., Schilling, C., The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5:4 (2003), 264–276.
• Martinez, V., Gerdtzen, Z., 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. 12:2 (2010), 129–137.
• Papin, J.A., Stelling, J., Price, N.D., Klamt, S., Schuster, S., Palsson, B.O., Comparison of network-based pathway analysis methods. Trends Biotechnol. 22:8 (2004), 400–405.
• Pe'er, I., Felder, C.E., Man, O., Silman, I., Sussman, J.L., Beckmann, J.S., Proteomic signatures: amino acid and oligopeptide compositions differentiate among phyla. Proteins Struct. Funct. Bioinf. 54:1 (2004), 20–40.
• Petiot, E., Cuperlovic-Culf, M., Shen, C.F., Kamen, A., Influence of hek293 metabolism on the production of viral vectors and vaccine. Vaccine 33:44 (2015), 5974–5981, 10.1016/j.vaccine.2015.05.097 Vaccine Engineering.
• Price, N.D., Reed, J.L., Palsson, B.Ø., Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol., 2(11), 2004, 886.
• Provost, A., Bastin, G., Dynamic metabolic modelling under the balanced growth condition. J Process Control 14:7 (2004), 717–728.
• Quek, L.-E., Dietmair, S., Hanscho, M., Martínez, V.S., Borth, N., Nielsen, L.K., Reducing recon 2 for steady-state flux analysis of HEK cell culture. J. Biotechnol. 184 (2014), 172–178.
• Ravi, M., Paramesh, V., Kaviya, S., Anuradha, E., Solomon, F.P., 3d cell culture systems: advantages and applications. J. Cell. Physiol. 230:1 (2015), 16–26.
• Richelle, A., Gziri, K.M., Bogaerts, P., A methodology for building a macroscopic FBA-based dynamical simulator of cell cultures through flux variability analysis. Biochem. Eng. J. 114 (2016), 50–64.
• Stephanopoulos, G., Aristidou, A.A., Nielsen, J., Metabolic Engineering: Principles and Methodologies. 1998, Academic Press.
• Thiele, I., Palsson, B.Ø., A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc., 5(1), 2010, 93.
• Trinh, C.T., Wlaschin, A., Srienc, F., Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl. Microbiol. Biotechnol., 81(5), 2009, 813.
• Vercammen, D., Logist, F., Van Impe, J., Dynamic estimation of specific fluxes in metabolic networks using non-linear dynamic optimization. BMC Syst. Biol., 8(1), 2014, 132.
• Zamorano, F., Vande Wouwer, A., Jungers, R.M., Bastin, G., Dynamic metabolic models of CHO cell cultures through minimal sets of elementary flux modes. J. Biotechnol. 164:3 (2013), 409–422.