Systems biotechnology of animal cells: The road to prediction

Trends in Biotechnology

Volumn 30, Issue 7, 2012, Pages 377-385

  Carinhas, Nuno ^a,b   Oliveira, Rui ^c   Alves, Paula Marques ^a,b   Carrondo, Manuel J T ^a,b,c   Teixeira, Ana P ^a,b  

^a INSTITUTO DE TECNOLOGIA QUÍMICA E BIOLÓGICA   (Portugal)

^b INSTITUTO DE BIOLOGIA EXPERIMENTAL E TECNOLÓGICA   (Portugal)

^c UNIVERSIDADE NOVA DE LISBOA   (Portugal)

Author keywords

Animal cell culture; Bioprocess improvement; Computational prediction; Mathematical modeling; Recombinant protein; Systems biotechnology

Indexed keywords

ANIMAL CELLS; BIOPROCESS OPTIMIZATION; BIOPROCESSES; CELL STATE; CENTRAL SCAFFOLD; COMPUTATIONAL PREDICTIONS; DATA INTEGRATION; HOST CELLS; MANUFACTURING PROCESS; MATHEMATICAL MODELING; METABOLIC MODELS; SYSTEMS BIOTECHNOLOGY; TECHNOLOGICAL ADVANCES;

ANIMAL CELL CULTURE; FORECASTING; RECOMBINANT PROTEINS;

BIOTECHNOLOGY;

ANIMAL CELL; BIOPROCESS; BIOTECHNOLOGY; CATALYSIS; CELL CULTURE; GENE EXPRESSION; INFORMATION; METABOLISM; METABOLOMICS; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PROTEOMICS; REVIEW; STEADY STATE; TRANSCRIPTOMICS;

ANIMALS; BIOREACTORS; BIOTECHNOLOGY; CELLS, CULTURED; GENE EXPRESSION PROFILING; GENOMICS; SYSTEMS BIOLOGY; TECHNOLOGY, PHARMACEUTICAL;

ANIMALIA;

EID: 84862307869     PISSN: 01677799     EISSN: 18793096     Source Type: Journal    
DOI: 10.1016/j.tibtech.2012.03.004     Document Type: Review

References (90)

1. Butler M. Animal cell cultures: recent achievements and perspectives in the production of biopharmaceuticals. Appl. Microbiol. Biotechnol. 2005, 68:283-291.
2. Sommerfeld S., Strube J. Challenges in biotechnology production - generic process and process optimization for monoclonal antibodies. Chem. Eng. Process 2005, 44:1123-1137.
3. Walsh G. Biopharmaceutical benchmarks 2010. Nat. Biotechnol. 2010, 28:917-924.
4. Rathore A.S. Follow-on protein products: scientific issues, developments and challenges. Trends Biotechnol. 2009, 27:698-705.
5. Wurm F.M. Production of recombinant protein therapeutics in cultivated mammalian cells. Nat. Biotechnol. 2004, 22:1393-1398.
6. O'Callaghan P.M., James D.C. Systems biotechnology of mammalian cell factories. Brief. Funct. Genomics 2008, 7:95-110.
7. Yoon S.K., et al. Enhancing effect of low culture temperature on specific antibody productivity of recombinant Chinese hamster ovary cells: clonal variation. Biotechnol. Prog. 2004, 20:1683-1688.
8. Ozturk S.S., Palsson B.O. Effect of medium osmolarity on hybridoma growth, metabolism and antibody production. Biotechnol. Bioeng. 1991, 37:989-993.
9. Shen C.F., Kamen A. Hyperosmotic pressure on HEK 293 cells during the growth phase, but not the production phase, improves adenovirus production. J. Biotechnol. 2012, 157:228-236.
10. Carvalhal A.V., et al. Extracellular purine and pyrimidine catabolism in cell culture. Biotechnol. Prog. 2011, 27:1373-1382.
11. Rodriguez J., et al. Enhanced production of monomeric interferon-β by CHO cells through the control of culture conditions. Biotechnol. Prog. 2005, 21:22-30.
12. Jiang Z., Sharfstein S.T. Sodium butyrate stimulates monoclonal antibody over-expression in CHO cells by improving gene accessibility. Biotechnol. Bioeng. 2008, 100:189-194.
13. Gòdia F., Cairó J.J. Metabolic engineering of animal cells. Bioprocess Biosyst. Eng. 2002, 24:289-298.
14. Dinnis D.M., James D.C. Engineering mammalian cell factories for improved recombinant monoclonal antibody production: lessons from nature?. Biotechnol. Bioeng. 2005, 91:180-189.
15. Balcarcel R.R., Stephanopoulos G. Rapamycin reduces hybridoma cell death and enhances monoclonal antibody production. Biotechnol. Bioeng. 2001, 76:1-10.
16. Dreesen I.A.J., Fussenegger M. Ectopic expression of human mTOR increases viability, robustness, cell size, proliferation, and antibody production of Chinese hamster ovary cells. Biotechnol. Bioeng. 2011, 108:853-866.
17. Mulukutla B.C., et al. Glucose metabolism in mammalian cell culture: new insights for tweaking vintage pathways. Trends Biotechnol. 2010, 28:476-484.
18. Ideker T., Lauffenburger D. Building with a scaffold: emerging strategies for high- to low-level cellular modeling. Trends Biotechnol. 2003, 21:255-262.
19. Sauer U., et al. Getting closer to the whole picture. Science 2007, 316:550-551.
20. Stephanopoulos G., et al. Exploiting biological complexity for strain improvement through systems biology. Nat. Biotechnol. 2004, 22:1261-1267.
21. Otero J.M., Nielsen J. Industrial systems biology. Biotechnol. Bioeng. 2010, 105:439-460.
22. Gatti M.D.L., et al. Comparative transcriptional analysis of mouse hybridoma and recombinant Chinese hamster ovary cells undergoing butyrate treatment. J. Biosci. Bioeng. 2007, 103:82-91.
23. Baik J.Y., et al. Initial transcriptome and proteome analyses of low culture temperature-induced expression in CHO cells producing erythropoietin. Biotechnol. Bioeng. 2006, 93:361-371.
24. Kantardjieff A., et al. Transcriptome and proteome analysis of Chinese hamster ovary cells under low temperature and butyrate treatment. J. Biotechnol. 2010, 145:143-159.
25. Shen D., Sharfstein S.T. Genome-wide analysis of the transcriptional response of murine hybridomas to osmotic shock. Biotechnol. Bioeng. 2006, 93:132-145.
26. Korke R., et al. Large scale gene expression profiling of metabolic shift of mammalian cells in culture. J. Biotechnol. 2004, 107:1-17.
27. Hackl M., et al. Next-generation sequencing of the Chinese hamster ovary microRNA transcriptome: identification, annotation and profiling of microRNAs as targets for cellular engineering. J. Biotechnol. 2011, 153:62-75.
28. Bort J.A.H., et al. Dynamic mRNA and miRNA profiling of CHO-k1 suspension cell cultures. Biotechnol. J. 2011, 10.1002/biot.201100143.
29. Griffin T.J., et al. Advancing mammalian cell culture engineering using genome-scale technologies. Trends Biotechnol. 2007, 25:401-408.
30. Xu X., et al. The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line. Nat. Biotechnol. 2011, 29:735-741.
31. Wurm F., Hacker D. First CHO genome. Nat. Biotechnol. 2011, 29:718-720.
32. Lee M.S., et al. Proteome analysis of antibody-expressing CHO cells in response to hyperosmotic pressure. Biotechnol. Prog. 2003, 19:1734-1741.
33. van Dyk D.D., et al. Identification of cellular changes associated with increased production of human growth hormone in a recombinant Chinese hamster ovary cell line. Proteomics 2003, 3:147-156.
34. Seow T.K., et al. Proteomic investigation of metabolic shift in mammalian cell culture. Biotechnol. Prog. 2001, 17:1137-1144.
35. Vester D., et al. Quantitative analysis of cellular proteome alterations in human influenza A virus-infected mammalian cell lines. Proteomics 2009, 9:3316-3327.
36. van Anken E., et al. Sequential waves of functionally related proteins are expressed when B cells prepare for antibody secretion. Immunity 2003, 18:243-253.
37. Ong S.-E., Mann M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 2005, 1:252-262.
38. Zhu W., et al. Mass spectrometry-based label-free quantitative proteomics. J. Biomed. Biotechnol. 2010, 2010:840518.
39. Seth G., et al. Molecular portrait of high productivity in recombinant NS0 cells. Biotechnol. Bioeng. 2007, 97:933-951.
40. Wu W.W., et al. Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, using 2D gel- or LC-MALDI TOF/TOF. J. Proteom. Res. 2006, 5:651-658.
41. Carinhas N., et al. Quantitative proteomics of Spodoptera frugiperda cells during growth and baculovirus infection. PLoS ONE 2011, 6:e26444.
42. Beausoleil S.A., et al. Large-scale characterization of HeLa cell nuclear phosphoproteins. Proc. Natl. Acad. Sci. U.S.A. 2004, 101:12130-12135.
43. Kim S.C., et al. Substrate and functional diversity of lysine acetylation revealed by a proteomics survey. Mol. Cell 2006, 23:607-618.
44. Zielinska D.F., et al. Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell 2010, 141:897-907.
45. Cuperlovic-Culf M., et al. Cell culture metabolomics: applications and future directions. Drug Discov. Today 2010, 15:610-621.
46. Koek M.M., et al. Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives. Metabolomics 2011, 7:307-328.
47. Wishart D.S. Quantitative metabolomics using NMR. Trends Anal. Chem. 2008, 27:228-237.
48. Ritter J.B., et al. Metabolic effects of influenza virus infection in cultured animal cells: intra- and extracellular metabolite profiling. BMC Syst. Biol. 2010, 4:61.
49. Sellick C.A., et al. Metabolite profiling of recombinant CHO cells: designing tailored feeding regimes that enhance recombinant antibody production. Biotechnol. Bioeng. 2011, 108:3025-3031.
50. Khoo S.H.G., Al-Rubeai M. Metabolic characterization of a hyper-productive state in an antibody producing NS0 myeloma cell line. Metab. Eng. 2009, 11:199-211.
51. Kell D.B., et al. Metabolic footprinting and systems biology: the medium is the message. Nat. Rev. Microbiol. 2005, 3:557-565.
52. Lee J.M., et al. Flux balance analysis in the era of metabolomics. Brief. Bioinform. 2006, 7:140-150.
53. Seth G., et al. In pursuit of a super producer - alternative paths to high producing recombinant mammalian cells. Curr. Opin. Biotechnol. 2007, 18:557-564.
54. Janke R., et al. Measurement of key metabolic enzyme activities in mammalian cells using rapid and sensitive microplate-based assays. Biotechnol. Bioeng. 2010, 107:566-581.
55. Bernal V., et al. An integrated analysis of enzyme activities, cofactor pools and metabolic fluxes in baculoviurs-infected Spodoptera frugiperda Sf9 cells. J. Biotechnol. 2010, 150:332-342.
56. Wiechert W., Noack S. Mechanistic pathway modeling for industrial biotechnology: challenging but worthwhile. Curr. Opin. Biotechnol. 2011, 22:1-7.
57. Mashego M.R., et al. In vivo kinetics with rapid perturbation experiments in Saccharomyces cerevisiae using a second-generation BioScope. Metab. Eng. 2006, 8:370-383.
58. Kresnowati M.T.A.P., et al. Determination of elasticities, concentration and flux control coefficients from transient metabolite data using linlog kinetics. Metab. Eng. 2005, 7:142-153.
59. Ahn W.S., Antoniewicz M.R. Towards dynamic metabolic flux analysis in CHO cell cultures. Biotechnol. J. 2012, 7:61-74.
60. Stephanopoulos G., et al. Metabolic flux analysis. Metabolic Engineering. Principles and Methodologies 1998, Academic Press, pp. 313-351.
61. Quek L.-E., et al. Metabolic flux analysis in mammalian cell culture. Metab. Eng. 2010, 12:161-171.
62. Bernal V., et al. Cell density effect in the baculovirus-insect cells system: a quantitative analysis of energetic metabolism. Biotechnol. Bioeng. 2009, 104:162-180.
63. Carinhas N., et al. Improving baculovirus production at high cell density through manipulation of energy metabolism. Metab. Eng. 2010, 12:39-52.
64. Nolan R.P., Lee K. Dynamic model of CHO cell metabolism. Metab. Eng. 2011, 13:108-124.
65. Sauer U. High-throughput phenomics: experimental methods for mapping fluxomes. Curr. Opin. Biotechnol. 2004, 15:58-63.
66. 13C-lactate nuclear magnetic resonance spectroscopy and metabolite balancing. Biotechnol. Bioeng. 2001, 74:528-538.
67. 13C metabolic flux analysis. J. Biosci. Bioeng. 2011, 112:616-623.
68. Sengupta N., et al. Metabolic flux analysis of CHO cell metabolism in the late non-growth phase. Biotechnol. Bioeng. 2011, 108:82-92.
69. Nöh K., Wiechert W. The benefits of being transient: isotope-based metabolic flux analysis at the short time scale. Appl. Microbiol. Biotechnol. 2011, 91:1247-1265.
70. Ahn W.S., Antoniewicz M.R. Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry. Metab. Eng. 2011, 13:598-609.
71. Munger J., et al. Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy. Nat. Biotechnol. 2008, 26:1179-1186.
72. 13C labeling experiments at metabolic nonstationary conditions: an exploratory study. BMC Bioinform. 2008, 9:152.
73. Lewis N.E., et al. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 2012, 10:291-305.
74. Schellenberger J., et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox v2.0. Nat. Protoc. 2011, 6:1290-1307.
75. Blazeck J., Alper H. Systems metabolic engineering: genome-scale models and beyond. Biotechnol. J. 2010, 5:647-659.
76. Sheikh K., et al. Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus. Biotechnol. Prog. 2005, 21:112-121.
77. Duarte N.C., et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. U.S.A. 2007, 104:1777-1782.
78. Ma H., et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 2007, 3:135.
79. Akesson M., et al. Integration of gene expression data into genome-scale metabolic models. Metab. Eng. 2004, 6:285-293.
80. Cox S.J., et al. Genetically constrained metabolic flux analysis. Metab. Eng. 2005, 7:445-456.
81. Shlomi T., et al. Network-based prediction of human tissue-specific metabolism. Nat. Biotechnol. 2008, 26:1003-1010.
82. Covert M.W., et al. Integrated metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 2008, 24:2044-2050.
83. Lee J.M., et al. Dynamic analysis of integrated signalling, metabolic, and regulatory networks. PLoS Comput. Biol. 2008, 4:e1000086.
84. O'Callaghan P.M., et al. Cell line-specific control of recombinant monoclonal antibody production by CHO cells. Biotechnol. Bioeng. 2010, 106:938-951.
85. Roldão A., et al. Modeling rotavirus-like particles production in a baculovirus expression vector system: infection kinetics, baculovirus DNA replication, mRNA synthesis and protein production. J. Biotechnol. 2007, 128:875-894.
86. Clarke C., et al. Predicting cell-specific productivity from CHO gene expression. J. Biotechnol. 2011, 151:159-165.
87. Teixeira A.P., et al. Hybrid semi-parametric mathematical systems: bridging the gap between systems biology and process engineering. J. Biotechnol. 2007, 132:418-425.
88. Moxley J.F., et al. Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:6477-6482.
89. Carinhas N., et al. Hybrid metabolic flux analysis: combining stoichiometric and statistical constraints to model the formation of complex recombinant products. BMC Syst. Biol. 2011, 5:34.
90. Teixeira A.P., et al. Cell functional enviromics: unravelling the function of environmental factors. BMC Syst. Biol. 2011, 5:92.