Multivariate analysis of cell culture bioprocess data—Lactate consumption as process indicator

Journal of Biotechnology

Volumn 162, Issue 2-3, 2012, Pages 210-223

  Le, Huong ^a   Kabbur, Santosh ^a   Pollastrini, Luciano ^b   Sun, Ziran ^b   Mills, Keri ^b   Johnson, Kevin ^b   Karypis, George ^a   Hu, Wei Shou ^a  

^a University of Minnesota   (United States)

^b GENENTECH INC   (United States)

Author keywords

Bioprocess data mining; Cell culture; Chinese hamster ovary (CHO) cells; Lactate consumption; Multivariate data analysis; Partial least square regression; Support vector regression

Indexed keywords

CELL CULTURE; CELLS; DATA MINING; FORECASTING; HISTORY; LEAST SQUARES APPROXIMATIONS; METABOLISM; PATTERN RECOGNITION; PHYSIOLOGY; PRODUCTIVITY; REGRESSION ANALYSIS; STAGES;

BIOPROCESSES; CHINESE HAMSTER OVARY CELLS; LACTATE CONSUMPTION; MULTIVARIATE DATA ANALYSIS; PARTIAL LEAST SQUARE REGRESSION; SUPPORT VECTOR REGRESSION (SVR);

MULTIVARIANT ANALYSIS;

INDICATOR; LACTIC ACID; GLUCOSE;

ANIMAL CELL; ANTIBODY BLOOD LEVEL; ARTICLE; BIOPROCESS; CELL CULTURE; CELL GROWTH; CELL VIABILITY; CONTROLLED STUDY; GLUCOSE INTAKE; INOCULATION; METABOLISM; MULTIVARIATE ANALYSIS; NONHUMAN; PATTERN RECOGNITION; PREDICTION; PRIORITY JOURNAL; PRODUCTIVITY; SUPPORT VECTOR MACHINE; TIME SERIES ANALYSIS; ANIMAL; BIOLOGY; BIOTECHNOLOGY; CELL COUNT; CHO CELL; CRICETULUS; CULTURE MEDIUM; CULTURE TECHNIQUE; HAMSTER; METHODOLOGY; REGRESSION ANALYSIS;

ANIMALS; BIOTECHNOLOGY; CELL COUNT; CELL CULTURE TECHNIQUES; CHO CELLS; COMPUTATIONAL BIOLOGY; CRICETINAE; CRICETULUS; CULTURE MEDIA; GLUCOSE; LACTIC ACID; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

EID: 84868481008     PISSN: 01681656     EISSN: 18734863     Source Type: Journal    
DOI: 10.1016/j.jbiotec.2012.08.021     Document Type: Article

References (26)

1. Altamirano, C., Illanes, A., Becerra, S., Cairó J.J., Gòdia, F., Considerations on the lactate consumption by CHO cells in the presence of galactose. Journal of Biotechnology 125:4 (2006), 547–556.
2. Bachinger, T., Riese, U., Eriksson, R., Mandenius, C.-F., Monitoring cellular state transitions in a production-scale CHO-cell process using an electronic nose. Journal of Biotechnology 76:1 (2000), 61–71.
3. Buck, K.K.S., Subramanian, V., Block, D.E., Identification of critical batch operating parameters in fed-batch recombinant E. coli fermentations using decision tree analysis. Biotechnology Progress 18:6 (2002), 1366–1376.
4. Chang, C.-C., Lin, C.-J., LIBSVM: A Library for Support Vector Machines. 2001.
5. Charaniya, S., Hu, W., Karypis, G., Mining bioprocess data: opportunities and challenges. Trends in Biotechnology 26:12 (2008), 690–699.
6. Charaniya, S., Le, H., Rangwala, H., Mills, K., Johnson, K., Karypis, G., Hu, W.-S., Mining manufacturing data for discovery of high productivity process characteristics. Journal of Biotechnology 147:3–4 (2010), 186–197.
7. Chen, K., Liu, Q., Xie, L., Sharp, P.A., Wang, D.I.C., Engineering of a mammalian cell line for reduction of lactate formation and high monoclonal antibody production. Biotechnology and Bioengineering 72:1 (2001), 55–61.
8. Chong, I.-G, Jun, C.-H., Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems 78:1–2 (2005), 103–112.
9. Coleman, M.C., Block, D.E., Retrospective optimization of time-dependent fermentation control strategies using time-independent historical data. Biotechnology and Bioengineering 95:3 (2006), 412–423.
10. Cruz, H.J., Moreira, J.L., Carrondo, M.J.T., Metabolic shifts by nutrient manipulation in continuous cultures of BHK cells. Biotechnology and Bioengineering 66:2 (1999), 104–113.
11. de Jong, S., SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems 18:3 (1993), 251–263.
12. Glacken, M.W., Fleischaker, R.J., Sinskey, A.J., Reduction of waste product excretion via nutrient control: possible strategies for maximizing product and cell yields on serum in cultures of mammalian cells. Biotechnology and Bioengineering 28:9 (1986), 1376–1389.
13. Gunther, J.C., Conner, J.S., Seborg, D.E., Fault detection and diagnosis in an industrial fed-batch cell culture process. Biotechnology Progress 23:4 (2007), 851–857.
14. Hu, W., Dodge, T., Frame, K., Himes, V., Effect of glucose on the cultivation of mammalian cells. Developments in Biological Standardization 66 (1987), 279–290.
15. Irani, N., Wirth, M., van den Heuvel, J., Wagner, R., Improvement of the primary metabolism of cell cultures by introducing a new cytoplasmic pyruvate carboxylase reaction. Biotechnology and Bioengineering 66:4 (1999), 238–246.
16. Kim, S., Lee, G., Down-regulation of lactate dehydrogenase-A by siRNAs for reduced lactic acid formation of Chinese hamster ovary cells producing thrombopoietin. Applied Microbiology and Biotechnology 74:1 (2007), 152–159.
17. Kim, S., Lee, G., Functional expression of human pyruvate carboxylase for reduced lactic acid formation of Chinese hamster ovary cells (DG44). Applied Microbiology and Biotechnology 76:3 (2007), 659–665.
18. Kirdar, A.O., Green, K.D., Rathore, A.S., Application of multivariate data analysis for identification and successful resolution of a root cause for a bioprocessing application. Biotechnology Progress 24:3 (2008), 720–726.
19. Le, H., Castro-Melchor, M., Hakemeyer, C., Jung, C., Szperalski, B., Karypis, G., Hu, W.-S., Mining bioprocess data for discovery of key parameters influencing high productivity and quality. Proceedings of the 22nd Annual Meeting of the European Society for Animal Cell Technology (ESACT), Vienna, Austria, May 15–18, 2011, Springer.
20. Liu, H., Hiroshi, M., Feature Selection for Knowledge Discovery and Data Mining. 1998, Springer, Norwell 244 pp.
21. Mulukutla, B.C., Gramer, M., Hu, W.-S., On metabolic shift to lactate consumption in fed-batch culture of mammalian cells. Metabolic Engineering 14:2 (2012), 138–149.
22. Qian, Y., Khattak, S.F., Xing, Z., He, A., Kayne, P.S., Qian, N.-X., Pan, S.-H., Li, Z.J., Cell culture and gene transcription effects of copper sulfate on Chinese hamster ovary cells. Biotechnology Progress 27:4 (2011), 1190–1194.
23. Ündey, C., Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations. Journal of Biotechnology 108:1 (2004), 61–77.
24. Ündey, C., Ertunç S., Mistretta, T., Looze, B., Applied advanced process analytics in biopharmaceutical manufacturing: challenges and prospects in real-time monitoring and control. Journal of Process Control 20:9 (2010), 1009–1018.
25. Wlaschin, K., Hu, W., Engineering cell metabolism for high-density cell culture via manipulation of sugar transport. Journal of Biotechnology 131:2 (2007), 168–176.
26. Zhou, W., Chen, C.-C., Buckland, B., Aunins, J., Fed-batch culture of recombinant NS0 myeloma cells with high monoclonal antibody production. Biotechnology and Bioengineering 55:5 (1997), 783–792.