Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris

Mariana L. Fazenda, Joao M L Dias, Linda M. Harvey, Alison Nordon, Ruangelie Edrada-Ebel

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Background: Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles.
Results: This is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R2, above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements.
Conclusions: The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.
LanguageEnglish
Pages51-59
Number of pages9
JournalMicrobial Cell Factories
Volume12
Issue number1
DOIs
Publication statusPublished - 21 May 2013

Fingerprint

Metabolic Flux Analysis
Pichia
Near-Infrared Spectroscopy
Industrial plants
Fluxes
Near infrared spectroscopy
Bioreactors
Biomass
Glycerol
Metabolism
Least-Squares Analysis
Recombinant Proteins
Industry
Recombinant proteins
Carbon
Multivariate Analysis
Manufacturing and Industrial Facilities
Process monitoring
Technology
Metabolites

Keywords

  • realtime flux modelling
  • pichia
  • industrial cell factory
  • real-time metabolic flux analysis
  • pichia pastoris

Cite this

@article{0a1a47c5f24f4dfcad60d90369fa8385,
title = "Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris",
abstract = "Background: Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles.Results: This is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R2, above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements.Conclusions: The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.",
keywords = "realtime flux modelling, pichia, industrial cell factory, real-time metabolic flux analysis, pichia pastoris",
author = "Fazenda, {Mariana L.} and Dias, {Joao M L} and Harvey, {Linda M.} and Alison Nordon and Ruangelie Edrada-Ebel",
note = "1. Walsh G: Biopharmaceutical benchmarks 2010. Nat Biotechnol 2010, 28:917–924. 2. Suresh P, Basu PK: Improving Pharmaceutical Product Development and Manufacturing: Impact on Cost of Drug Development and Cost of Goods Sold of Pharmaceuticals. J Pharm Innov 2008, 3:175–187. 3. Henriques JG, Buziol S, Stocker E, Voogd A, Menezes JC: Monitoring Mammalian cell cultivations for monoclonal antibody production using near-infrared spectroscopy. Adv Biochem Eng Biotechnol 2010, 116:73–97. 4. Niklas J, Schneider K, Heinzle E: Metabolic flux analysis in eukaryotes. Curr Opin Biotechnol 2010, 21:63–69. 5. Llaneras F, Pic{\'o} J: Stoichiometric modelling of cell metabolism. J Biosci Bioeng 2008, 105:1–11. 6. Wiechert W, Wiechert W: 13C Metabolic Flux Analysis. Metab Eng 2001, 3:195–206. 7. Antoniewicz MR, Kraynie DF, Laffend LA, Gonzalez-Lergier J, Kelleher JK, Stephanopoulos G: Metabolic flux analysis in a nonstationary system: Fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol. Metab Eng 2007, 9:277–292. 8. Carinhas N, Bernal V, Monteiro F, Carrondo MJT, Oliveira R, Alves PM: Improving baculovirus production at high cell density through manipulation of energy metabolism. Metab Eng 2010, 12:39–52. 9. Bernal V, Carinhas N, Yokomizo AY, Carrondo MJT, Alves PM: Cell density effect in the baculovirus-insect cells system: a quantitative analysis of energetic metabolism. Biotechnol Bioeng 2009, 104:162–180. 10. Metallo CM, Walther JL, Stephanopoulos G: Evaluation of C-13 isotopic tracers for metabolic flux analysis in mammalian cells. J Biotechnol 2009, 144:167–174. 11. Matsuoka Y, Shimizu K: The relationships between the metabolic fluxes and 13Clabeled isotopomer distribution for the flux analysis of the main metabolic pathways. Biochem Eng J 2010, 49:326–336. 12. Goudar C, Biener R, Zhang C, Michaels J, Piret J, Konstantinov K: Towards industrial application of quasi real-time metabolic flux analysis for mammalian cell culture. Cell Culture Eng 2006, 101:99–118. 13. Almeida JRM, Bertilsson M, Hahn-Hagerdal B, Liden G, Gorwa-Grauslund MF: Carbon fluxes of xylose-consuming Saccharomyces cerevisiae strains are affected differently by NADH and NADPH usage in HMF reduction. Appl Microbiol Biotechnol 2009, 84:751– 761. 14. Otero JM, Nielsen J: Industrial Systems Biology. Biotechnol Bioeng 2010, 105:439– 460. 15. US Department of Health and Human Services, Food and Drug Administration: PAT Guidance for Industry: A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. Washington, DC: FDA; 2004. 16. Scarff M, Arnold SA, Harvey LM, McNeil B: Near Infrared Spectroscopy for bioprocess monitoring and control: Current status and future trends. Crit Rev Biotechnol 2006, 26:17–39. 17. Arnold SA, Harvey LM, McNeil B, Hall JW: Employing near-infrared spectroscopic methods and analysis for fermentation monitoring and control - Part 1, method development. Biopharm Int-Appl Technol Biopharm Dev 2002, 15:26–34. 18. Arnold SA, Gaensakoo R, Harvey LM, McNeil B: Use of at-line and in-situ nearinfrared spectroscopy to monitor biomass in an industrial fed-batch Escherichia coli process. Biotechnol Bioeng 2002, 80:405–413. 19. Macauley-Patrick S, Fazenda ML, McNeil B, Harvey LM: Heterologous protein production using the Pichia pastoris expression system. Yeast 2005, 22:249–270. 20. Potvin G, Ahmad A, Zhang Z: Bioprocess engineering aspects of heterologous protein production in Pichia pastoris: a review. Biochem Eng J 2010, 34:91–105. 21. Workman J, Weyer L: Practical Guide to Interpretive Near-Infrared Spectroscopy. Boca Raton, FL: CRC Press; 2008. 22. Vaidyanathan S, Harvey LM, McNeil B: Deconvolution of near-infrared spectral information for monitoring mycelial biomass and other key analytes in a submerged fungal bioprocess. Anal Chim Acta 2001, 428:41–59. 23. Workman JJJ: NIRS Spectroscopy Calibration Basics. In Handbook of Near-Infrared Analysis. 3rd edition. Edited by Burns DA, Ciurczak EW. Boca aton, Fl: CRC Press; 2008. 24. Shenk J, Westerhaus M: Calibration the ISI way. In Near Infrared Spectroscopy: The Future Waves. Edited by Williams DAMCPC. Chichester, U.K: NIR Publications; 1996:198– 202. 25. Rhiel M, Cohen MB, Murhammer DW, Arnold MA: Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media. Biotechnol Bioeng 2002, 77:73–82. 26. Rhiel MH, Amrhein MI, Marison IW, von Stockar U: The influence of correlated calibration samples on the prediction performance of multivariate models based on mid-infrared spectra of animal cell cultures. Anal Chem 2002, 74:5227–5236. 27. Arnold SA, Crowley J, Vaidyanathan S, Matheson L, Mohan P, Hall JW, Harvey LM, McNeil B: At-line monitoring of a submerged filamentous bacterial cultivation using near-infrared spectroscopy. Enzyme Microb Technol 2000, 27:691–697. 28. Crowley J, Arnold SA, Wood N, Harvey LM, McNeil B: Monitoring a high cell density recombinant Pichia pastoris fed-batch bioprocess using transmission and reflectance near infrared spectroscopy. Enzyme Microb Technol 2005, 36:621–628. 29. Tosi S, Rossi M, Tamburini E, Vaccari G, Amaretti A, Matteuzzi D: Assessment of inline near-infrared spectroscopy for continuous monitoring of fermentation processes. Biotechnol Prog 2003, 19:1861–1821. 30. Holm-Nielsen JB, Lomborg CJ, Oleskowicz-Popiel P, Esbensen KH: On-line near infrared monitoring of glycerol-boosted anaerobic digestion processes: Evaluation of process analytical technologies. Biotechnol Bioeng 2008, 99:302–313. 31. Clementschitsch F, Bayer K: Improvement of bioprocess monitoring: development of novel concepts. Microb Cell Factories 2006, 5:19–31. 32. Roychoudhury P, Harvey L, McNeil B: The potential of mid infrared spectroscopy (MIRS) for real time bioprocess monitoring. Anal Chim Acta 2006, 571:159–166. 33. Ashton L, Johannessen C, Goodacre R: The importance of protonation in the investigation of protein phosphorylation using Raman spectroscopy and Raman optical activity. Anal Chem 2011, 83:7978–7983. 34. Kiviharju K, Salonen K, Moilanen U, Eerik{\"a}inen T: Biomass measurement online: the performance of in situ measurements and software sensors. J Ind Microbiol Biotechnol 2008, 35:657–665. 35. Cervera AE, Petersen N, Lantz AE, Larsen A, Gernaey KV: Application of nearinfrared spectroscopy for monitoring and control of cell culture and fermentation. Biotechnol Prog 2009:1561–1581. 36. Tortajada M, Llaneras F, Pic{\'o} J: Validation of a constraint-based model of Pichia pastoris metabolism under data scarcity. BMC Syst Biol 2010, 4:115–125. 37. Carnicer M, Baumann K, T{\"o}plitz I, S{\'a}nchez-Ferrando F, Mattanovich D, Ferrer P, Albiol J: Macromolecular and elemental composition analysis and extracellular metabolite balances of Pichia pastoris growing at different oxygen levels. Microbial Cell Factories 2009, 8:65–78. 38. Sola A, Jouhten P, Maaheimo H, Sanchez-Ferrando F, Szyperski T, Ferrer P: Metabolic flux profiling of Pichia pastoris grown on glycerol/methanol mixtures in chemostat cultures at low and high dilution rates. Microbiology 2007, 153:281–290. 39. Brereton RG: Applied Chemometrics for Scientists. Chichester, England: Wiley; 2007. 40. Baumann K, Carnicer M, Dragosits M, Graf AB, Stadlmann J, Jouhten P, Maaheimo H, Gasser B, Albiol J, Mattanovich D, Ferrer P: A multi-level study of recombinant Pichia pastoris in different oxygen conditions. BMC Syst Biol 2010, 4:141. 41. Heyland J, Fu J, Blank LM, Schmid A: Carbon metabolism limits recombinant protein production in Pichia pastoris. Biotechnol Bioeng 2011, 108:1942–1953. 42. Heyland J, Fu JA, Blank LM, Schmid A: Quantitative physiology of Pichia pastoris during glucose-limited high-cell density fed-batch cultivation for recombinant protein production. Biotechnol Bioeng 2010, 107:357–368. 43. Blank L, Lehmbeck F, Sauer U: Metabolic-flux and network analysis in fourteen hemiascomycetous yeasts. FEMS Yeast Res 2005, 5:545–558. 44. Sola A, Maaheimo H, Ylonen K, Ferrer P, Szyperski T: Amino acid biosynthesis and metabolic flux profiling of Pichia pastoris. Eur J Biochem 2004, 271:2462–2470. 45. Lin Y-H, Bayrock D, Ingledew WM: Metabolic flux variation of Saccharomyces cerevisiae cultivated in a multistage continuous stirred tank reactor fermentation environment. Biotechnol Prog 2001, 17:1055–1060. 46. Finn B, Harvey LM, McNeil B: The effect of dilution rate upon protein content and cellular amino acid profiles in chemostat cultures of Saccharomyces cerevisiae. Int J Food Eng 2010, 6:1–19. 47. Martens H, N{\ae}s T: Multivariate calibration. UK: Chichester; 1989. 48. Klamt S, Stelling J, Ginkel M, Dieter E: FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps. Bioinformatics 2003, 19:261–269. 49. Stephanopoulos G, Aristidou A, Nielsen J: Metabolic engineering: principles and methodologies. San Diego: Academic Press; 1998. 50. Nyberg GB, Balcarcel RR, Follstad BD, Stephanopoulos G, Wang DIC: Metabolism of peptide amino acids by Chinese hamster ovary cells grown in a complex medium. Biotechnol Bioeng Symp 1999, 62:324–335. 51. Ren HT, Yuan JQ, Bellgardt KH: Macrokinetic model for methylotrophic Pichia pastoris based on stoichiometric balance. J Biotechnol 2003, 106:53–68. 52. Lequeux G, Johansson L: MFA for overdetermined systems reviewed and compared with RNA expression data to elucidate the difference in shikimate yield between carbon- and phosphate-limited continuous cultures of E. coli W3110.shik1. Biotechnol Prog 2006, 22:1056–1070",
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month = "5",
day = "21",
doi = "10.1186/1475-2859-12-51",
language = "English",
volume = "12",
pages = "51--59",
journal = "Microbial Cell Factories",
issn = "1475-2859",
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Towards better understanding of an industrial cell factory : investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris. / Fazenda, Mariana L.; Dias, Joao M L; Harvey, Linda M.; Nordon, Alison; Edrada-Ebel, Ruangelie.

In: Microbial Cell Factories, Vol. 12, No. 1, 21.05.2013, p. 51-59.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Towards better understanding of an industrial cell factory

T2 - Microbial Cell Factories

AU - Fazenda, Mariana L.

AU - Dias, Joao M L

AU - Harvey, Linda M.

AU - Nordon, Alison

AU - Edrada-Ebel, Ruangelie

N1 - 1. Walsh G: Biopharmaceutical benchmarks 2010. Nat Biotechnol 2010, 28:917–924. 2. Suresh P, Basu PK: Improving Pharmaceutical Product Development and Manufacturing: Impact on Cost of Drug Development and Cost of Goods Sold of Pharmaceuticals. J Pharm Innov 2008, 3:175–187. 3. Henriques JG, Buziol S, Stocker E, Voogd A, Menezes JC: Monitoring Mammalian cell cultivations for monoclonal antibody production using near-infrared spectroscopy. Adv Biochem Eng Biotechnol 2010, 116:73–97. 4. Niklas J, Schneider K, Heinzle E: Metabolic flux analysis in eukaryotes. Curr Opin Biotechnol 2010, 21:63–69. 5. Llaneras F, Picó J: Stoichiometric modelling of cell metabolism. J Biosci Bioeng 2008, 105:1–11. 6. Wiechert W, Wiechert W: 13C Metabolic Flux Analysis. Metab Eng 2001, 3:195–206. 7. Antoniewicz MR, Kraynie DF, Laffend LA, Gonzalez-Lergier J, Kelleher JK, Stephanopoulos G: Metabolic flux analysis in a nonstationary system: Fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol. Metab Eng 2007, 9:277–292. 8. Carinhas N, Bernal V, Monteiro F, Carrondo MJT, Oliveira R, Alves PM: Improving baculovirus production at high cell density through manipulation of energy metabolism. Metab Eng 2010, 12:39–52. 9. Bernal V, Carinhas N, Yokomizo AY, Carrondo MJT, Alves PM: Cell density effect in the baculovirus-insect cells system: a quantitative analysis of energetic metabolism. Biotechnol Bioeng 2009, 104:162–180. 10. Metallo CM, Walther JL, Stephanopoulos G: Evaluation of C-13 isotopic tracers for metabolic flux analysis in mammalian cells. J Biotechnol 2009, 144:167–174. 11. Matsuoka Y, Shimizu K: The relationships between the metabolic fluxes and 13Clabeled isotopomer distribution for the flux analysis of the main metabolic pathways. Biochem Eng J 2010, 49:326–336. 12. Goudar C, Biener R, Zhang C, Michaels J, Piret J, Konstantinov K: Towards industrial application of quasi real-time metabolic flux analysis for mammalian cell culture. Cell Culture Eng 2006, 101:99–118. 13. Almeida JRM, Bertilsson M, Hahn-Hagerdal B, Liden G, Gorwa-Grauslund MF: Carbon fluxes of xylose-consuming Saccharomyces cerevisiae strains are affected differently by NADH and NADPH usage in HMF reduction. Appl Microbiol Biotechnol 2009, 84:751– 761. 14. Otero JM, Nielsen J: Industrial Systems Biology. Biotechnol Bioeng 2010, 105:439– 460. 15. US Department of Health and Human Services, Food and Drug Administration: PAT Guidance for Industry: A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. Washington, DC: FDA; 2004. 16. Scarff M, Arnold SA, Harvey LM, McNeil B: Near Infrared Spectroscopy for bioprocess monitoring and control: Current status and future trends. Crit Rev Biotechnol 2006, 26:17–39. 17. Arnold SA, Harvey LM, McNeil B, Hall JW: Employing near-infrared spectroscopic methods and analysis for fermentation monitoring and control - Part 1, method development. Biopharm Int-Appl Technol Biopharm Dev 2002, 15:26–34. 18. Arnold SA, Gaensakoo R, Harvey LM, McNeil B: Use of at-line and in-situ nearinfrared spectroscopy to monitor biomass in an industrial fed-batch Escherichia coli process. Biotechnol Bioeng 2002, 80:405–413. 19. Macauley-Patrick S, Fazenda ML, McNeil B, Harvey LM: Heterologous protein production using the Pichia pastoris expression system. Yeast 2005, 22:249–270. 20. Potvin G, Ahmad A, Zhang Z: Bioprocess engineering aspects of heterologous protein production in Pichia pastoris: a review. Biochem Eng J 2010, 34:91–105. 21. Workman J, Weyer L: Practical Guide to Interpretive Near-Infrared Spectroscopy. Boca Raton, FL: CRC Press; 2008. 22. Vaidyanathan S, Harvey LM, McNeil B: Deconvolution of near-infrared spectral information for monitoring mycelial biomass and other key analytes in a submerged fungal bioprocess. Anal Chim Acta 2001, 428:41–59. 23. Workman JJJ: NIRS Spectroscopy Calibration Basics. In Handbook of Near-Infrared Analysis. 3rd edition. Edited by Burns DA, Ciurczak EW. Boca aton, Fl: CRC Press; 2008. 24. Shenk J, Westerhaus M: Calibration the ISI way. In Near Infrared Spectroscopy: The Future Waves. Edited by Williams DAMCPC. Chichester, U.K: NIR Publications; 1996:198– 202. 25. Rhiel M, Cohen MB, Murhammer DW, Arnold MA: Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media. Biotechnol Bioeng 2002, 77:73–82. 26. Rhiel MH, Amrhein MI, Marison IW, von Stockar U: The influence of correlated calibration samples on the prediction performance of multivariate models based on mid-infrared spectra of animal cell cultures. Anal Chem 2002, 74:5227–5236. 27. Arnold SA, Crowley J, Vaidyanathan S, Matheson L, Mohan P, Hall JW, Harvey LM, McNeil B: At-line monitoring of a submerged filamentous bacterial cultivation using near-infrared spectroscopy. Enzyme Microb Technol 2000, 27:691–697. 28. Crowley J, Arnold SA, Wood N, Harvey LM, McNeil B: Monitoring a high cell density recombinant Pichia pastoris fed-batch bioprocess using transmission and reflectance near infrared spectroscopy. Enzyme Microb Technol 2005, 36:621–628. 29. Tosi S, Rossi M, Tamburini E, Vaccari G, Amaretti A, Matteuzzi D: Assessment of inline near-infrared spectroscopy for continuous monitoring of fermentation processes. Biotechnol Prog 2003, 19:1861–1821. 30. Holm-Nielsen JB, Lomborg CJ, Oleskowicz-Popiel P, Esbensen KH: On-line near infrared monitoring of glycerol-boosted anaerobic digestion processes: Evaluation of process analytical technologies. Biotechnol Bioeng 2008, 99:302–313. 31. Clementschitsch F, Bayer K: Improvement of bioprocess monitoring: development of novel concepts. Microb Cell Factories 2006, 5:19–31. 32. Roychoudhury P, Harvey L, McNeil B: The potential of mid infrared spectroscopy (MIRS) for real time bioprocess monitoring. Anal Chim Acta 2006, 571:159–166. 33. Ashton L, Johannessen C, Goodacre R: The importance of protonation in the investigation of protein phosphorylation using Raman spectroscopy and Raman optical activity. Anal Chem 2011, 83:7978–7983. 34. Kiviharju K, Salonen K, Moilanen U, Eerikäinen T: Biomass measurement online: the performance of in situ measurements and software sensors. J Ind Microbiol Biotechnol 2008, 35:657–665. 35. Cervera AE, Petersen N, Lantz AE, Larsen A, Gernaey KV: Application of nearinfrared spectroscopy for monitoring and control of cell culture and fermentation. Biotechnol Prog 2009:1561–1581. 36. Tortajada M, Llaneras F, Picó J: Validation of a constraint-based model of Pichia pastoris metabolism under data scarcity. BMC Syst Biol 2010, 4:115–125. 37. Carnicer M, Baumann K, Töplitz I, Sánchez-Ferrando F, Mattanovich D, Ferrer P, Albiol J: Macromolecular and elemental composition analysis and extracellular metabolite balances of Pichia pastoris growing at different oxygen levels. Microbial Cell Factories 2009, 8:65–78. 38. Sola A, Jouhten P, Maaheimo H, Sanchez-Ferrando F, Szyperski T, Ferrer P: Metabolic flux profiling of Pichia pastoris grown on glycerol/methanol mixtures in chemostat cultures at low and high dilution rates. Microbiology 2007, 153:281–290. 39. Brereton RG: Applied Chemometrics for Scientists. Chichester, England: Wiley; 2007. 40. Baumann K, Carnicer M, Dragosits M, Graf AB, Stadlmann J, Jouhten P, Maaheimo H, Gasser B, Albiol J, Mattanovich D, Ferrer P: A multi-level study of recombinant Pichia pastoris in different oxygen conditions. BMC Syst Biol 2010, 4:141. 41. Heyland J, Fu J, Blank LM, Schmid A: Carbon metabolism limits recombinant protein production in Pichia pastoris. Biotechnol Bioeng 2011, 108:1942–1953. 42. Heyland J, Fu JA, Blank LM, Schmid A: Quantitative physiology of Pichia pastoris during glucose-limited high-cell density fed-batch cultivation for recombinant protein production. Biotechnol Bioeng 2010, 107:357–368. 43. Blank L, Lehmbeck F, Sauer U: Metabolic-flux and network analysis in fourteen hemiascomycetous yeasts. FEMS Yeast Res 2005, 5:545–558. 44. Sola A, Maaheimo H, Ylonen K, Ferrer P, Szyperski T: Amino acid biosynthesis and metabolic flux profiling of Pichia pastoris. Eur J Biochem 2004, 271:2462–2470. 45. Lin Y-H, Bayrock D, Ingledew WM: Metabolic flux variation of Saccharomyces cerevisiae cultivated in a multistage continuous stirred tank reactor fermentation environment. Biotechnol Prog 2001, 17:1055–1060. 46. Finn B, Harvey LM, McNeil B: The effect of dilution rate upon protein content and cellular amino acid profiles in chemostat cultures of Saccharomyces cerevisiae. Int J Food Eng 2010, 6:1–19. 47. Martens H, Næs T: Multivariate calibration. UK: Chichester; 1989. 48. Klamt S, Stelling J, Ginkel M, Dieter E: FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps. Bioinformatics 2003, 19:261–269. 49. Stephanopoulos G, Aristidou A, Nielsen J: Metabolic engineering: principles and methodologies. San Diego: Academic Press; 1998. 50. Nyberg GB, Balcarcel RR, Follstad BD, Stephanopoulos G, Wang DIC: Metabolism of peptide amino acids by Chinese hamster ovary cells grown in a complex medium. Biotechnol Bioeng Symp 1999, 62:324–335. 51. Ren HT, Yuan JQ, Bellgardt KH: Macrokinetic model for methylotrophic Pichia pastoris based on stoichiometric balance. J Biotechnol 2003, 106:53–68. 52. Lequeux G, Johansson L: MFA for overdetermined systems reviewed and compared with RNA expression data to elucidate the difference in shikimate yield between carbon- and phosphate-limited continuous cultures of E. coli W3110.shik1. Biotechnol Prog 2006, 22:1056–1070

PY - 2013/5/21

Y1 - 2013/5/21

N2 - Background: Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles.Results: This is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R2, above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements.Conclusions: The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.

AB - Background: Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles.Results: This is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R2, above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements.Conclusions: The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.

KW - realtime flux modelling

KW - pichia

KW - industrial cell factory

KW - real-time metabolic flux analysis

KW - pichia pastoris

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EP - 59

JO - Microbial Cell Factories

JF - Microbial Cell Factories

SN - 1475-2859

IS - 1

ER -