Advertisement

Model-Based Design of Superior Cell Factory: An Illustrative Example of Penicillium chrysogenum

  • I. Emrah Nikerel
  • Peter J. T. Verheijen
  • Walter M. van Gulik
  • Joseph J. Heijnen
Chapter

Abstract

A dynamic model for metabolic reaction network of Penicillium chrysogenum, coupling the central metabolism to growth, product formation and storage pathways is presented. In constructing the model, we started from an existing stoichiometric model, and systematically reduced this initial model to a one compartment model and further eliminated unidentifiabilities due to time scales. Kinetic analysis focuses on a time scale of seconds, thereby neglecting biosynthesis of new enzymes. We used linlog kinetics in representing the kinetic rate equations of each individual reaction. The final parameterization is performed for the final reduced model using previously published short term glucose perturbation data. The constructed model is a self-contained model in the sense that it can also predict the cofactor dynamics. Using the model, we calculated the Metabolic Control Analysis (MCA) parameters and found that the interplay among the growth, product formation and production of storage materials is strongly governed by the energy budget in the cell, which is in agreement with the previous findings. The model predictions and experimental observations agree reasonably well for most of the metabolites.

Keywords

Model-based design Penicillium chrysogenum Dynamic model Metabolic network Cell factory Linlog kinetics Parameterization Metabolic Control Analysis (MCA) Model prediction Time-scale analysis Elasticities Kinetic model P/O ratio Post-genomic Genome-scale Stimulus response experiments Metabolic flux Stoichiometry Network reconstruction Compartments Pseudo equilibrium Pseudo steady-state 

References

  1. 1.
    Canelas A, van Gulik WM, Heijnen JJ (2008) Determination of the cytosolic free NAD/NADH ratio in Saccharomyces cerevisiae under steady-state and highly dynamic conditions. Biotechnol Bioeng 100(4):734–743PubMedCrossRefGoogle Scholar
  2. 2.
    Chassagnole C, Noisommit-Rizzi N, Schmid JW, Mauch K, Reuss M (2002) Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol Bioeng 79(1):53–73PubMedCrossRefGoogle Scholar
  3. 3.
    Christensen B, Nielsen J (2000) Metabolic network analysis of Penicillium chrysogenum using 13 C-labeled glucose. Biotechnol Bioeng 68(6):652–659PubMedCrossRefGoogle Scholar
  4. 4.
    Demin O, Goryanin I (2008) Kinetic modelling in systems biology, Chapman & hall/CRC mathematical & computational biology. Taylor & Francis Ltd, HobokenGoogle Scholar
  5. 5.
    Duarte NC, Herrgard MJ, Palsson BO (2004) Reconstruction and validation of Saccharomyces cerevisiaei ND750, a fully compartmentalized genome-scale metabolic model. Genome Res 14(7):1298–1309PubMedCrossRefGoogle Scholar
  6. 6.
    Galazzo JL, Bailey J (1990) Fermentation pathway kinetics and metabolic flux control in suspended and immobilized Saccharomyces cerevisiae. Enzyme Microb Technol 12(3):162–172CrossRefGoogle Scholar
  7. 7.
    Heijnen JJ (2005) Approximative kinetic formats used in metabolic network modeling. Biotechnol Bioeng 91(5):534–545PubMedCrossRefGoogle Scholar
  8. 8.
    Heinrich R, Schuster S (1996) The regulation of cellular systems. SpringerCrossRefGoogle Scholar
  9. 9.
    Hynne F, Danø S, Sørensen P (2001) Full-scale model of glycolysis in Saccharomyces cerevisiae. Biophys Chem 94(1–2):121–163PubMedCrossRefGoogle Scholar
  10. 10.
    Jørgensen H, Nielsen J, Villadsen J, Mollgaard H (2004) Metabolic flux distributions in Penicillium chrysogenum during fed-batch cultivations. Biotechnol Bioeng 42(2):117–131Google Scholar
  11. 11.
    Kell DB (2004) Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol 7(3):296–307PubMedCrossRefGoogle Scholar
  12. 12.
    Kitano H (2002) Computational systems biology. Nature 420(6912):206–210PubMedCrossRefGoogle Scholar
  13. 13.
    Kitano H (2002) Systems biology: a brief overview. Science 295(5560):1662–1664PubMedCrossRefGoogle Scholar
  14. 14.
    Kleijn RJ, Liu F, van Winden WA, van Gulik WM, Ras C, Heijnen JJ (2007) Cytosolic NADPH metabolism in penicillin-G producing and non-producing chemostat cultures of Penicillium chrysogenum. Metab Eng 9(1):112–123PubMedCrossRefGoogle Scholar
  15. 15.
    Kofahl B, Klipp E (2004) Modelling the dynamics of the yeast pheromone pathway. Yeast 21(10):831–850PubMedCrossRefGoogle Scholar
  16. 16.
    Liebermeister W, Klipp E (2006) Bringing metabolic networks to life: convenience rate law and thermodynamic constraints. Theor Biol Med Model 3:41PubMedCrossRefGoogle Scholar
  17. 17.
    Müller WH, van der Krift TP, Krouwer AJ, Wüsten HA, van der Voort LH, Smaal EB, Verkleij AJ (1991) Localization of the pathway of the penicillin biosynthesis in Penicillium chrysogenum. EMBO J 10(2):489–495PubMedGoogle Scholar
  18. 18.
    Nasution U, van Gulik WM, Proell A, van Winden WA, Heijnen JJ (2006) Generating short-term kinetic responses of primary metabolism of Penicillium chrysogenum through glucose perturbation in the bioscope mini reactor. Metab Eng 8(5):395–405PubMedCrossRefGoogle Scholar
  19. 19.
    Nikerel İE, van Winden WA, van Gulik WM, Heijnen JJ (2006) A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics. BMC Bioinformatics 7(540)Google Scholar
  20. 20.
    Nikerel İE, van Winden WA, Verheijen PJT, Heijnen JJ (2009) Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics. Metab Eng 11:20–30PubMedCrossRefGoogle Scholar
  21. 21.
    Palsson BO, Schellenberger J, Park JO, Conrad TM (2010) BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11:213PubMedCrossRefGoogle Scholar
  22. 22.
    Rizzi M, Baltes M, Theobald U, Reuss M (1997) In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: II. Mathematical model. Biotechnol Bioeng 55(4):592–608PubMedCrossRefGoogle Scholar
  23. 23.
    Schomburg I, Chang A, Schomburg D (2002) BRENDA, enzyme data and metabolic information. Nucleic Acids Res 30:47–49PubMedCrossRefGoogle Scholar
  24. 24.
    Serrano L, Di Ventura B, Lemerle C, Michalodimitrakis K (2006) From in vivo to in silico biology and back. Nature 443(7111):527–533PubMedCrossRefGoogle Scholar
  25. 25.
    Teusink B, Passarge J, Reijenga CA, Esgalhado E, van der Weijden CC, Schepper M, Walsh MC, Bakker BM, van Dam K, Westerhoff HV, Snoep JL (2000) Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem 267(17):5313–5329PubMedCrossRefGoogle Scholar
  26. 26.
    Theobald U, Mailinger W, Reuss M, Rizzi M (1993) In vivo analysis of glucose-induced fast changes in yeast adenine nucleotide pool applying a rapid sampling technique. Anal Biochem 214(1):31–37PubMedCrossRefGoogle Scholar
  27. 27.
    Thykaer J, Christensen B, Nielsen J (2002) Metabolic network analysis of an adipoyl-7-ADCA-producing strain of Penicillium chrysogenum: elucidation of adipate degradation. Metab Eng 4(2):151–158PubMedCrossRefGoogle Scholar
  28. 28.
    van Gulik WM, Antoniewicz MR, de Laat WT, Vinke JL, Heijnen JJ (2001) Energetics of growth and penicillin production in a high-producing strain of Penicillium chrysogenum. Biotechnol Bioeng 72(2):185–193CrossRefGoogle Scholar
  29. 29.
    van Gulik WM, de Laat WTAM, Vinke JL, Heijnen JJ (2000) Application of metabolic flux analysis for the identification ofmetabolic bottlenecks in the biosynthesis of penicillin-G. Biotechnol Bioeng 68(6):602–618PubMedCrossRefGoogle Scholar
  30. 30.
    van Winden WA, van Gulik WM, Schipper D, Verheijen PJT, Krabben P, Vinke JL, Heijnen JJ (2003) Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13 C, 1 H] COSY NMR measurements and cumulative bondomer simulation. Biotechnol Bioeng 83(1):75–92PubMedCrossRefGoogle Scholar
  31. 31.
    Vaseghi S, Baumeister A, Rizzi M, Reuss M (1999) In vivo dynamics of the pentose phosphate pathway in Saccharomyces cerevisiae. Metab Eng 1:128–140PubMedCrossRefGoogle Scholar
  32. 32.
    Visser D, Heijnen JJ (2003) Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics. Metab Eng 5(3):164–176PubMedCrossRefGoogle Scholar
  33. 33.
    Visser D, Schmid JW, Mauch K, Reuss M, Heijnen JJ (2004) Optimal re-design of primary metabolism in Escherichia coli using linlog kinetics. Metab Eng 6(4):378–390PubMedCrossRefGoogle Scholar
  34. 34.
    Visser D, van Zuylen G, van Dam J, Oudshoorn A, Eman M, Ras C, van Gulik WM, Frank J, van Dedem G, Heijnen JJ (2002) Rapid sampling for analysis of \it in vivo kinetics using the BioScope: A system for continuous-pulse experiments. Biotechnol Bioeng 79:674–681PubMedCrossRefGoogle Scholar
  35. 35.
    Weckwerth W (2003) Metabolomics in systems biology. Annu Rev Plant Biol 54:669–689PubMedCrossRefGoogle Scholar
  36. 36.
    Weckwerth W (2010) Metabolomics: an integral technique in systems biology. Bioanalysis 2(4):829–836PubMedCrossRefGoogle Scholar
  37. 37.
    Westerhoff HV, Bruggeman FJ (2007) The nature of systems biology. Trends Microbiol 15(1):45–50PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • I. Emrah Nikerel
    • 1
    • 2
  • Peter J. T. Verheijen
    • 2
  • Walter M. van Gulik
    • 2
  • Joseph J. Heijnen
    • 2
  1. 1.Delft Bioinformatics LabDelft University of TechnologyDelftThe Netherlands
  2. 2.Department of Biotechnology, Kluyver Centre for Genomics of Industrial FermentationDelft University of TechnologyDelftThe Netherlands

Personalised recommendations