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Stoichiometric Modelling of Microbial Metabolism

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Metabolic Flux Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1191))

Abstract

Stoichiometric models describe cellular biochemistry with systems of linear equations. The models which are fundamentally based on the steady-state assumption are comparatively easy to construct and can be applied to networks up to genome scale. Fluxes are inherent variables in stoichiometric models and linear optimization can be used to identify intracellular flux distributions. Great caution, however, has to be paid to the selection of the specific objective function which inevitably implies the existence of a specific global cellular rationale. On the other hand, stoichiometric models provide an analytical platform for contextualization of experimental data. Equally important, the stoichiometric models can be used for structural analyses of metabolic networks as such supporting for example rational model-driven strategies in metabolic engineering.

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References

  1. Blank LM, Kuepfer L (2010) Metabolic flux distributions: genetic information, computational predictions, and experimental validation. Appl Microbiol Biotechnol 86(5):1243–1255

    Article  CAS  PubMed  Google Scholar 

  2. Domach MM, Shuler ML (1984) Testing of a potential mechanism for E. coli temporal cycle imprecision with a structural model. J Theor Biol 106(4):577–585

    Article  CAS  PubMed  Google Scholar 

  3. Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10(4):291–305

    CAS  PubMed Central  PubMed  Google Scholar 

  4. Varma A, Palsson BO (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 60(10):3724–3731

    CAS  PubMed Central  PubMed  Google Scholar 

  5. Stelling J (2004) Mathematical models in microbial systems biology. Curr Opin Microbiol 7(5):513–518

    Article  PubMed  Google Scholar 

  6. Klamt S, Schuster S, Gilles ED (2002) Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotechnol Bioeng 77(7):734–751

    Article  CAS  PubMed  Google Scholar 

  7. Mahadevan R, Edwards JS, Doyle FJ 3rd (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83(3):1331–1340

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Schilling CH, Edwards JS, Letscher D, Palsson BO (2000) Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnol Bioeng 71(4):286–306

    Article  CAS  PubMed  Google Scholar 

  9. Feist AM, Palsson BO (2010) The biomass objective function. Curr Opin Microbiol 13(3):344–349

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:119

    Article  PubMed Central  PubMed  Google Scholar 

  11. Edwards JS, Palsson BO (2000) The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci U S A 97(10):5528–5533

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Price ND, Reed JL, Palsson BO (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2(11):886–897

    Article  CAS  PubMed  Google Scholar 

  13. Oliveira AP, Nielsen J, Forster J (2005) Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol 5:39

    Article  PubMed Central  PubMed  Google Scholar 

  14. Dauner M, Sauer U (2001) Stoichiometric growth model for riboflavin-producing Bacillus subtilis. Biotechnol Bioeng 76(2):132–143

    Article  CAS  PubMed  Google Scholar 

  15. Oberhardt MA, Palsson BO, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320

    Article  PubMed Central  PubMed  Google Scholar 

  16. Edwards JS, Palsson BO (1999) Systems properties of the Haemophilus influenzae Rd metabolic genotype. J Biol Chem 274(25):17410–17416

    Article  CAS  PubMed  Google Scholar 

  17. Orth JD et al (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol Syst Biol 7:535

    Article  PubMed Central  PubMed  Google Scholar 

  18. Krauss M et al (2012) Integrating cellular metabolism into a multiscale whole-body model. PLoS Comput Biol 8(10):e1002750

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Kuepfer L (2010) Towards whole-body systems physiology. Mol Syst Biol 6:409

    Article  PubMed Central  PubMed  Google Scholar 

  20. Duarte NC et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A 104(6):1777–1782

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Gille C et al (2010) HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol Syst Biol 6:411

    Article  PubMed Central  PubMed  Google Scholar 

  22. Dal'Molin CG, Quek LE, Palfreyman RW, Brumbley SM, Nielsen LK (2010) C4GEM, a genome-scale metabolic model to study C4 plant metabolism. Plant Physiol 154(4):1871–1885

    Article  PubMed  Google Scholar 

  23. de Oliveira Dal'molin CG, Nielsen LK (2012) Plant genome-scale metabolic reconstruction and modelling. Curr Opin Biotechnol 24(2):271–277

    Article  PubMed  Google Scholar 

  24. Feist AM et al (2010) Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab Eng 12(3):173–186

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Kuepfer L, Sauer U, Blank LM (2005) Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Res 15(10):1421–1430

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Shlomi T, Cabili MN, Herrgard MJ, Palsson BO, Ruppin E (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26(9):1003–1010

    Article  CAS  PubMed  Google Scholar 

  27. Burgard AP, Nikolaev EV, Schilling CH, Maranas CD (2004) Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 14(2):301–312

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Papin JA et al (2004) Comparison of network-based pathway analysis methods. Trends Biotechnol 22(8):400–405

    Article  CAS  PubMed  Google Scholar 

  29. Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647–657

    Article  CAS  PubMed  Google Scholar 

  30. Kummel A, Panke S, Heinemann M (2006) Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol 2(2006):0034

    PubMed  Google Scholar 

  31. Reed JL (2012) Shrinking the metabolic solution space using experimental datasets. PLoS Comput Biol 8(8):e1002662

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Applied Microbiology, RWTH Aachen University, Worringer Weg 1, Room 42A/114, Aachen, 52074, Germany

    Lars Kuepfer

Authors
  1. Lars Kuepfer
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Corresponding author

Correspondence to Lars Kuepfer .

Editor information

Editors and Affiliations

  1. Centre for Microbial Electrosynthesis (CEMES), Advanced Water Management Centre, University of Queensland, St. Lucia, Brisbane, Queensland, Australia

    Jens O. Krömer

  2. AIBN, University of Queensland, St. Lucia, Brisbane, Queensland, Australia

    Lars K. Nielsen

  3. Biology Department, RWTH Aachen University, Aachen, Germany

    Lars M. Blank

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Kuepfer, L. (2014). Stoichiometric Modelling of Microbial Metabolism. In: Krömer, J., Nielsen, L., Blank, L. (eds) Metabolic Flux Analysis. Methods in Molecular Biology, vol 1191. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1170-7_1

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  • DOI: https://doi.org/10.1007/978-1-4939-1170-7_1

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1169-1

  • Online ISBN: 978-1-4939-1170-7

  • eBook Packages: Springer Protocols

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