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Kinetic Modeling of Metabolic Pathways: Application to Serine Biosynthesis

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Systems Metabolic Engineering

Part of the book series: Methods in Molecular Biology ((MIMB,volume 985))

Abstract

In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway using kinetic data from both experimental measurements and literature review. Our methodology is presented by using the example of serine biosynthesis in E. coli.

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References

  1. Lazebnik Y (2002) Can a biologist fix a radio? Or, what I learned while studying apoptosis. Cancer Cell 2:179–182

    Article  CAS  Google Scholar 

  2. Mendes P, Kell D (1998) Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 14:869–883

    Article  CAS  Google Scholar 

  3. Szallasi Z, Stelling J, Periwal V (2006) System modeling in cellular biology: from concepts to nuts and bolts. MIT Press, Boston

    Google Scholar 

  4. Pizer LI (1963) The pathway and control of serine biosynthesis in Escherichia coli. J Biol Chem 238:3934–3944

    CAS  Google Scholar 

  5. Kanehisa M, Goto S, Sato Y et al (2012) KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res 40:D109–D114, http://www.genome.jp/kegg-bin/show_pathway?org_name=eco&mapno=00260

    Article  CAS  Google Scholar 

  6. Keseler IM, Collado-Vides J, Santos-Zavaleta A et al (2011) EcoCyc: a comprehensive database of Escherichia coli biology. Nucleic Acids Res 39:D583–D590, http://ecocyc.org/ECOLI/NEW-IMAGE?object=SERSYN-PWY

    Article  Google Scholar 

  7. Wishart DS, Tzur D, Knox C et al (2007) HMDB: the Human Metabolome Database. Nucleic Acids Res 35:D521–D526

    Article  CAS  Google Scholar 

  8. Scheer M, Grote A, Chang I et al (2010) BRENDA, the enzyme information system in 2011. Nucleic Acids Res 39:D670–D676

    Article  Google Scholar 

  9. Wittig U, Golebiewski M, Kania R et al (2006) SABIO-RK: integration and curation of reaction kinetics data. Lect Notes Bioinformatics 4075:94–103

    Google Scholar 

  10. Hoops S, Sahle S, Gauges R et al (2006) COPASI—a COmplex PAthway SImulator. Bioinformatics 22:3067–3074

    Article  CAS  Google Scholar 

  11. SBML software guide. http://sbml.org/SBML_Software_Guide 17 June 2011

  12. Hucka M, Finney A, Sauro HM et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531

    Article  CAS  Google Scholar 

  13. Le Novère N, Finney A, Hucka M et al (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23:1509–1515

    Article  Google Scholar 

  14. Li C, Donizelli M, Rodriguez N et al (2010) BioModels Database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4:92

    Article  Google Scholar 

  15. Heijnen JJ (2005) Approximative kinetic formats used in metabolic network modeling. Biotechnol Bioeng 91:534–545

    Article  CAS  Google Scholar 

  16. Liebermeister W, Klipp E (2006) Bringing metabolic networks to life: convenience rate law and thermodynamic constraints. Theor Biol Med Model 3:41

    Article  Google Scholar 

  17. Savageau MA (1976) Biochemical systems analysis: a study of function and design in molecular biology. Addison-Wesley, Boston

    Google Scholar 

  18. Smallbone K, Simeonidis E, Broomhead DS et al (2007) Something from nothing: bridging the gap between constraint-based and kinetic modelling. FEBS J 274:5576–5585

    Article  CAS  Google Scholar 

  19. Visser D, Heijnen JJ (2003) Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics. Metab Eng 5:164–176

    Article  CAS  Google Scholar 

  20. Degtyarenko K, de Matos P, Ennis M et al (2008) ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 36:D344–D350

    Article  CAS  Google Scholar 

  21. Ao P, Lee LW, Lidstrom ME et al (2008) Towards kinetic modeling of global metabolic networks: Methylobacterium extorquens AM1 growth as validation. Chin J Biotechnol 24:980–994

    Article  CAS  Google Scholar 

  22. Turnaev II, Ibragimova SS, Usuda Y et al (2006) Mathematical modeling of serine and glycine synthesis regulation in Escherichia coli. Proceedings of the fifth international conference on bioinformatics of genome regulation and structure 2:78–83

    Google Scholar 

  23. Zhao G, Winkler ME (1996) A novel alpha-ketoglutarate reductase activity of the serA-encoded 3-phosphoglycerate dehydrogenase of Escherichia coli K-12 and its possible implications for human 2-hydroxyglutaric aciduria. J Bacteriol 178:232–239

    CAS  Google Scholar 

  24. Drewke C, Klein M, Clade D et al (1996) 4-O-phosphoryl-L-threonine, a substrate of the pdxC(serC) gene product involved in vitamin B6 biosynthesis. FEBS Lett 390:179–182

    Article  CAS  Google Scholar 

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Acknowledgements

KS is grateful for the financial support of the EU FP7 (KBBE) grant 289434 “BioPreDyn: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications.” We thank Daniel Jameson for taking the time to comment on the manuscript. Natalie Stanford acknowledges the support of DirectFuel, a European Union Seventh Framework Programme (FP7-ENERGY-2010-1) under grant agreement n. [256808].

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Authors and Affiliations

  1. Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK

    Kieran Smallbone & Natalie J. Stanford

Authors
  1. Kieran Smallbone
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  2. Natalie J. Stanford
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Corresponding author

Correspondence to Kieran Smallbone .

Editor information

Editors and Affiliations

  1. Cockrell School of Engineering, Dept. of Chemical Engineering, The University of Texas at Austin, E. Dean Keeton Street 200, Austin, 78712, Texas, USA

    Hal S. Alper

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Smallbone, K., Stanford, N.J. (2013). Kinetic Modeling of Metabolic Pathways: Application to Serine Biosynthesis. In: Alper, H. (eds) Systems Metabolic Engineering. Methods in Molecular Biology, vol 985. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-299-5_7

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  • DOI: https://doi.org/10.1007/978-1-62703-299-5_7

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-298-8

  • Online ISBN: 978-1-62703-299-5

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