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The MONGOOSE Rational Arithmetic Toolbox

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Metabolic Network Reconstruction and Modeling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1716))

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Abstract

The modeling of metabolic networks has seen a rapid expansion following the complete sequencing of thousands of genomes. The constraint-based modeling framework has emerged as one of the most popular approaches to reconstructing and analyzing genome-scale metabolic models. Its main assumption is that of a quasi-steady-state, requiring that the production of each internal metabolite be balanced by its consumption. However, due to the multiscale nature of the models, the large number of reactions and metabolites, and the use of floating-point arithmetic for the stoichiometric coefficients, ensuring that this assumption holds can be challenging.

The MONGOOSE toolbox addresses this problem by using rational arithmetic, thus ensuring that models are analyzed in a reproducible manner and consistently with modeling assumptions. In this chapter we present a protocol for the complete analysis of a metabolic network model using the MONGOOSE toolbox, via its newly developed GUI, and describe how it can be used as a model-checking platform both during and after the model construction process.

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References

  1. Benson DA, Clark K, Karsch-Mizrachi I et al (2015) GenBank. Nucleic Acids Res 43:D30–D35

    Article  CAS  PubMed  Google Scholar 

  2. Zhang C, Hua Q (2015) Applications of genome-scale metabolic models in biotechnology and systems medicine. Front Physiol 6:413

    PubMed  Google Scholar 

  3. Long MR, Ong WK, Reed JL (2015) Computational methods in metabolic engineering for strain design. Curr Opin Biotechnol 34:135–141

    Article  CAS  PubMed  Google Scholar 

  4. Varma A, Palsson B (1994) Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotechnol 12:994–998

    Article  CAS  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Suthers PF, Zomorrodi A, Maranas CD (2009) Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol Syst Biol 5:1

    Article  Google Scholar 

  7. Gottstein W, Olivier BG, Bruggeman FJ, Teusink B (2016) Constraint-based stoichiometric modelling from single organisms to microbial communities. J R Soc Interface 13(124):20160627

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chindelevitch L, Trigg J, Regev A, Berger B (2014) An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models. Nat Commun 5:4893

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Boettiger C (2015) An introduction to Docker for reproducible research, with examples from the R environment. In: ACM SIGOPS operating systems review, special issue on repeatability and sharing of experimental artifacts, vol 49, no 1. ACM, New York, pp 71–79

    Google Scholar 

  10. van Rossum G (1995) Python tutorial. Technical Report CS-R9526, Centrum voor Wiskunde en Informatica (CWI), Amsterdam

    Google Scholar 

  11. Applegate D, Cook W, Dash S, Espinoza D (2007) Exact solutions to linear programming problems. Oper Res Lett 35:693–699

    Article  Google Scholar 

  12. Behnel S, Bradshaw R, Citro C, Dalcin L et al (2011) Cython: the best of both worlds. Comput Sci Eng 13:31–39

    Article  Google Scholar 

  13. Bornstein BJ, Keating SM, Jouraku A, Hucka M (2008) LibSBML: an API library for SBML. Bioinformatics 24(6):880–881

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. PyQt Whitepaper. Riverbank Computing. http://www.riverbankcomputing.com/static/Docs/PyQt4/pyqt-whitepaper-a4.pdf Accessed October 9, 2017

  15. Steffensen JL, Dufault-Thompson K, Zhang Y (2016) PSAMM: a portable system for the analysis of metabolic models. PLoS Comput Biol 12(2):e1004732

    Article  PubMed  PubMed Central  Google Scholar 

  16. Matzigkeit G, Oliva A, Tanner T, Vaughan GV (2015) GNU Libtool reference manual. Samurai Media Limited, Surrey

    Google Scholar 

  17. Granlund T, The GMP Development Team (2016) GNU MP: the GNU Multiple Precision Arithmetic Library. http://gmplib.org/. Accessed October 9, 2017

  18. Hucka M, Finney A, Sauro HM, Bolouri H et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524–531

    Article  CAS  PubMed  Google Scholar 

  19. Kim HU, Kim TY, Lee SY (2010) Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Mol BioSyst 6(2):339–348

    Article  CAS  PubMed  Google Scholar 

  20. In Silico Organisms (2016) UCSD systems biology group. http://systemsbiology.ucsd.edu/InSilicoOrganisms/OtherOrganisms. Accessed October 9, 2017

  21. Hyduke D, Schellenberger J, Que R, Fleming R et al (2011) COBRA Toolbox 2.0. Protoc Exch http://dx.doi.org/10.1038/protex.2011.234

  22. Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: constraints-based reconstruction and analysis for python. BMC Syst Biol 7(74). http://dx.doi.org/10.1186/1752-0509-7-7

  23. Ravikrishnan A, Raman K (2015) Critical assessment of genome-scale metabolic networks: the need for a unified standard. Brief Bioinform 16(6):1057–1068

    Article  PubMed  Google Scholar 

  24. Chindelevitch LA (2010) Extracting information from biological networks. Dissertation, Massachusetts Institute of Technology. http://hdl.handle.net/1721.1/64607

    Google Scholar 

  25. Schuster R, Schuster S (1991) Detecting strictly detailed balanced subnetworks in open chemical reaction networks. J Math Chem 6:17–40

    Article  CAS  Google Scholar 

  26. Ponce-de-León M, Montero F, Peretó J (2013) Solving gap metabolites and blocked reactions in genome-scale models: application to the metabolic network of Blattabacterium cuenoti. BMC Syst Biol 7(114)

    Google Scholar 

  27. Gagneur J, Klamt S (2004) Computation of elementary modes: a unifying framework and the new binary approach. BMC Bioinf 5(175)

    Google Scholar 

  28. Klamt S, Gilles E (2004) Minimal cut sets in biochemical reaction networks. Bioinformatics 20(2):226–234

    Article  CAS  PubMed  Google Scholar 

  29. Acuña V, Chierichetti F, Lacroix V, Marchetti-Spaccamela A et al (2009) Modes and cuts in metabolic networks: complexity and algorithms. BioSystems 95(1):51–60

    Article  PubMed  Google Scholar 

  30. Suthers PF, Dasika MS, Kumar VS, Denisov G et al (2009) A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189. PLoS Comput Biol 5(2):e1000285

    Google Scholar 

  31. Calcote J (2010) Autotools: a practioner’s guide to GNU autoconf, automake, and libtool. No Starch Press, San Francisco, CA

    Google Scholar 

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Acknowledgements

The authors would like to thank Dan Park for creating the MONGOOSE website. In addition, the authors would like to acknowledge the invaluable input of Bonnie Berger, Aviv Regev, and Jason Trigg, as well as the help of Daniel Espinoza and Dan Steffy. This work is supported by an NSERC Discovery Grant as well as an Alfred P. Sloan Fellowship.

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Correspondence to Leonid Chindelevitch .

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Le, C., Chindelevitch, L. (2018). The MONGOOSE Rational Arithmetic Toolbox. In: Fondi, M. (eds) Metabolic Network Reconstruction and Modeling. Methods in Molecular Biology, vol 1716. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7528-0_3

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  • DOI: https://doi.org/10.1007/978-1-4939-7528-0_3

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7527-3

  • Online ISBN: 978-1-4939-7528-0

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