Advertisement

Insights into Dynamic Network States Using Metabolomic Data

  • Reihaneh Mostolizadeh
  • Andreas Dräger
  • Neema JamshidiEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1978)

Abstract

Metabolomic data is the youngest of the high-throughput data types; however, it is potentially one of the most informative, as it provides a direct, quantitative biochemical phenotype. There are a number of ways in which metabolomic data can be analyzed in systems biology; however, the thermodynamic and kinetic relevance of these data cannot be overstated. Genome-scale metabolic network reconstructions provide a natural context to incorporate metabolomic data in order to provide insight into the condition-specific kinetic characteristics of metabolic networks. Herein we discuss how metabolomic data can be incorporated into constraint-based models in a flexible framework that enables scaling from small pathways to cell-scale models, while being able to accommodate coarse-grained to more detailed, allosteric interactions, all using the well-known principle of mass action.

Keywords

Systems biology Dynamic network states Metabolomics 

References

  1. 1.
    Ahn SY, Jamshidi N, Mo ML, Wu W, Eraly SA, Dnyanmote A, Bush KT, Gallegos TF, Sweet DH, Palsson BØ, Nigam SK (2011) Linkage of organic anion transporter-1 to metabolic pathways through integrated “omics”-driven network and functional analysis. J Biol Chem 286(36):31522–31531PubMedPubMedCentralGoogle Scholar
  2. 2.
    Beard DA, Liang S-d, Qian H (2002) Energy balance for analysis of complex metabolic networks. Biophys J 83(1):79–86PubMedPubMedCentralGoogle Scholar
  3. 3.
    Bordbar A, Monk JM, King ZA, Palsson BØ (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107–120PubMedGoogle Scholar
  4. 4.
    Bordbar A, McCloskey D, Zielinski DC, Sonnenschein N, Jamshidi N, Palsson BØ (2015) Personalized whole-cell kinetic models of metabolism for discovery in genomics and pharmacodynamics. Cell Syst 1(4):283–292PubMedGoogle Scholar
  5. 5.
    Canelas AB, ten Pierick A, Ras C, Seifar RM, van Dam JC, van Gulik WM, Heijnen JJ (2009) Quantitative evaluation of intracellular metabolite extraction techniques for yeast metabolomics. Anal Chem 81:7379–7389PubMedGoogle Scholar
  6. 6.
    Chakrabarti A, Miskovic L, Soh KC, Hatzimanikatis V (2013) Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. Biotechnol J 8(9):1043–1057PubMedGoogle Scholar
  7. 7.
    Dräger A, Palsson BØ (2014) Improving collaboration by standardization efforts in systems biology. Front Bioeng 2(61).  https://doi.org/10.3389/fbioe.2014.00061
  8. 8.
    Dräger A, Zielinski DC, Keller R, Rall M, Eichner J, Palsson BO, Zell A (2015) SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks. BMC Syst Biol 9(1):1–17Google Scholar
  9. 9.
    Du B, Zielinski D, Dräger A, Tan J, Zhang Z, Ruggiero K, Arzumanyan G, Palsson BO (2016) Evaluation of rate law approximations in bottom-up kinetic models of metabolism. BMC Syst Biol 10(1):1–15Google Scholar
  10. 10.
    Duarte NC, Herrgard MJ, Palsson BØ (2004) Constraint-based models predict metabolic and associated cellular functions. Genome Res 14(7):1298–1309PubMedPubMedCentralGoogle Scholar
  11. 11.
    Ebrahim A, Almaas E, Bauer E, Bordbar A, Burgard AP, Chang RL, Dräger A, Famili I, Feist AM, Fleming RMT, Fong SS, Hatzimanikatis V, Herrgård MJ, Holder A, Hucka M, Hyduke D, Jamshidi N, Lee SY, Le Novère N, Lerman JA, Lewis NE, Ma D, Mahadevan R, Maranas C, Nagarajan H, Navid A, Nielsen J, Nielsen LK, Nogales J, Noronha A, Pal C, Palsson BO, Papin JA, Patil KR, Price ND, Reed JL, Saunders M, Senger RS, Sonnenschein N, Sun Y, Thiele I (2015) Do genome-scale models need exact solvers or clearer standards? Mol Syst Biol 11(10):831PubMedPubMedCentralGoogle Scholar
  12. 12.
    Famili I, Mahadevan R, Palsson BO (2005) k-cone analysis: determining all candidate values for kinetic parameters on a network scale. Biophys J 88(3):1616–1625PubMedGoogle Scholar
  13. 13.
    Flamholz A, Noor E, Bar-Even A, Milo R (2011) Equilibrator–the biochemical thermodynamics calculator. Nucleic Acids Res 40(D1):D770–D775PubMedPubMedCentralGoogle Scholar
  14. 14.
    Förster J, Famili I, Fu P, Palsson BØ, Nielsen J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13(2):244–253PubMedPubMedCentralGoogle Scholar
  15. 15.
    Frezza C, Zheng L, Folger O, Rajagopalan KN, MacKenzie ED, Jerby L, Micaroni M, Chaneton B, Adam J, Hedley A, Kalna G, Tomlinson IP, Pollard PJ, Watson DG, Deberardinis RJ, Shlomi T, Ruppin E, Gottlieb E (2011) Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477(7363):225–228Google Scholar
  16. 16.
    Gianchandani EP, Chavali AK, Papin JA (2010) The application of flux balance analysis in systems biology. Wiley Interdiscip Rev Syst Biol Med 2(3):372–382PubMedGoogle Scholar
  17. 17.
    Glont M, Nguyen TVN, Graesslin M, Hälke R, Ali R, Schramm J, Wimalaratne SM, Kothamachu VB, Rodriguez N, Swat MJ, Eils J, Eils R, Laibe C, Malik-Sheriff RS, Chelliah V, Le Novère N, Hermjakob H (2018) BioModels: expanding horizons to include more modelling approaches and formats. Nucleic Acids Res 46:D1248–D1253PubMedGoogle Scholar
  18. 18.
    Hamilton JJ, Dwivedi V, Reed JL (2013) Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models. Biophys J 105(2):512–522PubMedPubMedCentralGoogle Scholar
  19. 19.
    Heinrich R, Rapoport SM, Rapoport TA (1978) Metabolic regulation and mathematical models. Prog Biophys Mol Biol 32:1–82Google Scholar
  20. 20.
    Henry CS, Broadbelt LJ, Hatzimanikatis V (2007) Thermodynamics-based metabolic flux analysis. Biophys J 92(5):1792–1805PubMedGoogle Scholar
  21. 21.
    Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr J-H, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novère N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524–531PubMedPubMedCentralGoogle Scholar
  22. 22.
    Jamshidi N, Palsson BØ (2008) Formulating genome-scale kinetic models in the post-genome era. Mol Syst Biol 4(1):171PubMedPubMedCentralGoogle Scholar
  23. 23.
    Jamshidi N, Palsson BØ (2008) Top-down analysis of temporal hierarchy in biochemical reaction networks. PLoS Comput Biol 4(9):e1000177PubMedPubMedCentralGoogle Scholar
  24. 24.
    Jamshidi N, Palsson BØ (2009) Flux-concentration duality in dynamic nonequilibrium biological networks. Biophys J 97(5):L11–L13PubMedPubMedCentralGoogle Scholar
  25. 25.
    Jamshidi N, Palsson BØ (2010) Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. Biophys J 98(2):175–185PubMedPubMedCentralGoogle Scholar
  26. 26.
    Jamshidi N, Miller FJ, Mandel J, Evans T, Kuo MD (2011) Individualized therapy of HHT driven by network analysis of metabolomic profiles. BMC Syst Biol 5:200PubMedPubMedCentralGoogle Scholar
  27. 27.
    Jankowski MD, Henry CS, Broadbelt LJ, Hatzimanikatis V (2008) Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys J 95(3):1487–1499PubMedPubMedCentralGoogle Scholar
  28. 28.
    Kauffman KJ, Pajerowski JD, Jamshidi N, Palsson BØ, Edwards JS (2002) Description and analysis of metabolic connectivity and dynamics in the human red blood cell. Biophys J 83(2):2646–2662Google Scholar
  29. 29.
    Kim TY, Sohn SB, Kim YB, Kim WJ, Lee SY (2012) Recent advances in reconstruction and applications of genome-scale metabolic models. Biotechnol Adv 23(4):617–623Google Scholar
  30. 30.
    Kim B, Kim WJ, Kim DI, Lee SY (2015) Applications of genome-scale metabolic network model in metabolic engineering. J Ind Microbiol Biotechnol 42(3):339–348PubMedGoogle Scholar
  31. 31.
    King ZA, Dräger A, Ebrahim A, Sonnenschein N, Lewis NE, Palsson BO (2015) Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Comput Biol 11(8):e1004321PubMedPubMedCentralGoogle Scholar
  32. 32.
    King ZA, Lu JS, Dräger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, Lewis NE (2016) BiGG Models: a platform for integrating, standardizing, and sharing genome-scale models. Nucleic Acids Res 44(D1):D515–D522PubMedGoogle Scholar
  33. 33.
    Kümmel A, Panke S, Heinemann M (2006) Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol 2:2006.0034PubMedPubMedCentralGoogle Scholar
  34. 34.
    Lopes H, Rocha I (2017) Genome-scale modeling of yeast: chronology, applications and critical perspectives. FEMS Yeast Res 17(5).  https://doi.org/10.1093/femsyr/fox050
  35. 35.
    Lopez CF, Muhlich JL, Bachman JA, Sorger PK (2013) Programming biological models in python using PySB. Mol Syst Biol 9:646PubMedPubMedCentralGoogle Scholar
  36. 36.
    Medley K, König M, Galdzicki M, Choi K, Sauro H, Stocking K, Gu S, Smith LP, Asifullah S, Somogyi A (2014–2018) Tellurium. https://github.com/sys-bio/tellurium
  37. 37.
    Megchelenbrink W, Huynen M, Marchiori E (2014) optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks. PLoS One 9(2):e86587Google Scholar
  38. 38.
    Mo M, Palsson BØ, Herrgård MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 3:37PubMedPubMedCentralGoogle Scholar
  39. 39.
    Nielsen LK, Saa PA (2016) Construction of feasible and accurate kinetic models of metabolism: a Bayesian approach. Sci Rep 6:29635PubMedPubMedCentralGoogle Scholar
  40. 40.
    Okino MS, Mavrovouniotis ML (1998) Simplification of mathematical models of chemical reaction systems. Chem Rev 98(2):391–408PubMedGoogle Scholar
  41. 41.
    Osterlund T, Nookaew I, Nielsen J (2012) Fifteen years of large scale metabolic modeling of yeast: developments and impacts. Biotechnol Adv 30(5):979–988PubMedGoogle Scholar
  42. 42.
    Palsson BØ (2006) Systems biology: determining the capabilities of reconstructed networks. Cambridge University Press, CambridgeGoogle Scholar
  43. 43.
    Palsson BØ (2011) Systems biology: simulation of dynamic network states, 1st edn. Cambridge University Press, CambridgeGoogle Scholar
  44. 44.
    Palsson BØ, Joshi A, Ozturk SS (1987) Reducing complexity in metabolic networks: making metabolic meshes manageable. Fed Proc 46(8):2485–2489PubMedGoogle Scholar
  45. 45.
    Pan S, Reed JL (2017) Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries. Curr Opin Biotechnol 51:103–108PubMedGoogle Scholar
  46. 46.
    Ramirez-Guana M, Marcu A, Pon A, Guo AC, Sajed T, Wishart NA, Karu N, Djoumbou Y, Arndt D, Wishart DS (2017) Ymdb 2.0: a significantly expanded version of the yeast metabolome database. Nucleic Acids Res 45(D1):D440–D445Google Scholar
  47. 47.
    Reich JG, Sel’kov EE (1981) Energy metabolism of the cell a theoretical treatise. Academic, LondonGoogle Scholar
  48. 48.
    Saa PA, Nielsen LK (2016) ll-ACHRB: a scalable algorithm for sampling the feasible solution space of metabolic networks. Bioinformatics 32(15):2330–2337PubMedGoogle Scholar
  49. 49.
    Sastry A, Sonnenschein N (2013–2018) Mass-toolbox. https://github.com/opencobra/MASS-Toolbox
  50. 50.
    Schellenberger J, Palsson BØ (2009) Use of randomized sampling for analysis of metabolic networks. J Biol Chem 284(9):5457–5461PubMedGoogle Scholar
  51. 51.
    Schellenberger J, Park JO, Conrad TM, Palsson BØ (2010) BiGG: a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinf 11(1):213Google Scholar
  52. 52.
    Schellenberger J, Lewis NE, Palsson BØ (2011) Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys J 100(3):544–553PubMedPubMedCentralGoogle Scholar
  53. 53.
    Segel IH (1975) Enzyme kinetics: behavior and analysis of rapid equilibrium and steady-state enzyme systems. Wiley-Interscience, New YorkGoogle Scholar
  54. 54.
    Shoaie S, Karlsson F, Mardinoglu A, Nookaew I, Bordel S, Nielsen J (2013) Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci Rep 3:2532PubMedPubMedCentralGoogle Scholar
  55. 55.
    Srinivasan S, Cluett W, Mahadevan R (2015) Constructing kinetic models of metabolism at genome-scales: a review. Biotechnol J 10(9):1345–1359PubMedGoogle Scholar
  56. 56.
    Terzer M, Maynard ND, Covert MW, Stelling J (2009) Genome-scale metabolic networks. Wiley Interdiscip Rev Syst Biol Med 1(3):285–297PubMedGoogle Scholar
  57. 57.
    Tran LM, Rizk ML, Liao JC (2008) Ensemble modeling of metabolic networks. Biophys J 95(12):5606–5617PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Reihaneh Mostolizadeh
    • 1
    • 3
    • 4
  • Andreas Dräger
    • 1
    • 3
    • 4
  • Neema Jamshidi
    • 2
    Email author
  1. 1.Center for Bioinformatics Tübingen (ZBIT)University of TübingenTübingenGermany
  2. 2.University of California, Los AngelesLos AngelesUSA
  3. 3.Department for Computer ScienceUniversity of TübingenTübingenGermany
  4. 4.German Center for Infection Research (DZIF)TübingenGermany

Personalised recommendations