Inferring Metabolic Flux from Time-Course Metabolomics

  • Scott Campit
  • Sriram ChandrasekaranEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2088)


The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.

Key words

Dynamic flux activity Constraint-based modeling Flux balance analysis Genome-scale metabolic models Time-course metabolomics Cancer metabolism 

Supplementary material

465987_1_En_13_MOESM1_ESM.docx (51 kb)
Data 1 (DOCX 20 kb)


  1. 1.
    Luengo A, Gui DY, Vander Heiden MG (2017) Targeting metabolism for cancer therapy. Cell Chem Biol 24(9):1161–1180. Scholar
  2. 2.
    Saa PA, Nielsen LK (2017) Formulation, construction and analysis of kinetic models of metabolism: a review of modelling frameworks. Biotechnol Adv 35(8):981–1003. Scholar
  3. 3.
    Nilsson A, Nielsen J, Palsson BO (2017) Commentary metabolic models of protein allocation call for the kinetome. Cell Syst 5:538–541. Scholar
  4. 4.
    Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28(3):245–248. Scholar
  5. 5.
    O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities. Cell 161(5):971–987. Scholar
  6. 6.
    Uhlén M, Hallström BM, Lindskog C, Mardinoglu A, Pontén F, Nielsen J (2016) Transcriptomics resources of human tissues and organs. Mol Syst Biol 12(4):862. Scholar
  7. 7.
    Chandrasekaran S, Zhang J, Sun Z, Zhang L, Ross CA, Huang Y-C et al (2017) Comprehensive mapping of pluripotent stem cell metabolism using dynamic genome-scale network modeling. Cell Rep 21(10):2965–2977. Scholar
  8. 8.
    Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson ÓE, Palsson BO (2017) Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci Rep 7:46249CrossRefGoogle Scholar
  9. 9.
    Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A et al (2017) Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0.
  10. 10.
    Zielinski DC, Jamshidi N, Corbett AJ, Bordbar A, Thomas A, Palsson BO (2017) Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism. Sci Rep 7:41241CrossRefGoogle Scholar
  11. 11.
    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):e1004321. Scholar
  12. 12.
    Jain M, Nilsson R, Sharma S, Madhusudhan N, Kitami T, Souza AL et al (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336(6084):1040–1044. Scholar
  13. 13.
    Yizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B et al (2014) Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. Elife 3.
  14. 14.
    Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD et al (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6:390. Scholar
  15. 15.
    Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G et al (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46(W1):W486–W494. Scholar
  16. 16.
    Shen F, Boccuto L, Pauly R, Srikanth S, Chandrasekaran S (2019) Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors. Genome Biol 20(1):49CrossRefGoogle Scholar
  17. 17.
    Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 107(41):17845–17850. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Program in Chemical BiologyUniversity of MichiganAnn ArborUSA
  2. 2.Department of Biomedical EngineeringUniversity of MichiganAnn ArborUSA

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