Abstract
Stem cell metabolism is intrinsically tied to stem cell pluripotency and function. Yet, understanding metabolic rewiring in stem cells has been challenging due to the complex and highly interconnected nature of the metabolic network. Genome-scale metabolic network models are increasingly used to holistically model the metabolic behavior of various cells and tissues using transcriptomics data. However, these powerful approaches that model steady-state behavior have limited utility for studying dynamic stem cell state transitions. To address this complexity, we recently developed the dynamic flux activity (DFA) approach; DFA is a genome-scale modeling approach that uses time-course metabolic data to predict metabolic flux rewiring. This protocol outlines the steps for modeling steady-state and dynamic metabolic behavior using transcriptomics and time-course metabolomics data, respectively. Using data from naive and primed pluripotent stem cells, we demonstrate how we can use genome-scale modeling and DFA to comprehensively characterize the metabolic differences between these states.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Brunk E, Sahoo S, Zielinski DC et al (2018) Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36:272
O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities. Cell 161:971–987. https://doi.org/10.1016/j.cell.2015.05.019
Feist AM, Herrgard MJ, Thiele I et al (2009) Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 7:129–143. https://doi.org/10.1038/nrmicro1949
Bordbar A, Monk JM, King ZA, Palsson BO (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107–120. https://doi.org/10.1038/nrg3643
Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305. https://doi.org/10.1038/nrmicro2737
Folger O, Jerby L, Frezza C et al (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501. https://doi.org/10.1038/msb.2011.35
Frezza C, Zheng L, Folger O et al (2011) Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477:225–U132. https://doi.org/10.1038/nature10363
Uhlen M, Fagerberg L, Hallstrom BM et al (2015) Tissue-based map of the human proteome. Science 80:347. https://doi.org/10.1126/science.1260419
Shlomi T, Cabili MN, Herrgard MJ et al (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26:1003–1010. https://doi.org/10.1038/nbt.1487
Chandrasekaran S, Zhang J, Sun Z et al (2017) Comprehensive mapping of pluripotent stem cell metabolism using dynamic genome-scale network modeling. Cell Rep 21:2965–2977. https://doi.org/10.1016/J.CELREP.2017.07.048
Becker SA, Feist AM, Mo ML et al (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2:727–738. https://doi.org/10.1038/nprot.2007.99
Schellenberger J, Que R, Fleming RMT et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6:1290–1307. https://doi.org/10.1038/nprot.2011.308
Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol 7:74. https://doi.org/10.1186/1752-0509-7-74
Duarte NC, Becker SA, Jamshidi N et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A 104:1777–1782. https://doi.org/10.1073/pnas.0610772104
King ZA, Lu J, Drager A et al (2016) BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:D515–D522. https://doi.org/10.1093/nar/gkv1049
Zhang J, Ratanasirintrawoot S, Chandrasekaran S et al (2016) LIN28 regulates stem cell metabolism and conversion to primed pluripotency. Cell Stem Cell 19:66–80. https://doi.org/10.1016/j.stem.2016.05.009
Gunawardena J (2014) Time-scale separation—Michaelis and Menten’s old idea, still bearing fruit. FEBS J 281:473–488. https://doi.org/10.1111/febs.12532
Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14:491–496. https://doi.org/10.1016/j.copbio.2003.08.001
Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. https://doi.org/10.1038/nbt.1614
Ibarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420:186–189. https://doi.org/10.1038/nature01149
Feist AM, Palsson BO (2016) What do cells actually want? Genome Biol 17:110. https://doi.org/10.1186/s13059-016-0983-3
Reed JL (2012) Shrinking the metabolic solution space using experimental datasets. PLoS Comput Biol 8:e1002662. https://doi.org/10.1371/journal.pcbi.1002662
Zur H, Ruppin E, Shlomi T (2010) iMAT: an integrative metabolic analysis tool. Bioinformatics 26:3140–3142. https://doi.org/10.1093/bioinformatics/btq602
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 107:17845–17850. https://doi.org/10.1073/pnas.1005139107
Xia JG, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res 43:W251–W257. https://doi.org/10.1093/nar/gkv380
Wishart DS, Feunang YD, Marcu A et al (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46:D608–D617. https://doi.org/10.1093/nar/gkx1089
Kanehisa M, Furumichi M, Tanabe M et al (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361. https://doi.org/10.1093/nar/gkw1092
Kim S, Thiessen PA, Bolton EE et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213. https://doi.org/10.1093/nar/gkv951
Hastings J, Owen G, Dekker A et al (2016) ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res 44:D1214–D1219. https://doi.org/10.1093/nar/gkv1031
Smith CA, O’Maille G, Want EJ et al (2005) METLIN—a metabolite mass spectral database. Ther Drug Monit 27:747–751. https://doi.org/10.1097/01.ftd.0000179845.53213.39
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. https://doi.org/10.1093/bioinformatics/btg015
Raghevendran V, Gombert AK, Christensen B et al (2004) Phenotypic characterization of glucose repression mutants ofSaccharomyces cerevisiae using experiments with13C-labelled glucose. Yeast 21:769–779. https://doi.org/10.1002/yea.1136
Sabra W, Bommareddy RR, Maheshwari G et al (2017) Substrates and oxygen dependent citric acid production by Yarrowia lipolytica: insights through transcriptome and fluxome analyses. Microb Cell Factories 16:78. https://doi.org/10.1186/s12934-017-0690-0
Lewis NE, Hixson KK, Conrad TM et al (2010) Omic data from evolved E-coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6:390. https://doi.org/10.1038/msb.2010.47
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Shen, F., Cheek, C., Chandrasekaran, S. (2019). Dynamic Network Modeling of Stem Cell Metabolism. In: Cahan, P. (eds) Computational Stem Cell Biology. Methods in Molecular Biology, vol 1975. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9224-9_14
Download citation
DOI: https://doi.org/10.1007/978-1-4939-9224-9_14
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-9223-2
Online ISBN: 978-1-4939-9224-9
eBook Packages: Springer Protocols