Skip to main content
SpringerLink
Log in

Enhancing Metabolic Models with Genome-Scale Experimental Data

  • Chapter
  • First Online:
Systems Biology

Part of the book series: RNA Technologies ((RNATECHN))

Abstract

Genome-scale metabolic reconstructions have found widespread use in scientific research as structured representations of knowledge about an organism’s metabolism and as starting points for metabolic simulations. With few simplifying assumptions, genome-scale models of metabolism can be used to estimate intracellular reaction rates in any organism for which a well-curated metabolic reconstruction is available. However, with the rapid increase in the availability of genome-scale data, there is ample opportunity to refine the predictions made by metabolic models by integrating experimental data. In this chapter, we review different methods for combining genome-scale metabolic models with genome-scale experimental data, such as transcriptomics, proteomics, and metabolomics. Integrating experimental data into the models generally results in more precise and accurate simulations of cellular metabolism.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Åkesson M, Förster J, Nielsen J (2004) Integration of gene expression data into genome-scale metabolic models. Metab Eng 6:285–293

    Article  PubMed  CAS  Google Scholar 

  • Antoniewicz MR (2015) Methods and advances in metabolic flux analysis: a mini-review. J Ind Microbiol Biotechnol 42:317–325

    Article  PubMed  CAS  Google Scholar 

  • Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y et al (2006) Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol 2:2006.0008

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Basan M, Hui S, Okano H, Zhang Z, Shen Y et al (2015) Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528:99–104

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Becker SA, Palsson BO (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 4:e1000082

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bordbar A, McCloskey D, Zielinski DC, Sonnenschein N, Jamshidi N et al (2015) Personalized whole-cell kinetic models of metabolism for discovery in genomics and pharmacodynamics. Cell Syst 1:283–292

    Article  PubMed  CAS  Google Scholar 

  • Bordbar A, Johansson PI, Paglia G, Harrison SJ, Wichuk K et al (2016) Identified metabolic signature for assessing red blood cell unit quality is associated with endothelial damage markers and clinical outcomes. Transfusion 56:852–862

    Article  PubMed  Google Scholar 

  • Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson ÓE et al (2017) Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci Rep 7:46249

    Article  PubMed  PubMed Central  Google Scholar 

  • Crabtree HG (1929) Observations on the carbohydrate metabolism of tumours. Biochem J 23:536–545

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • de Oliveira Dal’Molin CG, Quek L-E, Palfreyman RW, Brumbley SM, Nielsen LK (2010) AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol 152:579–589

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci 104:1777–1782

    Article  PubMed  CAS  Google Scholar 

  • Edwards JS, Palsson BO (2000) The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci 97:5528–5533

    Article  PubMed  CAS  Google Scholar 

  • Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E et al (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:527–527

    Article  PubMed Central  Google Scholar 

  • Förster J, Famili I, Palsson BO, Nielsen J (2003) Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. OMICS J Integr Biol 7:193–202

    Article  Google Scholar 

  • Goelzer A, Muntel J, Chubukov V, Jules M, Prestel E et al (2015) Quantitative prediction of genome-wide resource allocation in bacteria. Metab Eng 32:232–243

    Article  PubMed  CAS  Google Scholar 

  • Gopalakrishnan S, Maranas CD (2015) 13C metabolic flux analysis at a genome-scale. Metab Eng 32:12–22

    Article  PubMed  CAS  Google Scholar 

  • Gry M, Rimini R, Strömberg S, Asplund A, Pontén F et al (2009) Correlations between RNA and protein expression profiles in 23 human cell lines. BMC Genomics 10:365

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Halldorsson S, Rohatgi N, Magnusdottir M, Choudhary KS, Gudjonsson T et al (2017) Metabolic re-wiring of isogenic breast epithelial cell lines following epithelial to mesenchymal transition. Cancer Lett 396:117–129

    Article  PubMed  CAS  Google Scholar 

  • Heavner BD, Price ND (2015) Comparative analysis of yeast metabolic network models highlights progress, opportunities for metabolic reconstruction. PLoS Comput Biol 11:1–26

    Article  CAS  Google Scholar 

  • Jamshidi N, Palsson BØ (2008) Formulating genome-scale kinetic models in the post-genome era. Mol Syst Biol 4:171

    Article  PubMed  PubMed Central  Google Scholar 

  • Karr JR, Sanghvi JC, MacKlin DN, Gutschow M, Jacobs JM et al (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150:389–401

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Khodayari A, Maranas CD (2016) A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains. Nat Commun 7:13806

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lerman JA, Hyduke DR, Latif H, Portnoy VA, Lewis NE et al (2012) In silico method for modelling metabolism and gene product expression at genome scale. Nat Commun 3:929

    Article  PubMed  CAS  Google Scholar 

  • Li L, Zhou X, Ching W-K, Wang P (2010) Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines. BMC Bioinformatics 11:501

    PubMed  PubMed Central  Google Scholar 

  • Lloyd CJ, Ebrahim A, Yang L, King ZA, Catoiu E et al (2017) COBRAme: a computational framework for building and manipulating models of metabolism and gene expression. bioRxiv 106559. https://doi.org/10.1101/106559

  • Machado D, Herrgård M (2014) Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 10:e1003580

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Mardinoglu A, Agren R, Kampf C, Asplund A, Nookaew I et al (2014) Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol Syst Biol 9:649–649

    Article  CAS  Google Scholar 

  • McCloskey D, Palsson BØ, Feist AM (2013) Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol Syst Biol 9:661

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Noor E, Haraldsdóttir HS, Milo R, Fleming RMT (2013) Consistent estimation of Gibbs energy using component contributions. PLoS Comput Biol 9:e1003098

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • O’Brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson BO (2013) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9:693–693

    PubMed  PubMed Central  Google Scholar 

  • O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities. Cell 161:971–987

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Oberhardt MA, Puchałka J, Fryer KE, Martins Dos Santos VAP, Papin JA (2008) Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol 190:2790–2803

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Plaimas K, Mallm J-P, Oswald M, Svara F, Sourjik V et al (2008) Machine learning based analyses on metabolic networks supports high-throughput knockout screens. BMC Syst Biol 2:67

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Price ND, Reed JL, Palsson BØ (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2:886–897

    Article  PubMed  CAS  Google Scholar 

  • Saa PA, Nielsen LK (2017) Formulation, construction and analysis of kinetic models of metabolism: a review of modelling frameworks. Biotechnol Adv 35:981–1003

    Article  PubMed  CAS  Google Scholar 

  • Sánchez BJ, Zhang C, Nilsson A, Lahtvee P, Kerkhoven EJ et al (2017) Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol Syst Biol 13:935

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Sauer U (2006) Metabolic networks in motion: 13C-based flux analysis. Mol Syst Biol 2:1–10

    Article  CAS  Google Scholar 

  • Schilling CH, Palsson BØ (2000) Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J Theor Biol 203:249–283

    Article  PubMed  CAS  Google Scholar 

  • Shaked I, Oberhardt MA, Atias N, Sharan R, Ruppin E (2016) Metabolic network prediction of drug side effects. Cell Syst 2:209–213

    Article  PubMed  CAS  Google Scholar 

  • Shlomi T, Cabili MN, Herrgård MJ, Palsson BØ, Ruppin E (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26:1003–1010

    Article  PubMed  CAS  Google Scholar 

  • Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E (2011) Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the warburg effect. PLoS Comput Biol 7:1–8

    Article  CAS  Google Scholar 

  • Shoaie S, Karlsson F, Mardinoglu A, Nookaew I, Bordel S et al (2013) Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci Rep 3:2532

    Article  PubMed  PubMed Central  Google Scholar 

  • Soh KC, Hatzimanikatis V (2014) Constraining the flux space using thermodynamics and integration of metabolomics data. In: Krömer JO, Nielsen LK, Blank LM (eds) Metabolic flux analysis: methods and protocols. Springer, New York, pp 49–63

    Google Scholar 

  • Soh KC, Miskovic L, Hatzimanikatis V (2012) From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks. FEMS Yeast Res 12:129–143

    Article  PubMed  CAS  Google Scholar 

  • Srinivasan S, Cluett WR, Mahadevan R (2015) Constructing kinetic models of metabolism at genome-scales: a review. Biotechnol J 10:1345–1359

    Article  PubMed  CAS  Google Scholar 

  • Stephanopoulos G (1999) Metabolic fluxes and metabolic engineering. Metab Eng 1:1–11

    Article  PubMed  CAS  Google Scholar 

  • Teusink B, Passarge J, Reijenga CA, Esgalhado E, Van Der Weijden CC et al (2000) Can yeast glycolysis be understood terms of vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem 267:5313–5329

    Article  PubMed  CAS  Google Scholar 

  • Thiele I, Jamshidi N, Fleming RMT, Palsson BO (2009) Genome-scale reconstruction of Escherichia coli’s transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput Biol 5:e1000312

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Thiele I, Fleming RMT, Que R, Bordbar A, Diep D et al (2012) Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage. PLoS One 7:e45635

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Thomas A, Rahmanian S, Bordbar A, Palsson BØ, Jamshidi N (2015) Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance. Sci Rep 4:3925

    Article  CAS  Google Scholar 

  • Warburg O, Wind F, Negelein E (1927) The metabolism of tumors in the body. J Gen Physiol 8:519–530

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Yang L, Ma D, Ebrahim A, Lloyd CJ, Saunders MA et al (2016) solveME: fast and reliable solution of nonlinear ME models. BMC Bioinformatics 17:391

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zampieri M, Enke T, Chubukov V, Ricci V, Piddock L et al (2017) Metabolic constraints on the evolution of antibiotic resistance. Mol Syst Biol 13:917

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark

    Kristian Jensen & Markus J. Herrgård

  2. Center for Systems Biology, University of Iceland, Reykjavik, Iceland

    Steinn Gudmundsson

Authors
  1. Kristian Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steinn Gudmundsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Markus J. Herrgård
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Markus J. Herrgård .

Editor information

Editors and Affiliations

  1. Max Delbrück Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Berlin-Buch, Berlin, Germany

    Nikolaus Rajewsky

  2. Nanobiomedical Center, Adam Mickiewicz University, Poznań, Poland

    Stefan Jurga

  3. Polish Academy of Sciences, Institute of Bioorganic Chemistry, Poznań, Poland

    Jan Barciszewski

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jensen, K., Gudmundsson, S., Herrgård, M.J. (2018). Enhancing Metabolic Models with Genome-Scale Experimental Data. In: Rajewsky, N., Jurga, S., Barciszewski, J. (eds) Systems Biology. RNA Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-92967-5_17

Download citation

Publish with us

Policies and ethics

Search

Navigation