Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine

Part of the Methods in Molecular Biology book series (MIMB, volume 1386)


Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model’s topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.

An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.

Key words

Systems biology Constraint-based modeling Personalized health Metabolomics OMICS COBRA Flux balance analysis Cancer metabolism Human disease Personalized models 



This study was supported by an ATTRACT program grant (FNR/A12/01) from the Luxembourg National Research Fund (FNR).


  1. 1.
    Kitano H (2001) Foundations of systems biology. MIT Press, Cambridge, MAGoogle Scholar
  2. 2.
    Machado D, Costa R, Rocha M et al (2011) Modeling formalisms in systems biology. AMB Express 1:45PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Durot M, Bourguignon PY, Schachter V (2009) Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 33:164–190PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Palsson BØ (2006) Systems biology: properties of reconstructed networks. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  5. 5.
    Aurich MK, Thiele I (2012) Contextualization procedure and modeling of monocyte specific TLR signaling. PLoS One 7:e49978PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Li F, Thiele I, Jamshidi N, Palsson BØ (2009) Identification of potential pathway mediation targets in toll-like receptor signaling. PLoS Comput Biol 5:e1000292PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Papin JA, Palsson BØ (2004) The JAK-STAT signaling network in the human B-cell: an extreme signaling pathway analysis. Biophys J 87:37–46PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Thiele I, Jamshidi N, Fleming RMT et al (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:e1000312PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Thorleifsson SG, Thiele I (2011) rBioNet: a COBRA toolbox extension for reconstructing high-quality biochemical networks. Bioinformatics 27:2009–2010PubMedCrossRefGoogle Scholar
  10. 10.
    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 6:1290–1307PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Reed JL, Famili I, Thiele I et al (2006) Towards multidimensional genome annotation. Nat Rev Genet 7:130–141PubMedCrossRefGoogle Scholar
  13. 13.
    Sahoo S, Thiele I (2013) Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells. Hum Mol Genet 22:2705–2722PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Folger O, Jerby L, Frezza C et al (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotech 28:245–248CrossRefGoogle Scholar
  16. 16.
    Varma A, Palsson BØ (1994) Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotech 12:994–998CrossRefGoogle Scholar
  17. 17.
    Terzer M, Maynard ND, Covert MW et al (2009) Genome-scale metabolic networks. Wiley Interdiscip Rev Syst Biol Med 1:285–297PubMedCrossRefGoogle Scholar
  18. 18.
    Aurich M, Paglia G, Rolfsson Ó et al (2015) Prediction of intracellular metabolic states from extracellular metabolomic data. Metabolomics 11:603–619Google Scholar
  19. 19.
    Lewis NE, Nagarajan H, Palsson BØ (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305PubMedCentralPubMedGoogle Scholar
  20. 20.
    Savinell JM, Palsson BØ (1992) Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism. J Theor Biol 154:421–454PubMedCrossRefGoogle Scholar
  21. 21.
    Vo TD, Greenberg HJ, Palsson BØ (2004) Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. J Biol Chem 279:39532–39540PubMedCrossRefGoogle Scholar
  22. 22.
    Feist AM, Palsson BØ (2010) The biomass objective function. Curr Opin Microbiol 1:344–349CrossRefGoogle Scholar
  23. 23.
    Hernández Patiño CE, Jaime-Muñoz G, Resendis-Antonio O (2013) Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells. Front Physiol 3:481PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Duarte NC, Becker SA, Jamshidi N et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. PNAS 104:1777–1782PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Thiele I, Swainston N, Fleming RMT et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425PubMedCrossRefGoogle Scholar
  26. 26.
    Bordbar A, Feist AM, Usaite-Black R et al (2011) A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst Biol 5:180PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Thiele I, Price ND, Vo TD et al (2005) Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. J Biol Chem 280:11683–11695PubMedCrossRefGoogle Scholar
  28. 28.
    Bordel S, Agren R, Nielsen J (2010) Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput Biol 6:e1000859PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Lewis NE, Jamshidi N, Thiele I et al (2009) Metabolic systems biology: a constraint-based approach. In: Encyclopedia of complexity and system science. Chapter 329, 5535-5552, Springer, New York, ISBN 978-0-387-75888-6Google Scholar
  30. 30.
    Bordbar A, Lewis NE, Schellenberger J et al (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 6:422PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276PubMedCrossRefGoogle Scholar
  32. 32.
    Gudmundsson S, Thiele I (2010) Computationally efficient flux variability analysis. BMC Bioinformatics 11:489PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Schellenberger J, Palsson BØ (2009) Use of randomized sampling for analysis of metabolic networks. J Biol Chem 284:5457–5461PubMedCrossRefGoogle Scholar
  34. 34.
    Kaufman DE, Smith RL (1998) Direction choice for accelerated convergence in hit-and-run sampling. Oper Res 46:84–95CrossRefGoogle Scholar
  35. 35.
    Becker SA, Palsson BØ (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 4:e1000082PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Jerby L, Ruppin E (2012) Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clin Cancer Res 18:5572–5584PubMedCrossRefGoogle Scholar
  37. 37.
    Lewis NE, Schramm G, Bordbar A et al (2010) Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat Biotechnol 28:1279–1285PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Bordbar A, Palsson BØ (2012) Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med 271:131–141PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Shlomi T, Cabili MN, Ruppin E (2009) Predicting metabolic biomarkers of human inborn errors of metabolism. Mol Syst Biol 5:263PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Rolfsson O, Palsson BØ, Thiele I (2011) The human metabolic reconstruction Recon 1 directs hypotheses of novel human metabolic functions. BMC Syst Biol 5:155PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Heinken A, Thiele I (2015) Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework. Gut Microbes. doi: 10.1080/19490976.2015.1023494 PubMedGoogle Scholar
  42. 42.
    Heinken A, Sahoo S, Fleming RMT et al (2013) Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4:28–40PubMedCentralPubMedCrossRefGoogle Scholar
  43. 43.
    Ma H, Sorokin A, Mazein A et al (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol 3:135PubMedCentralPubMedCrossRefGoogle Scholar
  44. 44.
    Hao T, Ma HW, Zhao XM et al (2010) Compartmentalization of the Edinburgh human metabolic network. BMC Bioinformatics 11:393PubMedCentralPubMedCrossRefGoogle Scholar
  45. 45.
    Gille C, Bolling C, Hoppe A et al (2010) HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol Syst Biol 6:411PubMedCentralPubMedCrossRefGoogle Scholar
  46. 46.
    Mardinoglu A, Agren R, Kampf C et al (2014) Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun 5:3083PubMedCrossRefGoogle Scholar
  47. 47.
    Agren R, Bordel S, Mardinoglu A et al (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 8:e1002518PubMedCentralPubMedCrossRefGoogle Scholar
  48. 48.
    Agren R, Liu L, Shoaie S et al (2013) The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol 9:e1002980PubMedCentralPubMedCrossRefGoogle Scholar
  49. 49.
    Agren R, Mardinoglu A, Asplund A et al (2014) Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol 10:721PubMedCentralPubMedCrossRefGoogle Scholar
  50. 50.
    Mardinoglu A, Agren R, Kampf C et al (2013) Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol Syst Biol 9:649PubMedCentralPubMedCrossRefGoogle Scholar
  51. 51.
    Sahoo S, Franzson L, Jonsson JJ et al (2012) A compendium of inborn errors of metabolism mapped onto the human metabolic network. Mol Biosyst 8:2545–2558PubMedCrossRefGoogle Scholar
  52. 52.
    Jain M, Nilsson R, Sharma S et al (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336:1040–1044PubMedCentralPubMedCrossRefGoogle Scholar
  53. 53.
    Uhlen M, Oksvold P, Fagerberg L et al (2010) Towards a knowledge-based human protein atlas. Nat Biotech 28:1248–1250CrossRefGoogle Scholar
  54. 54.
    Orth JD, Palsson B (2012) Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions. BMC Syst Biol 6:30PubMedCentralPubMedCrossRefGoogle Scholar
  55. 55.
    Thiele I, Vlassis N, Fleming RMT (2014) fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30:2529–2531PubMedCentralPubMedCrossRefGoogle Scholar
  56. 56.
    Wishart DS, Knox C, Guo AC et al (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37:D603–D610PubMedCentralPubMedCrossRefGoogle Scholar
  57. 57.
    Sahoo S, Aurich MK, Jonsson JJ et al (2014) Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease. Front Physiol 5:91PubMedCentralPubMedCrossRefGoogle Scholar
  58. 58.
    Sahoo S, Haraldsdottir HS, Fleming RMT et al (2014) Modeling the effects of commonly used drugs on human metabolism. FEBS J 282:297–317PubMedCrossRefGoogle Scholar
  59. 59.
    Colijn C, Brandes A, Zucker J et al (2009) Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 5:e1000489PubMedCentralPubMedCrossRefGoogle Scholar
  60. 60.
    Cox J, Mann M (2007) Is proteomics the new genomics? Cell 130:395–398PubMedCrossRefGoogle Scholar
  61. 61.
    Gatto F, Nookaew I, Nielsen J (2014) Chromosome 3p loss of heterozygosity is associated with a unique metabolic network in clear cell renal carcinoma. PNAS 111:E866–E875PubMedCentralPubMedCrossRefGoogle Scholar
  62. 62.
    Jamshidi N, Palsson BØ (2006) Systems biology of SNPs. Mol Syst Biol 2:38PubMedCentralPubMedCrossRefGoogle Scholar
  63. 63.
    Reed JL (2012) Shrinking the metabolic solution space using experimental datasets. PLoS Comput Biol 8:e1002662PubMedCentralPubMedCrossRefGoogle Scholar
  64. 64.
    Mo ML, Palsson BØ, Herrgard MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 3:37PubMedCentralPubMedCrossRefGoogle Scholar
  65. 65.
    Shlomi T, Cabili MN, Herrgard MJ et al (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26:1003–1010PubMedCrossRefGoogle Scholar
  66. 66.
    Zhao Y, Huang J (2011) Reconstruction and analysis of human heart-specific metabolic network based on transcriptome and proteome data. Biochem Biophys Res Commun 415:450–454PubMedCrossRefGoogle Scholar
  67. 67.
    Karlstadt A, Fliegner D, Kararigas G et al (2012) CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism. BMC Syst Biol 6:114PubMedCentralPubMedCrossRefGoogle Scholar
  68. 68.
    Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6:401PubMedCentralPubMedCrossRefGoogle Scholar
  69. 69.
    Chang RL, Xie L, Xie L et al (2010) Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput Biol 6:e1000938PubMedCentralPubMedCrossRefGoogle Scholar
  70. 70.
    Bordbar A, Mo ML, Nakayasu ES et al (2012) Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol Syst Biol 8:558PubMedCentralPubMedCrossRefGoogle Scholar
  71. 71.
    Bordbar A, Jamshidi N, Palsson BØ (2011) iAB-RBC-283: a proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states. BMC Syst Biol 5:110PubMedCentralPubMedCrossRefGoogle Scholar
  72. 72.
    Yizhak K, Gaude E, Le Devedec S et al (2014) Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. eLife 3:e03641Google Scholar
  73. 73.
    Wang Y, Eddy JA, Price ND (2012) Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol 6:153PubMedCentralPubMedCrossRefGoogle Scholar
  74. 74.
    Blazier AS, Papin JA (2012) Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 3:299PubMedCentralPubMedCrossRefGoogle Scholar
  75. 75.
    Shlomi T (2010) Metabolic network-based interpretation of gene expression data elucidates human cellular metabolism. Biotechnol Genet Eng Rev 26:281–296PubMedCrossRefGoogle Scholar
  76. 76.
    Vlassis N, Pacheco MP, Sauter T (2014) Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol 10:e1003424PubMedCentralPubMedCrossRefGoogle Scholar
  77. 77.
    Antonucci R, Pilloni MD, Atzori L et al (2012) Pharmaceutical research and metabolomics in the newborn. J Matern Fetal Neonatal Med 25:22–26PubMedCrossRefGoogle Scholar
  78. 78.
    Schmidt BJ, Ebrahim A, Metz TO et al (2013) GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. Bioinformatics 29:2900–2908PubMedCentralPubMedCrossRefGoogle Scholar
  79. 79.
    Fleming RMT, Thiele I, Nasheuer HP (2009) Quantitative assignment of reaction directionality in constraint-based models of metabolism: application to Escherichia coli. Biophys Chem 145:47–56PubMedCentralPubMedCrossRefGoogle Scholar
  80. 80.
    Yizhak K, Benyamini T, Liebermeister W et al (2010) Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 26:i255–i260PubMedCentralPubMedCrossRefGoogle Scholar
  81. 81.
    Kummel A, Panke S, Heinemann M (2006) Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics 7:512PubMedCentralPubMedCrossRefGoogle Scholar
  82. 82.
    Ahn SY, Jamshidi N, Mo ML et al (2011) Linkage of organic anion transporter-1 to metabolic pathways through integrated “omics”-driven network and functional analysis. J Biol Chem 286:31522–31531PubMedCentralPubMedCrossRefGoogle Scholar
  83. 83.
    Fan J, Kamphorst JJ, Mathew R et al (2013) Glutamine-driven oxidative phosphorylation is a major ATP source in transformed mammalian cells in both normoxia and hypoxia. Mol Syst Biol 9:712PubMedCentralPubMedCrossRefGoogle Scholar
  84. 84.
    Cakir T, Patil KR, Onsan Z et al (2006) Integration of metabolome data with metabolic networks reveals reporter reactions. Mol Syst Biol 2:50PubMedCentralPubMedCrossRefGoogle Scholar
  85. 85.
    Allen J, Davey HM, Broadhurst D et al (2004) Discrimination of modes of action of antifungal substances by use of metabolic footprinting. Appl Environ Microbiol 70:6157–6165PubMedCentralPubMedCrossRefGoogle Scholar
  86. 86.
    Allen J, Davey HM, Broadhurst D et al (2003) High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat Biotechnol 21:692–696PubMedCrossRefGoogle Scholar
  87. 87.
    Warburg O (1956) On the origin of cancer cells. Science 123:309–314PubMedCrossRefGoogle Scholar
  88. 88.
    Resendis-Antonio O, Checa A, Encarnacion S (2010) Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect. PLoS One 5:e12383PubMedCentralPubMedCrossRefGoogle Scholar
  89. 89.
    Tedeschi PM, Markert EK, Gounder M et al (2013) Contribution of serine, folate and glycine metabolism to the ATP, NADPH and purine requirements of cancer cells. Cell Death Dis 4:e877PubMedCentralPubMedCrossRefGoogle Scholar
  90. 90.
    Vazquez A, Markert EK, Oltvai ZN (2011) Serine biosynthesis with one carbon catabolism and the glycine cleavage system represents a novel pathway for ATP generation. PLoS One 6:e25881PubMedCentralPubMedCrossRefGoogle Scholar
  91. 91.
    Frezza C, Zheng L, Folger O et al (2011) Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477:225–228PubMedCrossRefGoogle Scholar
  92. 92.
    Jerby L, Wolf L, Denkert C et al (2012) Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Res 72:5712–5720PubMedCrossRefGoogle Scholar
  93. 93.
    Shlomi T, Benyamini T, Gottlieb E et al (2011) Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. PLoS Comput Biol 7:e1002018PubMedCentralPubMedCrossRefGoogle Scholar
  94. 94.
    Bordbar A, Monk JM, King ZA et al (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107–120PubMedCrossRefGoogle Scholar
  95. 95.
    Lewis NE, Abdel-Haleem AM (2013) The evolution of genome-scale models of cancer metabolism. Front Physiol 4:237PubMedCentralPubMedGoogle Scholar
  96. 96.
    Masoudi-Nejad A, Asgari Y (2014) Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment. Semin Cancer Biol 30C:21–29Google Scholar
  97. 97.
    Vazquez A, Liu J, Zhou Y et al (2010) Catabolic efficiency of aerobic glycolysis: the Warburg effect revisited. BMC Syst Biol 4:58PubMedCentralPubMedCrossRefGoogle Scholar
  98. 98.
    Vazquez A, Oltvai ZN (2011) Molecular crowding defines a common origin for the Warburg effect in proliferating cells and the lactate threshold in muscle physiology. PLoS One 6:e19538PubMedCentralPubMedCrossRefGoogle Scholar
  99. 99.
    Pampols T (2010) Inherited metabolic rare disease. Adv Exp Med Biol 686:397–431PubMedCrossRefGoogle Scholar
  100. 100.
    Levy HL (2010) Newborn screening conditions: what we know, what we do not know, and how we will know it. Genet Med 12:S213–S214PubMedCrossRefGoogle Scholar
  101. 101.
    Seymour CA, Thomason MJ, Chalmers RA et al (1997) Newborn screening for inborn errors of metabolism: a systematic review. Health Technol Assess 1:84–95Google Scholar
  102. 102.
    Lanpher B, Brunetti-Pierri N, Lee B (2006) Inborn errors of metabolism: the flux from Mendelian to complex diseases. Nat Rev Genet 7:449–460PubMedCrossRefGoogle Scholar
  103. 103.
    Vockley J (2008) Metabolism as a complex genetic trait, a systems biology approach: implications for inborn errors of metabolism and clinical diseases. J Inherit Metab Dis 31:619–629PubMedCentralPubMedCrossRefGoogle Scholar
  104. 104.
    Fernandes J (2006) Inborn metabolic diseases: diagnosis and treatment, 4th edn. Springer, HeidelbergCrossRefGoogle Scholar
  105. 105.
    Becroft DM, Phillips LI (1965) Hereditary orotic aciduria and megaloblastic anaemia: a second case, with response to uridine. Br Med J 1:547–552PubMedCentralPubMedCrossRefGoogle Scholar
  106. 106.
    Becroft DM, Phillips LI, Simmonds A (1969) Hereditary orotic aciduria: long-term therapy with uridine and a trial of uracil. J Pediatr 75:885–891PubMedCrossRefGoogle Scholar
  107. 107.
    Jamshidi N, Miller FJ, Mandel J et al (2011) Individualized therapy of HHT driven by network analysis of metabolomic profiles. BMC Syst Biol 5:200PubMedCentralPubMedCrossRefGoogle Scholar
  108. 108.
    Bairoch A, Apweiler R, Wu CH et al (2005) The universal protein resource (UniProt). Nucleic Acids Res 33:D154–D159PubMedCentralPubMedCrossRefGoogle Scholar
  109. 109.
    Thiele I, Palsson BØ (2010) Reconstruction annotation jamborees: a community approach to systems biology. Mol Syst Biol 6:361PubMedCentralPubMedCrossRefGoogle Scholar
  110. 110.
    Suhre K, Wallaschofski H, Raffler J et al (2011) A genome-wide association study of metabolic traits in human urine. Nat Genet 43:565–569PubMedCrossRefGoogle Scholar
  111. 111.
    Krug S, Kastenmuller G, Stuckler F et al (2012) The dynamic range of the human metabolome revealed by challenges. FASEB J 26:2607–2619PubMedCrossRefGoogle Scholar
  112. 112.
    Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:1–15CrossRefGoogle Scholar
  113. 113.
    Gianchandani EP, Oberhardt MA, Burgard AP et al (2008) Predicting biological system objectives de novo from internal state measurements. BMC Bioinformatics 9:43PubMedCentralPubMedCrossRefGoogle Scholar
  114. 114.
    Price ND, Schellenberger J, Palsson BØ (2004) Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J 87:2172–2186PubMedCentralPubMedCrossRefGoogle Scholar
  115. 115.
    Akesson M, Forster J, Nielsen J (2004) Integration of gene expression data into genome-scale metabolic models. Metab Eng 6:285–293PubMedCrossRefGoogle Scholar
  116. 116.
    Zur H, Ruppin E, Shlomi T (2010) iMAT: an integrative metabolic analysis tool. Bioinformatics 26:3140–3142PubMedCrossRefGoogle Scholar
  117. 117.
    Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. PNAS 107:17845–17850PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Luxembourg Center for Systems BiomedicineUniversity of LuxembourgEsch-sur-alzetteLuxembourg

Personalised recommendations