Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kitano H (2001) Foundations of systems biology. MIT Press, Cambridge, MA
Machado D, Costa R, Rocha M et al (2011) Modeling formalisms in systems biology. AMB Express 1:45
Durot M, Bourguignon PY, Schachter V (2009) Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 33:164–190
Palsson BØ (2006) Systems biology: properties of reconstructed networks. Cambridge University Press, Cambridge
Aurich MK, Thiele I (2012) Contextualization procedure and modeling of monocyte specific TLR signaling. PLoS One 7:e49978
Li F, Thiele I, Jamshidi N, Palsson BØ (2009) Identification of potential pathway mediation targets in toll-like receptor signaling. PLoS Comput Biol 5:e1000292
Papin JA, Palsson BØ (2004) The JAK-STAT signaling network in the human B-cell: an extreme signaling pathway analysis. Biophys J 87:37–46
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:e1000312
Thorleifsson SG, Thiele I (2011) rBioNet: a COBRA toolbox extension for reconstructing high-quality biochemical networks. Bioinformatics 27:2009–2010
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
Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121
Reed JL, Famili I, Thiele I et al (2006) Towards multidimensional genome annotation. Nat Rev Genet 7:130–141
Sahoo S, Thiele I (2013) Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells. Hum Mol Genet 22:2705–2722
Folger O, Jerby L, Frezza C et al (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501
Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotech 28:245–248
Varma A, Palsson BØ (1994) Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotech 12:994–998
Terzer M, Maynard ND, Covert MW et al (2009) Genome-scale metabolic networks. Wiley Interdiscip Rev Syst Biol Med 1:285–297
Aurich M, Paglia G, Rolfsson Ó et al (2015) Prediction of intracellular metabolic states from extracellular metabolomic data. Metabolomics 11:603–619
Lewis NE, Nagarajan H, Palsson BØ (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305
Savinell JM, Palsson BØ (1992) Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism. J Theor Biol 154:421–454
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 279:39532–39540
Feist AM, Palsson BØ (2010) The biomass objective function. Curr Opin Microbiol 1:344–349
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 3:481
Duarte NC, Becker SA, Jamshidi N et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. PNAS 104:1777–1782
Thiele I, Swainston N, Fleming RMT et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425
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 5:180
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 280:11683–11695
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:e1000859
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 978-0-387-75888-6
Bordbar A, Lewis NE, Schellenberger J et al (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 6:422
Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276
Gudmundsson S, Thiele I (2010) Computationally efficient flux variability analysis. BMC Bioinformatics 11:489
Schellenberger J, Palsson BØ (2009) Use of randomized sampling for analysis of metabolic networks. J Biol Chem 284:5457–5461
Kaufman DE, Smith RL (1998) Direction choice for accelerated convergence in hit-and-run sampling. Oper Res 46:84–95
Becker SA, Palsson BØ (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 4:e1000082
Jerby L, Ruppin E (2012) Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clin Cancer Res 18:5572–5584
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 28:1279–1285
Bordbar A, Palsson BØ (2012) Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med 271:131–141
Shlomi T, Cabili MN, Ruppin E (2009) Predicting metabolic biomarkers of human inborn errors of metabolism. Mol Syst Biol 5:263
Rolfsson O, Palsson BØ, Thiele I (2011) The human metabolic reconstruction Recon 1 directs hypotheses of novel human metabolic functions. BMC Syst Biol 5:155
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
Heinken A, Sahoo S, Fleming RMT et al (2013) Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4:28–40
Ma H, Sorokin A, Mazein A et al (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol 3:135
Hao T, Ma HW, Zhao XM et al (2010) Compartmentalization of the Edinburgh human metabolic network. BMC Bioinformatics 11:393
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 6:411
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 5:3083
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:e1002518
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:e1002980
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 10:721
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 9:649
Sahoo S, Franzson L, Jonsson JJ et al (2012) A compendium of inborn errors of metabolism mapped onto the human metabolic network. Mol Biosyst 8:2545–2558
Jain M, Nilsson R, Sharma S et al (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336:1040–1044
Uhlen M, Oksvold P, Fagerberg L et al (2010) Towards a knowledge-based human protein atlas. Nat Biotech 28:1248–1250
Orth JD, Palsson B (2012) Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions. BMC Syst Biol 6:30
Thiele I, Vlassis N, Fleming RMT (2014) fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30:2529–2531
Wishart DS, Knox C, Guo AC et al (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37:D603–D610
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 5:91
Sahoo S, Haraldsdottir HS, Fleming RMT et al (2014) Modeling the effects of commonly used drugs on human metabolism. FEBS J 282:297–317
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:e1000489
Cox J, Mann M (2007) Is proteomics the new genomics? Cell 130:395–398
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–E875
Jamshidi N, Palsson BØ (2006) Systems biology of SNPs. Mol Syst Biol 2:38
Reed JL (2012) Shrinking the metabolic solution space using experimental datasets. PLoS Comput Biol 8:e1002662
Mo ML, Palsson BØ, Herrgard MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 3:37
Shlomi T, Cabili MN, Herrgard MJ et al (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26:1003–1010
Zhao Y, Huang J (2011) Reconstruction and analysis of human heart-specific metabolic network based on transcriptome and proteome data. Biochem Biophys Res Commun 415:450–454
Karlstadt A, Fliegner D, Kararigas G et al (2012) CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism. BMC Syst Biol 6:114
Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6:401
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:e1000938
Bordbar A, Mo ML, Nakayasu ES et al (2012) Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol Syst Biol 8:558
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 5:110
Yizhak K, Gaude E, Le Devedec S et al (2014) Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. eLife 3:e03641
Wang Y, Eddy JA, Price ND (2012) Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol 6:153
Blazier AS, Papin JA (2012) Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 3:299
Shlomi T (2010) Metabolic network-based interpretation of gene expression data elucidates human cellular metabolism. Biotechnol Genet Eng Rev 26:281–296
Vlassis N, Pacheco MP, Sauter T (2014) Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol 10:e1003424
Antonucci R, Pilloni MD, Atzori L et al (2012) Pharmaceutical research and metabolomics in the newborn. J Matern Fetal Neonatal Med 25:22–26
Schmidt BJ, Ebrahim A, Metz TO et al (2013) GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. Bioinformatics 29:2900–2908
Fleming RMT, Thiele I, Nasheuer HP (2009) Quantitative assignment of reaction directionality in constraint-based models of metabolism: application to Escherichia coli. Biophys Chem 145:47–56
Yizhak K, Benyamini T, Liebermeister W et al (2010) Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 26:i255–i260
Kummel A, Panke S, Heinemann M (2006) Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics 7:512
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 286:31522–31531
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 9:712
Cakir T, Patil KR, Onsan Z et al (2006) Integration of metabolome data with metabolic networks reveals reporter reactions. Mol Syst Biol 2:50
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 70:6157–6165
Allen J, Davey HM, Broadhurst D et al (2003) High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat Biotechnol 21:692–696
Warburg O (1956) On the origin of cancer cells. Science 123:309–314
Resendis-Antonio O, Checa A, Encarnacion S (2010) Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect. PLoS One 5:e12383
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:e877
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:e25881
Frezza C, Zheng L, Folger O et al (2011) Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477:225–228
Jerby L, Wolf L, Denkert C et al (2012) Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Res 72:5712–5720
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:e1002018
Bordbar A, Monk JM, King ZA et al (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107–120
Lewis NE, Abdel-Haleem AM (2013) The evolution of genome-scale models of cancer metabolism. Front Physiol 4:237
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:21–29
Vazquez A, Liu J, Zhou Y et al (2010) Catabolic efficiency of aerobic glycolysis: the Warburg effect revisited. BMC Syst Biol 4:58
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:e19538
Pampols T (2010) Inherited metabolic rare disease. Adv Exp Med Biol 686:397–431
Levy HL (2010) Newborn screening conditions: what we know, what we do not know, and how we will know it. Genet Med 12:S213–S214
Seymour CA, Thomason MJ, Chalmers RA et al (1997) Newborn screening for inborn errors of metabolism: a systematic review. Health Technol Assess 1:84–95
Lanpher B, Brunetti-Pierri N, Lee B (2006) Inborn errors of metabolism: the flux from Mendelian to complex diseases. Nat Rev Genet 7:449–460
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 31:619–629
Fernandes J (2006) Inborn metabolic diseases: diagnosis and treatment, 4th edn. Springer, Heidelberg
Becroft DM, Phillips LI (1965) Hereditary orotic aciduria and megaloblastic anaemia: a second case, with response to uridine. Br Med J 1:547–552
Becroft DM, Phillips LI, Simmonds A (1969) Hereditary orotic aciduria: long-term therapy with uridine and a trial of uracil. J Pediatr 75:885–891
Jamshidi N, Miller FJ, Mandel J et al (2011) Individualized therapy of HHT driven by network analysis of metabolomic profiles. BMC Syst Biol 5:200
Bairoch A, Apweiler R, Wu CH et al (2005) The universal protein resource (UniProt). Nucleic Acids Res 33:D154–D159
Thiele I, Palsson BØ (2010) Reconstruction annotation jamborees: a community approach to systems biology. Mol Syst Biol 6:361
Suhre K, Wallaschofski H, Raffler J et al (2011) A genome-wide association study of metabolic traits in human urine. Nat Genet 43:565–569
Krug S, Kastenmuller G, Stuckler F et al (2012) The dynamic range of the human metabolome revealed by challenges. FASEB J 26:2607–2619
Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:1–15
Gianchandani EP, Oberhardt MA, Burgard AP et al (2008) Predicting biological system objectives de novo from internal state measurements. BMC Bioinformatics 9:43
Price ND, Schellenberger J, Palsson BØ (2004) Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J 87:2172–2186
Akesson M, Forster J, Nielsen J (2004) Integration of gene expression data into genome-scale metabolic models. Metab Eng 6:285–293
Zur H, Ruppin E, Shlomi T (2010) iMAT: an integrative metabolic analysis tool. Bioinformatics 26:3140–3142
Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. PNAS 107:17845–17850
Acknowledgment
This study was supported by an ATTRACT program grant (FNR/A12/01) from the Luxembourg National Research Fund (FNR).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Aurich, M.K., Thiele, I. (2016). Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine. In: Schmitz, U., Wolkenhauer, O. (eds) Systems Medicine. Methods in Molecular Biology, vol 1386. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3283-2_12
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3283-2_12
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3282-5
Online ISBN: 978-1-4939-3283-2
eBook Packages: Springer Protocols