Skip to main content

Integration of Metabolomics and Flux Balance Analysis: Applications and Challenges

  • Chapter
  • First Online:
Advances in Bioinformatics
  • 462 Accesses

Abstract

This book chapter presents an in-depth analysis of the integration of metabolomics and flux balance analysis (FBA) as powerful tools for understanding metabolic processes and their applications in various scientific disciplines. The potential applications of metabolomics in these fields were discussed, highlighting the valuable insights it offers into metabolic pathways and networks. The subsequent sections delve into the different techniques employed in metabolomics research, including targeted and untargeted approaches using “LC–MS, GC–MS, and NMR”. The chapter also explores important tools utilized in flux balance analysis, such as OptKnock, OptGene, OptStrain, COBRA Tools, MetaboAnalyst 4.0, OptFlux, CellNetAnalyzer, SBRT, and Escher-FBA. Furthermore, the chapter discusses metabolomics integration using FBA and highlights the methodologies for identifying and annotating metabolites, including the use of metabolite databases and spectral libraries. The integration of metabolomics data with genome-scale metabolic models was explored, along with the estimation of metabolic fluxes from metabolomics data using the “Constraint-Based Reconstruction and Analysis (COBRA) Toolbox”. The chapter presents case studies and applications that demonstrate the utility of metabolomics and FBA in various contexts, including therapeutic and diagnostic applications. It explores the application of metabolomics in blood, urine, and saliva, highlighting their potential as non-invasive diagnostic tools. Moreover, the chapter addresses the challenges and limitations associated with integrating metabolomics and FBA, providing insights into future perspectives and directions for further research.

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

Access this chapter

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
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

  • Agarwal P, Goyal A (2017) Ionization sources used in mass spectroscopy: a review

    Google Scholar 

  • Allen F, Pon A, Wilson M, Greiner R, Wishart D (2014) CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic acids Res 42(W1):W94–W99

    Article  CAS  PubMed Central  Google Scholar 

  • Alygizakis N, Lestremau F, Gago-Ferrero P, Gil-Solsona R, Arturi K, Hollender J, Thomaidis NS (2023) Towards a harmonized identification scoring system in LC-HRMS/MS based non-target screening (NTS) of emerging contaminants. TrAC Trends Anal Chem 159:116944

    Article  CAS  Google Scholar 

  • Amer B, Deshpande RR, Bird SS (2023) Simultaneous quantitation and discovery (SQUAD) analysis: combining the best of targeted and untargeted mass spectrometry-based metabolomics. Meta 13(5):648

    CAS  Google Scholar 

  • Anderson R, Groundwater PW, Todd A, Worsley A (2012) Antibacterial agents: chemistry, mode of action, mechanisms of resistance and clinical applications. Wiley, New York

    Book  Google Scholar 

  • Antoniewicz MR (2018) A guide to 13C metabolic flux analysis for the cancer biologist. Exp Mol Med 50(4):1–13

    Article  CAS  Google Scholar 

  • Aurich MK, Fleming RM, Thiele I (2016) MetaboTools: a comprehensive toolbox for analysis of genome-scale metabolic models. Front Physiol 7:327

    Article  PubMed Central  Google Scholar 

  • Awlia M, Alshareef N, Saber N, Korte A, Oakey H, Panzarová K, Julkowska MM (2021) Genetic mapping of the early responses to salt stress in Arabidopsis thaliana. Plant J 107(2):544–563

    Article  CAS  Google Scholar 

  • Badilita V, Meier RC, Spengler N, Wallrabe U, Utz M, Korvink JG (2012) Microscale nuclear magnetic resonance: a tool for soft matter research. Soft Matter 8(41):10583–10597

    Article  CAS  Google Scholar 

  • Bartle KD, Myers P (2002) History of gas chromatography. TrAC Trends Anal Chem 21(9–10):547–557

    Article  CAS  Google Scholar 

  • Becker SA, Feist AM, Mo ML, Hannum G, Palsson BØ, Herrgard MJ (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox. Nat Protoc 2(3):727–738

    Article  CAS  Google Scholar 

  • Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K, Zardecki C (2002) The protein data bank. Acta Crystallogr D Biol Crystallogr 58(6):899–907

    Article  Google Scholar 

  • Böttcher C, Roepenack-Lahaye EV, Willscher E, Scheel D, Clemens S (2007) Evaluation of matrix effects in metabolite profiling based on capillary liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry. Anal Chem 79(4):1507–1513

    Article  Google Scholar 

  • Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R (2015) Metabolomics for laboratory diagnostics. J Pharm Biomed Anal 113:108–120

    Article  CAS  Google Scholar 

  • Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647–657

    Article  CAS  Google Scholar 

  • Calderón-Santiago M, López-Bascón MA, Peralbo-Molina A, Priego-Capote F (2017) MetaboQC: a tool for correcting untargeted metabolomics data with mass spectrometry detection using quality controls. Talanta 174:29–37

    Article  Google Scholar 

  • Canelas AB, van Gulik WM, Heijnen JJ (2008) Determination of the cytosolic free NAD/NADH ratio in Saccharomyces cerevisiae under steady-state and highly dynamic conditions. Biotechnol Bioeng 100(4):734–743

    Article  CAS  Google Scholar 

  • Castillo S, Gopalacharyulu P, Yetukuri L, Orešič M (2011) Algorithms and tools for the preprocessing of LC–MS metabolomics data. Chemom Intell Lab Syst 108(1):23–32

    Article  CAS  Google Scholar 

  • Cheng Q (ed) (2012) Microbial metabolic engineering: methods and protocols, vol 834. Humana Press

    Google Scholar 

  • Cheng JH, Dai Q, Sun DW, Zeng XA, Liu D, Pu HB (2013) Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends Food Sci Technol 34(1):18–31

    Article  CAS  Google Scholar 

  • Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, Xia J (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46(W1):W486–W494

    Article  CAS  PubMed Central  Google Scholar 

  • Colijn C, Brandes A, Zucker J, Lun DS, Weiner B (2009) Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 5:e1000489

    Article  PubMed Central  Google Scholar 

  • Coquin L, Feala JD, McCulloch AD, Paternostro G (2008) Metabolomic and flux-balance analysis of age-related decline of hypoxia tolerance in Drosophila muscle tissue. Mol Syst Biol 4(1):233

    Article  PubMed Central  Google Scholar 

  • Cova MAMN, Castagnola M, Messana I, Cabras T, Ferreira RMP, Amado FML, Vitorino RMP (2015) Salivary omics. In: Advances in salivary diagnostics. Springer, pp 63–82

    Chapter  Google Scholar 

  • Croasmun WR, Carlson RM (eds) (1996) Two-dimensional NMR spectroscopy: applications for chemists and biochemists, vol 15. Wiley, New York

    Google Scholar 

  • Cui L, Lu H, Lee YH (2018) Challenges and emergent solutions for LC–MS/MS based untargeted metabolomics in diseases. Mass Spectrom Rev 37(6):772–792

    Article  CAS  Google Scholar 

  • da Silva RR, Wang M, Nothias LF, van der Hooft JJ, Caraballo-Rodríguez AM, Fox E, Dorrestein PC (2018) Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput Biol 14(4):e1006089

    Article  PubMed Central  Google Scholar 

  • Dettmer K, Aronov PA, Hammock BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26(1):51–78

    Article  CAS  PubMed Central  Google Scholar 

  • Dowling G (2017) Analysis of bitterness compounds by mass spectrometry. In: Bitterness: perception, chemistry and food processing. Wiley, New York, pp 161–194

    Chapter  Google Scholar 

  • Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: constraints-based reconstruction and analysis for python. BMC Syst Biol 7:1–6

    Article  Google Scholar 

  • Fabian CJ, Kimler BF, Hursting SD (2015) Omega-3 fatty acids for breast cancer prevention and survivorship. Breast Cancer Res 17(1):1–11

    Article  CAS  Google Scholar 

  • Fabregat A, Sidiropoulos K, Viteri G, Forner O, Marin-Garcia P, Arnau V, Hermjakob H (2017) Reactome pathway analysis: a high-performance in-memory approach. BMC Bioinform 18(1):1–9

    Article  Google Scholar 

  • Fan TWM, Lane AN (2016) Applications of NMR spectroscopy to systems biochemistry. Prog Nucl Magn Reson Spectrosc 92:18–53

    Article  Google Scholar 

  • Feala JD, Coquin L, Zhou D, Haddad GG, Paternostro G, McCulloch AD (2009) Metabolism as means for hypoxia adaptation: metabolic profiling and flux balance analysis. BMC Syst Biol 3(1):1–15

    Article  Google Scholar 

  • Fiehn O (2016) Metabolomics by gas chromatography–mass spectrometry: combined targeted and untargeted profiling. Curr Protoc Mol Biol 114(1):30–34

    Article  PubMed Central  Google Scholar 

  • Förster J, Famili I, Fu P, Palsson BØ, Nielsen J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13(2):244–253

    Article  PubMed Central  Google Scholar 

  • Freire M, Nelson KE, Edlund A (2021) The oral host–microbial interactome: an ecological chronometer of health? Trends Microbiol 29(6):551–561

    Article  CAS  Google Scholar 

  • Fuerstenau SD, Benner WH (1995) Molecular weight determination of megadalton DNA electrospray ions using charge detection time-of-flight mass spectrometry. Rapid Commun Mass Spectrom 9(15):1528–1538

    Article  CAS  Google Scholar 

  • Gerstl MP, Ruckerbauer DE, Mattanovich D, Jungreuthmayer C, Zanghellini J (2015) Metabolomics integrated elementary flux mode analysis in large metabolic networks. Sci Rep 5(1):1–8

    Article  Google Scholar 

  • González-Domínguez R, Sayago A, Fernández-Recamales Á (2017) Metabolomics in Alzheimer’s disease: the need of complementary analytical platforms for the identification of biomarkers to unravel the underlying pathology. J Chromatogr B 1071:75–92

    Article  Google Scholar 

  • Gowda GN, Raftery D (2015) Can NMR solve some significant challenges in metabolomics? J Magn Reson 260:144–160

    Article  PubMed Central  Google Scholar 

  • Griffin JL, Bollard ME (2004) Metabonomics: its potential as a tool in toxicology for safety assessment and data integration. Curr Drug Metab 5(5):389–398

    Article  CAS  Google Scholar 

  • Griffiths WJ, Wang Y (2009) Mass spectrometry: from proteomics to metabolomics and lipidomics. Chem Soc Rev 38(7):1882–1896

    Article  CAS  Google Scholar 

  • Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, Siuzdak G (2018) METLIN: a technology platform for identifying knowns and unknowns. Anal Chem 90(5):3156–3164

    Article  CAS  PubMed Central  Google Scholar 

  • Guilhaus M (1995) Special feature: tutorial. Principles and instrumentation in time-of-flight mass spectrometry. Physical and instrumental concepts. J Mass Spectrom 30(11):1519–1532

    Article  CAS  Google Scholar 

  • Haug K, Salek RM, Steinbeck C (2017) Global open data management in metabolomics. Curr Opin Chem Biol 36:58–63

    Article  CAS  PubMed Central  Google Scholar 

  • Heck M, Blaum K, Cakirli RB, Rodríguez D, Schweikhard L, Stahl S, Ubieto-Díaz M (2011) Dipolar and quadrupolar detection using an FT-ICR MS setup at the MPIK Heidelberg. Hyperfine Interact 199:347–355

    Article  CAS  Google Scholar 

  • Hird SJ, Lau BPY, Schuhmacher R, Krska R (2014) Liquid chromatography-mass spectrometry for the determination of chemical contaminants in food. TrAC Trends Anal Chem 59:59–72

    Article  CAS  Google Scholar 

  • Hucka M, Bergmann FT, Dräger A, Hoops S, Keating SM, Le Novere N, Wilkinson DJ (2015) Systems biology markup language (SBML) level 2 version 5: structures and facilities for model definitions. J Integr Bioinform 12(2):731–901

    Article  Google Scholar 

  • Hurd RE, Yen YF, Chen A, Ardenkjaer-Larsen JH (2012) Hyperpolarized 13C metabolic imaging using dissolution dynamic nuclear polarization. J Magn Reson Imaging 36(6):1314–1328

    Article  Google Scholar 

  • Jalili V, Barkhordari A, Ghiasvand A (2020) Solid-phase microextraction technique for sampling and preconcentration of polycyclic aromatic hydrocarbons: a review. Microchem J 157:104967

    Article  CAS  Google Scholar 

  • Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6(1):401

    Article  PubMed Central  Google Scholar 

  • Jobard E, Pontoizeau C, Blaise BJ, Bachelot T, Elena-Herrmann B, Trédan O (2014) A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer. Cancer Lett 343(1):33–41

    Article  CAS  Google Scholar 

  • Johar D, Elmehrath AO, Khalil RM, Elberry MH, Zaky S, Shalabi SA, Bernstein LH (2021) Protein networks linking Warburg and reverse Warburg effects to cancer cell metabolism. Biofactors 47(5):713–728

    Article  CAS  Google Scholar 

  • Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed Central  Google Scholar 

  • Kanehisa M, Sato Y, Kawashima M (2022) KEGG mapping tools for uncovering hidden features in biological data. Protein Sci 31(1):47–53

    Article  CAS  Google Scholar 

  • Karp PD, Billington R, Caspi R, Fulcher CA, Latendresse M, Kothari A, Subhraveti P (2019) The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform 20(4):1085–1093

    Article  CAS  Google Scholar 

  • Karpievitch YV, Polpitiya AD, Anderson GA, Smith RD, Dabney AR (2010) Liquid chromatography mass spectrometry-based proteomics: biological and technological aspects. Ann Appl Stat 4(4):1797–1823

    Article  PubMed Central  Google Scholar 

  • Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14(5):491–496

    Article  CAS  Google Scholar 

  • Kitano H (2004) Biological robustness. Nat Rev Genet 5(11):826–837

    Article  CAS  Google Scholar 

  • Klamt S, Saez-Rodriguez J, Gilles ED (2007) Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol 1(1):1–13

    Article  Google Scholar 

  • Kobayashi T, Nishiumi S, Ikeda A, Yoshie T, Sakai A, Matsubara A, Yoshida M (2013) A novel serum metabolomics-based diagnostic approach to pancreatic CancerSerum Metabolomic analysis of pancreatic cancer. Cancer Epidemiol Biomark Prev 22(4):571–579

    Article  CAS  Google Scholar 

  • Koek MM, Jellema RH, van der Greef J, Tas AC, Hankemeier T (2011) Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives. Metabolomics 7:307–328

    Article  CAS  Google Scholar 

  • Krishnan SN, Sun YA, Mohsenin A, Wyman RJ, Haddad GG (1997) Behavioral and electrophysiologic responses of Drosophila melanogaster to prolonged periods of anoxia. J Insect Physiol 43(3):203–210

    Article  CAS  Google Scholar 

  • Kueger S, Steinhauser D, Willmitzer L, Giavalisco P (2012) High-resolution plant metabolomics: from mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions. Plant J 70(1):39–50

    Article  CAS  Google Scholar 

  • Lane AN, Fan TWM, Higashi RM (2008) Isotopomer-based metabolomic analysis by NMR and mass spectrometry. Methods Cell Biol 84:541–588

    Article  CAS  Google Scholar 

  • Lee DY, Yun H, Park S, Lee SY (2003) MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics 19(16):2144–2146

    Article  CAS  Google Scholar 

  • Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10(4):291–305

    Article  CAS  PubMed Central  Google Scholar 

  • Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Laibe C (2010) BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4(1):1–14

    Article  Google Scholar 

  • Liu T, Peng XC, Li B (2019) The metabolic profiles in hematological malignancies. Indian J Hematol Blood Transfus 35:625–634

    Article  PubMed Central  Google Scholar 

  • Lu X, Zhao X, Bai C, Zhao C, Lu G, Xu G (2008) LC–MS-based metabonomics analysis. J Chromatogr B 866(1–2):64–76

    Article  CAS  Google Scholar 

  • Mahadevan R, Edwards JS, Doyle FJ (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83(3):1331–1340

    Article  CAS  PubMed Central  Google Scholar 

  • Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, Wishart DS (2017) The future of NMR-based metabolomics. Curr Opin Biotechnol 43:34–40

    Article  CAS  Google Scholar 

  • McNair HM, Miller JM, Snow NH (2019) Basic gas chromatography. Wiley, New York

    Book  Google Scholar 

  • Modisha PM, Jordaan JH, Bösmann A, Wasserscheid P, Bessarabov D (2018) Analysis of reaction mixtures of perhydro-dibenzyltoluene using two-dimensional gas chromatography and single quadrupole gas chromatography. Int J Hydrog Energy 43(11):5620–5636

    Article  CAS  Google Scholar 

  • Monteiro M, Carvalho M, Henrique R, Jeronimo C, Moreira N, de Lourdes Bastos M, de Pinho PG (2014) Analysis of volatile human urinary metabolome by solid-phase microextraction in combination with gas chromatography–mass spectrometry for biomarker discovery: application in a pilot study to discriminate patients with renal cell carcinoma. Eur J Cancer 50(11):1993–2002

    Article  CAS  Google Scholar 

  • Morain BÉV (2013) In-situ and operando infrared investigations on supported ionic liquid-and ionic liquid crystal-based catalytic materials. Friedrich-Alexander-Universitaet Erlangen-Nuernberg (Germany)

    Google Scholar 

  • Nagana Gowda GA, Raftery D (2019) Overview of NMR spectroscopy-based metabolomics: opportunities and challenges. In: NMR-based metabolomics: methods and protocols. Springer, pp 3–14

    Chapter  Google Scholar 

  • Narad P, Naresh G, Sengupta A (2022) Metabolomics and flux balance analysis. In: Bioinformatics. Academic Press, pp 337–365

    Chapter  Google Scholar 

  • Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y (2022) Constraint-based reconstruction and analyses of metabolic models: open-source python tools and applications to cancer. Front Oncol 12:914594

    Article  CAS  PubMed Central  Google Scholar 

  • Nielsen J, Jewett MC (eds) (2007) Metabolomics: a powerful tool in systems biology, vol 18. Springer Science & Business Media, Berlin

    Google Scholar 

  • O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities. Cell 161(5):971–987

    Article  PubMed Central  Google Scholar 

  • O’Grady J, Schwender J, Shachar-Hill Y, Morgan JA (2012) Metabolic cartography: experimental quantification of metabolic fluxes from isotopic labelling studies. J Exp Bot 63(6):2293–2308

    Article  Google Scholar 

  • Oftadeh O, Salvy P, Masid M, Curvat M, Miskovic L, Hatzimanikatis V (2021) A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics. Nat Commun 12(1):4790

    Article  CAS  PubMed Central  Google Scholar 

  • Parkhitko AA, Jouandin P, Mohr SE, Perrimon N (2019) Methionine metabolism and methyltransferases in the regulation of aging and lifespan extension across species. Aging Cell 18(6):e13034

    Article  CAS  PubMed Central  Google Scholar 

  • Pitt JJ (2009) Principles and applications of liquid chromatography–mass spectrometry in clinical biochemistry. Clin Biochem Rev 30(1):19–34

    PubMed Central  Google Scholar 

  • Poole CF (2015) Ionization-based detectors for gas chromatography. J Chromatogr A 1421:137–153

    Article  CAS  Google Scholar 

  • Pozo ÓJ, Sancho JV, Ibáñez M, Hernández F, Niessen WM (2006) Confirmation of organic micropollutants detected in environmental samples by liquid chromatography tandem mass spectrometry: achievements and pitfalls. TrAC Trends Anal Chem 25(10):1030–1042

    Article  CAS  Google Scholar 

  • Putri SP, Nakayama Y, Matsuda F, Uchikata T, Kobayashi S, Matsubara A, Fukusaki E (2013) Current metabolomics: practical applications. J Biosci Bioeng 115(6):579–589

    Article  CAS  Google Scholar 

  • Quek LE, Wittmann C, Nielsen LK, Krömer JO (2009) OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis. Microb Cell Factories 8:1–15

    Article  Google Scholar 

  • Ràfols P, Vilalta D, Brezmes J, Cañellas N, Del Castillo E, Yanes O, Correig X (2018) Signal preprocessing, multivariate analysis and software tools for MA (LDI)-TOF mass spectrometry imaging for biological applications. Mass Spectrom Rev 37(3):281–306

    Article  Google Scholar 

  • Raman K, Chandra N (2009) Flux balance analysis of biological systems: applications and challenges. Brief Bioinform 10(4):435–449

    Article  CAS  Google Scholar 

  • Redestig H, Szymanski J, Hirai MY, Selbig J, Willmitzer L, Nikoloski Z, Saito K (2011) Data integration, metabolic networks and systems biology. Annu Plant Rev Biol Plant Metabol 43:261–316

    CAS  Google Scholar 

  • Riekeberg E, Powers R (2017) New frontiers in metabolomics: from measurement to insight. F1000Research 6:1148

    Article  PubMed Central  Google Scholar 

  • Riley ML, Schmidt T, Artamonova II, Wagner C, Volz A, Heumann K, Frishman D (2007) PEDANT genome database: 10 years online. Nucleic Acids Res 35(suppl_1):D354–D357

    Article  CAS  Google Scholar 

  • Rocha I, Maia P, Evangelista P, Vilaça P, Soares S, Pinto JP, Rocha M (2010) OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst Biol 4(1):1–12

    Article  Google Scholar 

  • Romero R, Espinoza J, Gotsch F, Kusanovic JP, Friel LA, Erex O, Tromp G (2008) The use of high-dimensional biology (genomics, transcriptomics, proteomics, and metabolomics) to understand the preterm parturition syndrome. BJOG 113(Suppl. 3):118–135

    Google Scholar 

  • Rouger L, Gouilleux B, Nantes FPG (2017) Fast n-dimensional data acquisition methods

    Google Scholar 

  • Ruttkies C, Neumann S, Posch S (2019) Improving MetFrag with statistical learning of fragment annotations. BMC Bioinform 20(1):1–14

    Article  CAS  Google Scholar 

  • Sato S, Soga T, Nishioka T, Tomita M (2004) Simultaneous determination of the main metabolites in rice leaves using capillary electrophoresis mass spectrometry and capillary electrophoresis diode array detection. Plant J 40(1):151–163

    Article  CAS  Google Scholar 

  • Sauer UWE, Lasko DR, Fiaux J, Hochuli M, Glaser R, Szyperski T, Bailey JE (1999) Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism. J Bacteriol 181(21):6679–6688

    Article  CAS  PubMed Central  Google Scholar 

  • Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Palsson BØ (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6(9):1290–1307

    Article  CAS  PubMed Central  Google Scholar 

  • Schmidt BJ, Ebrahim A, Metz TO, Adkins JN, Palsson BØ, Hyduke DR (2013) GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. Bioinformatics 29(22):2900–2908

    Article  CAS  PubMed Central  Google Scholar 

  • Schomburg I, Chang A, Schomburg D (2002) BRENDA, enzyme data and metabolic information. Nucleic Acids Res 30(1):47–49

    Article  CAS  PubMed Central  Google Scholar 

  • Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3(1):119

    Article  PubMed Central  Google Scholar 

  • Schwarz R, Musch P, von Kamp A, Engels B, Schirmer H, Schuster S, Dandekar T (2005) YANA—a software tool for analyzing flux modes, gene-expression and enzyme activities. BMC Bioinform 6:1–12

    Article  Google Scholar 

  • Seger C, Sturm S, Stuppner H (2013) Mass spectrometry and NMR spectroscopy: modern high-end detectors for high resolution separation techniques–state of the art in natural product HPLC-MS, HPLC-NMR, and CE-MS hyphenations. Nat Prod Rep 30(7):970–987

    Article  CAS  Google Scholar 

  • Shamsipur M, Naseri MT, Babri M (2013) Quantification of candidate prostate cancer metabolite biomarkers in urine using dispersive derivatization liquid–liquid microextraction followed by gas and liquid chromatography–mass spectrometry. J Pharm Biomed Anal 81:65–75

    Article  Google Scholar 

  • Sibille N, Bellot G, Wang J, Déméné H (2012) Low concentration of a Gd-chelate increases the signal-to-noise ratio in fast pulsing BEST experiments. J Magn Reson 224:32–37

    Article  CAS  Google Scholar 

  • Sleno L (2012) The use of mass defect in modern mass spectrometry. J Mass Spectrom 47(2):226–236

    Article  CAS  Google Scholar 

  • Smart KF, Aggio RB, Van Houtte JR, Villas-Bôas SG (2010) Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography–mass spectrometry. Nat Protoc 5(10):1709–1729

    Article  CAS  Google Scholar 

  • Stein S (2012) Mass spectral reference libraries: an ever-expanding resource for chemical identification. Anal Chem 84:7274

    Article  CAS  Google Scholar 

  • Stelling J (2004) Mathematical models in microbial systems biology. Curr Opin Microbiol 7(5):513–518

    Article  Google Scholar 

  • Stephens NS, Siffledeen J, Su X, Murdoch TB, Fedorak RN, Slupsky CM (2013) Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. J Crohns Colitis 7(2):e42–e48

    Article  Google Scholar 

  • Struck W, Siluk D, Yumba-Mpanga A, Markuszewski M, Kaliszan R, Markuszewski MJ (2013) Liquid chromatography tandem mass spectrometry study of urinary nucleosides as potential cancer markers. J Chromatogr A 1283:122–131

    Article  CAS  Google Scholar 

  • Struck-Lewicka W, Kaliszan R, Markuszewski MJ (2014) Analysis of urinary nucleosides as potential cancer markers determined using LC–MS technique. J Pharm Biomed Anal 101:50–57

    Article  CAS  Google Scholar 

  • Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M (2012) Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Curr Bioinform 7(1):96–108

    Article  CAS  PubMed Central  Google Scholar 

  • Tavares LC, Jarak I, Nogueira FN, Oliveira PJ, Carvalho RA (2015) Metabolic evaluations of cancer metabolism by NMR-based stable isotope tracer methodologies. Eur J Clin Investig 45:37–43

    Article  CAS  Google Scholar 

  • Teusink B, Passarge J, Reijenga CA, Esgalhado E, Weijden CC, van der Schepper M, Walsh MC, Bakker BM, van Dam K, Westerhoff HV, Snoep JL (2000) Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem 267(17):5313–5329

    Article  CAS  Google Scholar 

  • Töpfer N, Kleessen S, Nikoloski Z (2015) Integration of metabolomics data into metabolic networks. Front Plant Sci 6:49

    PubMed Central  Google Scholar 

  • Toya Y, Shimizu H (2013) Flux analysis and metabolomics for systematic metabolic engineering of microorganisms. Biotechnol Adv 31(6):818–826

    Article  CAS  Google Scholar 

  • Trimigno A, Marincola FC, Dellarosa N, Picone G, Laghi L (2015) Definition of food quality by NMR-based foodomics. Curr Opin Food Sci 4:99–104

    Article  Google Scholar 

  • Urbanczik R (2006) SNA—a toolbox for the stoichiometric analysis of metabolic networks. BMC Bioinform 7(1):1–4

    Article  Google Scholar 

  • Van Winden WA, Van Dam JC, Ras C, Kleijn RJ, Vinke JL, Van Gulik WM, Heijnen JJ (2005) Metabolic-flux analysis of Saccharomyces cerevisiae CEN.PK113-7D based on mass isotopomer measurements of 13C-labeled primary metabolites. FEMS Yeast Res 5(6–7):559–568

    Google Scholar 

  • Varma A, Palsson BO (1994) Metabolic flux balancing: basic concepts, scientific and practical use. Bio/Technology 12(10):994–998

    Article  CAS  Google Scholar 

  • Volkova S, Matos MR, Mattanovich M, Marín de Mas I (2020) Metabolic modelling as a framework for metabolomics data integration and analysis. Metabolites 10(8):303

    Article  CAS  PubMed Central  Google Scholar 

  • Wang X, Chen S, Jia W (2016) Metabolomics in cancer biomarker research. Curr Pharmacol Rep 2:293–298

    Article  CAS  Google Scholar 

  • Wang ZJ, Ohliger MA, Larson PE, Gordon JW, Bok RA, Slater J, Vigneron DB (2019) Hyperpolarized 13C MRI: state of the art and future directions. Radiology 291(2):273–284

    Article  Google Scholar 

  • Wang CY, Lempp M, Farke N, Donati S, Glatter T, Link H (2021) Metabolome and proteome analyses reveal transcriptional misregulation in glycolysis of engineered E. coli. Nat Commun 12(1):4929

    Article  CAS  PubMed Central  Google Scholar 

  • Wiechert W (2001) 13C metabolic flux analysis. Metab Eng 3(3):195–206

    Article  CAS  Google Scholar 

  • Willemsen AM, Hendrickx DM, Hoefsloot HC, Hendriks MM, Wahl SA, Teusink B, van Kampen AH (2015) MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Mol Biosyst 11(1):137–145

    Article  CAS  Google Scholar 

  • Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Querengesser L (2007) HMDB: the human metabolome database. Nucleic Acids Res 35(suppl_1):D521–D526

    Article  CAS  PubMed Central  Google Scholar 

  • Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, Gautam V (2022) HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res 50(D1):D622–D631

    Article  CAS  Google Scholar 

  • Wright J, Wagner A (2008) The systems biology research tool: evolvable open-source software. BMC Syst Biol 2:1–6

    Article  Google Scholar 

  • Xia J, Broadhurst DI, Wilson M, Wishart DS (2013) Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 9:280–299

    Article  CAS  Google Scholar 

  • Xiao JF, Zhou B, Ressom HW (2012) Metabolite identification and quantitation in LC–MS/MS-based metabolomics. TrAC Trends Anal Chem 32:1–14

    Article  Google Scholar 

  • Yao R, Li J, Feng L, Zhang X, Hu H (2019) 13C metabolic flux analysis-guided metabolic engineering of Escherichia coli for improved acetol production from glycerol. Biotechnol Biofuels 12(1):1–13

    Article  CAS  Google Scholar 

  • Yu D, Zhou L, Liu X, Xu G (2023) Stable isotope-resolved metabolomics based on mass spectrometry: methods and their applications. TrAC Trends Anal Chem 116985:116985

    Article  Google Scholar 

  • Zaikin V, Halket JM (2009) A handbook of derivatives for mass spectrometry. IM Publications

    Google Scholar 

  • Zeki ÖC, Eylem CC, Reçber T, Kır S, Nemutlu E (2020) Integration of GC–MS and LC–MS for untargeted metabolomics profiling. J Pharm Biomed Anal 190:113509

    Article  CAS  Google Scholar 

  • Zhang A, Sun H, Wang X (2012) Serum metabolomics as a novel diagnostic approach for disease: a systematic review. Anal Bioanal Chem 404:1239–1245

    Article  CAS  Google Scholar 

  • Zhang AH, Sun H, Qiu S, Wang XJ (2013) NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis. Magn Reson Chem 51(9):549–556

    Article  CAS  Google Scholar 

  • Zhang A, Sun H, Yan G, Wang P, Wang X (2015) Metabolomics for biomarker discovery: moving to the clinic. Biomed Res Int 2015:354671

    PubMed Central  Google Scholar 

  • Zhang Y, Cai J, Shang X, Wang B, Liu S, Chai X, Wen T (2017) A new genome-scale metabolic model of Corynebacterium glutamicum and its application. Biotechnol Biofuels 10:1–16

    Article  Google Scholar 

  • Zhou B, Xiao JF, Tuli L, Ressom HW (2012) LC–MS-based metabolomics. Mol BioSyst 8(2):470–481

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gholamreza Abdi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Abdi, G., Patil, N., Jain, M., Barwant, M. (2024). Integration of Metabolomics and Flux Balance Analysis: Applications and Challenges. In: Singh, V., Kumar, A. (eds) Advances in Bioinformatics. Springer, Singapore. https://doi.org/10.1007/978-981-99-8401-5_10

Download citation

Publish with us

Policies and ethics