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The three-legged stool of understanding metabolism: integrating metabolomics with biochemical genetics and computational modeling

1 Department of Microbiology, University of Georgia, Athens, GA, 30602, USA
2 Department of Biological Sciences, University of Idaho, Moscow, ID, 83844, USA
3 Department of Biomedical Engineering, Georgia Institute of Technology, 950 Atlantic Drive, Suite 2115, Atlanta, GA, 30332-2000, USA

Topical Section: Genomics and Proteomics in Microbiology

Traditional biochemical research has resulted in a good understanding of many aspects of metabolism. However, this reductionist approach is time consuming and requires substantial resources, thus raising the question whether modern metabolomics and genomics should take over and replace the targeted experiments of old. We proffer that such a replacement is neither feasible not desirable and propose instead the tight integration of modern, system-wide omics with traditional experimental bench science and dedicated computational approaches. This integration is an important prerequisite toward the optimal acquisition of knowledge regarding metabolism and physiology in health and disease. The commentary describes advantages and drawbacks of current approaches to assessing metabolism and highlights the challenges to be overcome as we strive to achieve a deeper level of metabolic understanding in the future.
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Keywords genomics; metabolic pathway; metabolomics; systems analysis

Citation: Diana M. Downs, Jannell V. Bazurto, Anuj Gupta, Luis L. Fonseca, Eberhard O. Voit. The three-legged stool of understanding metabolism: integrating metabolomics with biochemical genetics and computational modeling. AIMS Microbiology, 2018, 4(2): 289-303. doi: 10.3934/microbiol.2018.2.289


  • 1. Griffin TJ, Gygi SP, Ideker T, et al. (2002) Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae. Mol Cell Proteomics 1: 323–333.    
  • 2. Castrillo JI, Zeef LA, Hoyle DC, et al. (2007) Growth control of the eukaryote cell: a systems biology study in yeast. J Biol 6: 4.    
  • 3. Bai Y, Wang S, Zhong H, et al. (2015) Integrative analyses reveal transcriptome-proteome correlation in biological pathways and secondary metabolism clusters in A. flavus in response to temperature. Sci Rep 5: 14582.
  • 4. Dyhrman ST, Jenkins BD, Rynearson TA, et al. (2012) The transcriptome and proteome of the diatom Thalassiosira pseudonana reveal a diverse phosphorus stress response. PLoS One 7: e33768.    
  • 5. Lundberg E, Fagerberg L, Klevebring D, et al. (2010) Defining the transcriptome and proteome in three functionally different human cell lines. Mol Syst Biol 6: 450.
  • 6. Mak CM, Lee HC, Chan AY, et al. (2013) Inborn errors of metabolism and expanded newborn screening: review and update. Crit Rev Cl Lab Sci 50: 142–162.    
  • 7. Therrell BL, Lloyd-Puryear MA, Camp KM, et al. (2014) Inborn errors of metabolism identified via newborn screening: Ten-year incidence data and costs of nutritional interventions for research agenda planning. Mol Genet Metab 113: 14–26.    
  • 8. Kanehisa M, Goto S, Sato Y, et al. (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40: D109–D114.    
  • 9. Karp PD, Riley M, Paley SM, et al. (2002) The MetaCyc database. Nucleic Acids Res 30: 59–61.    
  • 10. Scheer M, Grote A, Chang A, et al. (2011) BRENDA, the enzyme information system in 2011. Nucleic Acids Res 39: D670–D676.    
  • 11. Deutscher J (2008) The mechanisms of carbon catabolite repression in bacteria. Curr Opin Microbiol 11: 87–93.    
  • 12. Bazurto JV, Heitman NJ, Downs DM (2015) Aminoimidazole carboxamide ribotide exerts opposing effects on thiamine synthesis in Salmonella enterica. J Bacteriol 197: 2821–2830.    
  • 13. Borchert AJ, Downs DM (2017) The response to 2-aminoacrylate differs in Escherichia coli and Salmonella enterica, despite shared metabolic components. J Bacteriol 199: e00140-17.
  • 14. Winfield MD, Groisman EA (2004) Phenotypic differences between Salmonella and Escherichia coli resulting from the disparate regulation of homologous genes. P Natl Acad Sci USA 101: 17162–17167.    
  • 15. Voit EO (2012) A First Course in Systems Biology, New York: Garland Science.
  • 16. Voit EO, Alvarez-Vasquez F, Hannun YA (2010) Computational analysis of sphingolipid pathway systems. Adv Exp Med Biol 688: 264–275.    
  • 17. Koenigsknecht MJ, Downs DM (2010) Thiamine biosynthesis can be used to dissect metabolic integration. Trends Microbiol 18: 240–247.    
  • 18. Koenigsknecht MJ, Lambrecht JA, Fenlon LA, et al. (2012) Perturbations in histidine biosynthesis uncover robustness in the metabolic network of Salmonella enterica. PLoS One 7: e48207.    
  • 19. Ramos I, Vivas EI, Downs DM (2008) Mutations in the tryptophan operon allow PurF-independent thiamine synthesis by altering flux in vivo. J Bacteriol 190: 815–822.    
  • 20. Palmer LD, Dougherty MJ, Downs DM (2012) Analysis of ThiC variants in the context of the metabolic network of Salmonella enterica. J Bacteriol 194: 6088–6095.    
  • 21. Dougherty MJ, Downs DM (2006) A connection between iron-sulfur cluster metabolism and the biosynthesis of 4-amino-5-hydroxymethyl-2-methylpyrimidine pyrophosphate in Salmonella enterica. Microbiology 152: 2345–2353.    
  • 22. Kirk PD, Babtie AC, Stumpf MP (2015) Systems biology (un)certainties. Science 350: 386–388.    
  • 23. Wang Z, Zhang JZ (2009) Abundant indispensable redundancies in cellular metabolic networks. Genome Biol Evol 1: 23–33.
  • 24. Stitt M, Sulpice R, Keurentjes J (2010) Metabolic networks: How to identify key components in the regulation of metabolism and growth. Plant Physiol 152: 428–444.    
  • 25. Cornish-Bowden A (2013) Biochemistry: Curbing the excesses of low demand. Nature 500: 157–158.    
  • 26. Kim J, Kershner JP, Novikov Y, et al. (2010) Three serendipitous pathways in E. coli can bypass a block in pyridoxal-5'-phosphate synthesis. Mol Syst Biol 6: 436.
  • 27. Masel J, Trotter MV (2010) Robustness and evolvability. Trends Genet 26: 406–414.    
  • 28. Hadi NI, Jamal Q, Iqbal A, et al. (2017) Serum metabolomic profiles for breast cancer diagnosis, grading and staging by gas chromatography-mass spectrometry. Sci Rep 7: 1715.    
  • 29. Willmann L, Erbes T, Halbach S, et al. (2015) Exometabolom analysis of breast cancer cell lines: Metabolic signature. Sci Rep 5: 13374.    
  • 30. Spratlin JL, Serkova NJ, Eckhardt SG (2009) Clinical applications of metabolomics in oncology: a review. Clin Cancer Res 15: 431–440.    
  • 31. Biocrates, Biocrates Life Sciences, The Deep Phenotyping Company, 2008. Available from: http://www.biocrates.com/.
  • 32. Metabolon, 2008. Available from: http://www.metabolon.com/.
  • 33. Voit EO (2002) Models-of-data and models-of-processes in the post-genomic era. Math Biosci 180: 263–274.    
  • 34. Voit EO (2004) The dawn of a new era of metabolic systems analysis. Drug Discov Today 2: 182–189.    
  • 35. Lay JO, Liyanage R, Borgmann S, et al. (2006) Problems with the "omics". Trends Anal Chem 25: 1046–1056.    
  • 36. Huang S, Chaudhary K, Garmire LX (2017) More is better: Recent progress in multi-omics data integration methods. Front Genet 8: 84.    
  • 37. Wang J, Zuo Y, Man Y, et al. (2015) Pathway and network approaches for identification of cancer signature markers from omics data. J Cancer 6: 54–65.    
  • 38. Voit EO (2017) The best models of metabolism. WIREs Syst Biol Med 9.
  • 39. Voit EO (2013) Biochemical systems theory: A review. ISRN Biomath.
  • 40. Faraji M, Voit EO (2017) Nonparametric dynamic modeling. Math Biosci 287: 130–146.    
  • 41. Chou IC, Voit EO (2009) Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math Biosci 219: 57–83.    
  • 42. Gennemark P, Wedelin D (2007) Efficient algorithms for ordinary differential equation model identification of biological systems. IET Syst Biol 1: 120–129.    
  • 43. Goel G, Chou IC, Voit EO (2008) System estimation from metabolic time-series data. Bioinformatics 24: 2505–2511.    
  • 44. Lee Y, Escamilla-Treviño L, Dixon RA, et al. (2012) Functional analysis of metabolic channeling and regulation in lignin biosynthesis: A computational approach. PLoS Comput Biol 8: e1002769.    
  • 45. Dolatshahi S, Fonseca LL, Voit EO (2015) New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. I. Construction and diagnosis of a comprehensive dynamic model. Mol Biosyst 12: 23–36.
  • 46. Dolatshahi S, Fonseca LL, Voit EO (2015) New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. II. Inference of the precisely timed control system regulating glycolysis. Mol Biosyst 12: 37–47.
  • 47. Palsson BO (2006) Systems Biology: Properties of Reconstructed Networks, New York: Cambridge University Press.
  • 48. Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28: 245–248.    
  • 49. Voit EO (1992) Optimization in integrated biochemical systems. Biotechnol Bioeng 40: 572–582.    
  • 50. Torres NV, Voit EO (2002) Pathway Analysis and Optimization in Metabolic Engineering, Cambridge: Cambridge University Press.
  • 51. Bazurto JV, Dearth SP, Tague ED, et al. (2017) Untargeted metabolomics confirms and extends the understanding of the impact of aminoimidazole carboxamide ribotide (AICAR) in the metabolic network of Salmonella enterica. Microb Cell 5: 74–87.    
  • 52. Neves AR, Ramos A, Nunes MC, et al. (1999) In vivo nuclear magnetic resonance studies of glycolytic kinetics in Lactococcus lactis. Biotechnol Bioeng 64: 200–212.    
  • 53. Fonseca LL, Sánchez C, Santos H, et al. (2011) Complex coordination of multi-scale cellular responses to environmental stress. Mol Biosyst 7: 731–741.    
  • 54. Fonseca LL, Alves PM, Carrondo MJ, et al. (2001) Effect of ethanol on the metabolism of primary astrocytes studied by (13)C- and (31)P-NMR spectroscopy. J Neurosci Res 66: 803–811.    
  • 55. Alves PM, Fonseca LL, Peixoto CC, et al. (2000) NMR studies on energy metabolism of immobilized primary neurons and astrocytes during hypoxia, ischemia and hypoglycemia. NMR Biomed 13: 438–448.    
  • 56. Fonseca CP, Fonseca LL, Montezinho LP, et al. (2013) 23Na multiple quantum filtered NMR characterisation of Na+ binding and dynamics in animal cells: a comparative study and effect of Na+/Li+ competition. Eur Biophys J 42: 503–519.    
  • 57. Voit EO, Almeida JS, Marino S, et al. (2006) Regulation of glycolysis in Lactococcus lactis: An unfinished systems biological case study. IEE P Syst Biol 153: 286–298.    
  • 58. Voit EO, Neves AR, Santos H (2006) The intricate side of systems biology. P Natl Acad Sci USA 103: 9452–9457.    


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