Skip to main content

Systems Biology Meets Metabolism

  • Chapter
  • First Online:
Genetics Meets Metabolomics

Abstract

In the preceding chapters many aspects of metabolite quantification and relation to trait and disease phenotypes have been described, in particular the linkage of intermediate metabolic traits to genetic heterogeneities. Although many analyses start on the genome-wide level, they end up picking out single polymorphisms or other variations and study these in detail. This reductionist approach is very common in molecular biology and has proven hugely successful over the past decades. In recent years however, a second paradigm has become increasingly popular, namely that of integrating multiple such analyses into larger ones commonly called models. This paradigm, nowadays, is known as systems biology and is expected to penetrate many classical molecular analyses.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

  1. Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664

    Article  PubMed  CAS  Google Scholar 

  2. Gille C, Bölling 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

    Article  PubMed  CAS  Google Scholar 

  3. Orth JD, Conrad TM, Na J et al (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol Syst Biol 7:535

    Article  PubMed  Google Scholar 

  4. Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121

    Article  PubMed  CAS  Google Scholar 

  5. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35:W182–W185

    Article  PubMed  Google Scholar 

  6. Karp PD, Paley SM, Krummenacker M et al (2009) Pathway tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform 11:40–79

    Article  PubMed  CAS  Google Scholar 

  7. Aziz RK, Bartels D, Best AA et al (2008) The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9:75

    Article  PubMed  CAS  Google Scholar 

  8. Durot M, Bourguignon PY, Schachter V (2009) Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 33:164–190

    Article  PubMed  CAS  Google Scholar 

  9. Gehlenborg N, O’Donoghue SI, Baliga NS et al (2010) Visualization of omics data for systems biology. Nat Methods 7:S56–S68

    Article  PubMed  CAS  Google Scholar 

  10. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2011) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114

    Article  PubMed  CAS  Google Scholar 

  11. Casp R, Altman T, Dale JM et al (2010) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 38:D473–D479

    Article  CAS  Google Scholar 

  12. Overbeek R, Begley T, Butler RM et al (2005) The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33:5691–5702

    Article  PubMed  CAS  Google Scholar 

  13. Schellenberger J (2010) BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11:213

    Article  PubMed  CAS  Google Scholar 

  14. Feist AM, Herrgård MJ, Thiele I, Reed JL, Palsson BØ (2009) Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 7:129–143

    PubMed  CAS  Google Scholar 

  15. Pitkänen E, Rousu J, Ukkonen E (2010) Computational methods for metabolic reconstruction. Curr Opin Biotechnol 21:70–77

    Article  PubMed  CAS  Google Scholar 

  16. Francke C, Siezen RJ, Teusink B (2005) Reconstructing the metabolic network of a bacterium from its genome. Trends Microbiol 13:550–558

    Article  PubMed  CAS  Google Scholar 

  17. Covert MW, Schilling CH, Famili I et al (2001) Metabolic modeling of microbial strains in silico. Trends Biochem Sci 2:179–186

    Article  Google Scholar 

  18. Oberhardt MA, Palsson BØ, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320

    Article  PubMed  Google Scholar 

  19. Karp PD, Caspi R (2011) A survey of metabolic databases emphasizing the MetaCyc family. Arch Toxicol 85:1015–1033

    Article  PubMed  CAS  Google Scholar 

  20. Delcher AL, Harmon D, Kasif S, White O, Salzberg SL (1999) Improved microbial gene identification with GLIMMER. Nucleic Acids Res 27:4636–4641

    Article  PubMed  CAS  Google Scholar 

  21. Borodovsky M, Lomsadze A (2011) Eukaryotic gene prediction using GeneMark.hmm-E and GeneMark-ES. Curr Protoc Bioinform Chapter 4:Unit 4.6.1–4.6.10

    Google Scholar 

  22. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    PubMed  CAS  Google Scholar 

  23. Pearson WR (1990) Rapid and sensitive sequence comparison with FASTP and FASTA. Methods Enzymol 183:63–98

    Article  PubMed  CAS  Google Scholar 

  24. The Universal Protein Resource (UniProt) (2009) Consortium, UniProt. Nucleic Acids Res 37: D169–D174

    Google Scholar 

  25. Médigue C, Moszer I (2007) Annotation, comparison and databases for hundreds of bacterial genomes. Res Microbiol 158:724–736

    Article  PubMed  CAS  Google Scholar 

  26. Apweiler R, Altwood TK, Bairoch A et al (2000) InterPro–an integrated documentation resource for protein families, domains and functional sites. Bioinformatics 16:1145–1150

    Article  PubMed  CAS  Google Scholar 

  27. Claudel-Renard C, Chevalet C, Faraut T, Kahn D (2003) Enzyme-specific profiles for genome annotation: PRIAM. Nucleic Acids Res 31:6633–6639

    Article  PubMed  CAS  Google Scholar 

  28. Seffernick JL, de Souza ML, Sadowsky MJ, Wackett LP (2001) Melamine deaminase and atrazine chlorohydrolase: 98 percent identical but functionally different. J Bacteriol 183:2405–2410

    Article  PubMed  CAS  Google Scholar 

  29. Palmer DR, Garrett JB, Sharma V et al (1999) Unexpected divergence of enzyme function and sequence: “N-acylamino acid racemase” is o-succinylbenzoate synthase. Biochemistry 38:4252–4258

    Article  PubMed  CAS  Google Scholar 

  30. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW (2011) GenBank. Nucleic Acids Res 39:D32–D37

    Article  PubMed  Google Scholar 

  31. Markowitz VM, Chen I-MA, Palaniappan K et al (2010) The integrated microbial genomes system: an expanding comparative analysis resource. Nucleic Acids Res 38:D382–D390

    Article  PubMed  CAS  Google Scholar 

  32. Pinney JW, Shirley MW, McConkey GA, Westhead DR (2005) metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Res 33:1399–1409

    Article  PubMed  CAS  Google Scholar 

  33. Sun J, Zeng A-P (2004) IdentiCS–identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence. BMC Bioinformatics 5:112

    Article  PubMed  CAS  Google Scholar 

  34. Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28:304–305

    Article  PubMed  CAS  Google Scholar 

  35. Scheer M, Grote A, Chang A et al (2011) BRENDA, the enzyme information system in 2011. Nucleic Acids Res 39:D670–D676

    Article  PubMed  Google Scholar 

  36. Ren Q, Chen K, Paulsen IT (2007) TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Res 35:D274–D279

    Article  PubMed  CAS  Google Scholar 

  37. Fleischmann A, Darsiw M, Degtyarenko K et al (2004) IntEnz, the integrated relational enzyme database. Nucleic Acids Res 32:D434–D437

    Article  PubMed  CAS  Google Scholar 

  38. Mao X, Cai T, Olyarchuk JG, Wei L (2005) Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 21:3787–3793

    Article  PubMed  CAS  Google Scholar 

  39. Ma H, Zeng A-P (2003) Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19:270–277

    Article  PubMed  CAS  Google Scholar 

  40. Kümmel A, Panke S, Heinemann M (2006) Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics 7:512

    Article  PubMed  CAS  Google Scholar 

  41. Gardy JL, Liard MR, Chen F et al (2005) PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 21:617–623

    Article  PubMed  CAS  Google Scholar 

  42. Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786

    Article  PubMed  CAS  Google Scholar 

  43. Emanuelsson O, Brunak S, von Heijne G, Nielsen H (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc 2:953–971

    Article  PubMed  CAS  Google Scholar 

  44. Liao L, Kim S, Tomb JF (2002) Genome comparisons based on profiles of metabolic pathways

    Google Scholar 

  45. Hong SH, Kim TY, Lee SY (2004) Phylogenetic analysis based on genome-scale metabolic pathway reaction content. Appl Microbiol Biotechnol 65:203–210

    Article  PubMed  CAS  Google Scholar 

  46. Kastenmüller G, Gasteiger J, Mewes HW (2008) An environmental perspective on large-scale genome clustering based on metabolic capabilities. Bioinformatics 24:i56–i62

    Article  PubMed  Google Scholar 

  47. Maltsev N, Glass E, Sulakhe D et al (2006) PUMA2–grid-based high-throughput analysis of genomes and metabolic pathways. Nucleic Acids Res 34:D369–D372

    Article  PubMed  CAS  Google Scholar 

  48. Haft DH, Selengut JD, Brinkac LM, Zafar N, White O (2005) Genome Properties: a system for the investigation of prokaryotic genetic content for microbiology, genome annotation and comparative genomics. Bioinformatics 21:293–306

    Article  PubMed  CAS  Google Scholar 

  49. Kastenmüller G, Schenk ME, Gasteiger J, Mewes HW (2009) Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes. Genome Biol 10:R28

    Article  PubMed  CAS  Google Scholar 

  50. Croes D, Couche F, Wodak SJ, van Helden J (2005) Metabolic pathFinding: inferring relevant pathways in biochemical networks. Nucleic Acids Res 33:W326–W330

    Article  PubMed  CAS  Google Scholar 

  51. Faust K, Croes D, van Helden J (2009) Metabolic pathfinding using RPAIR annotation. J Mol Biol 388:390–414

    Article  PubMed  CAS  Google Scholar 

  52. Blum T, Kohlbacher O (2008) MetaRoute: fast search for relevant metabolic routes for interactive network navigation and visualization. Bioinformatics 24:2108–2109

    Article  PubMed  CAS  Google Scholar 

  53. Arita M (2003) In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary metabolism. Genome Res 13:2455–2466

    Article  PubMed  CAS  Google Scholar 

  54. Rahman SA, Advani P, Schunk R, Schrader R, Schomburg D (2005) Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC). Bioinformatics 21:1189–1193

    Article  PubMed  CAS  Google Scholar 

  55. Blum T, Kohlbacher O (2008) Using atom mapping rules for an improved detection of relevant routes in weighted metabolic networks. J Comput Biol 15:565–576

    Article  PubMed  CAS  Google Scholar 

  56. Pitkänen E, Jouhten P, Rousu J (2009) Inferring branching pathways in genome-scale metabolic networks. BMC Syst Biol 3:103

    Article  PubMed  CAS  Google Scholar 

  57. Orth JD, Palsson BØ (2010) Systematizing the generation of missing metabolic knowledge. Biotechnol Bioeng 107:403–412

    Article  PubMed  CAS  Google Scholar 

  58. Kumar VS, Dasika MS, Maranas CD (2007) Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8:212

    Article  CAS  Google Scholar 

  59. Schilling CH, Palsson BO (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 

  60. Henry CS, DeJongh M, Best AA et al (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28:977–982

    Article  PubMed  CAS  Google Scholar 

  61. Kumar VS, Maranas CD (2009) GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput Biol 5:e1000308

    Article  PubMed  CAS  Google Scholar 

  62. Breitling R, Vitkup D, Barrett MP (2008) New surveyor tools for charting microbial metabolic maps. Nat Rev Microbiol 6:156–161

    Article  PubMed  CAS  Google Scholar 

  63. Palsson BØ (2006) Systems biology: properties of reconstructed networks. Cambridge University Press, Cambridge

    Book  Google Scholar 

  64. Papin JA, Price ND, Wiback SJ, Fell DA, Palsson BØ (2003) Metabolic pathways in the post-genome era. Trends Biochem Sci 28:250–258

    Article  PubMed  CAS  Google Scholar 

  65. Schuster S, Fell DA, Dandekar T (2000) A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol 18:326–332

    Article  PubMed  CAS  Google Scholar 

  66. Schilling CH, Letscher D, Palsson BØ (2000) Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J Theor Biol 203:229–248

    Article  PubMed  CAS  Google Scholar 

  67. Llaneras F, Picó J (2010) Which metabolic pathways generate and characterize the flux space? A comparison among elementary modes, extreme pathways and minimal generators. J Biomed Biotechnol 2010:753904

    Article  PubMed  Google Scholar 

  68. Price ND, Papin JA, Palsson BØ (2002) Determination of redundancy and systems properties of the metabolic network of Helicobacter pylori using genome-scale extreme pathway analysis. Genome Res 12:760–769

    PubMed  CAS  Google Scholar 

  69. Segrè D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci USA 99:15112–15117

    Article  PubMed  CAS  Google Scholar 

  70. Rojas I, Golebiewski M, Kania R et al (2007) Storing and annotating of kinetic data. In Silico Biol 7:S3–S44

    Google Scholar 

  71. Rizzi M, Baltes M, Theobald U, Reuss M (1997) In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: II. Mathematical model. Biotechnol Bioeng 55:592–608

    Article  PubMed  CAS  Google Scholar 

  72. Teusink B, Passarge J, Reijenga CA et al (2000) Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem 267:5313–5329

    Article  PubMed  CAS  Google Scholar 

  73. Blow N (2008) Metabolomics: biochemistry’s new look. Nature 455:697–700

    Article  PubMed  CAS  Google Scholar 

  74. Famili I, Mahadevan R, Palsson BØ (2005) k-Cone analysis: determining all candidate values for kinetic parameters on a network scale. Biophys J 88:1616–1625

    Article  PubMed  CAS  Google Scholar 

  75. Jamshidi N, Palsson BØ (2010) Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. Biophys J 98:175–185

    Article  PubMed  CAS  Google Scholar 

  76. Jamshidi N, Palsson BØ (2008) Top-down analysis of temporal hierarchy in biochemical reaction networks. PLoS Comput Biol 4:e1000177

    Article  PubMed  CAS  Google Scholar 

  77. 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

    Article  PubMed  CAS  Google Scholar 

  78. Schellenberger J, Palsson BØ (2009) Use of randomized sampling for analysis of metabolic networks. J Biol Chem 284:5457–5461

    Article  PubMed  CAS  Google Scholar 

  79. Bakker BM, van Eunen K, Jeneson JA et al (2010) Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models. Biochem Soc Trans 38:1294–1301

    Article  PubMed  CAS  Google Scholar 

  80. Arkin A, Shen P, Ross J (1997) A test case of correlation metric construction of a reaction pathway from measurements. Science 277:1275–1279

    Article  CAS  Google Scholar 

  81. Vance W, Arkin A, Ross J (2002) Determination of causal connectivities of species in reaction networks. Proc Natl Acad Sci USA 99:5816–5821

    Article  PubMed  CAS  Google Scholar 

  82. Steuer R, Kurths J, Fiehn O, Weckwerth W (2003) Observing and interpreting correlations in metabolomic networks. Bioinformatics 19:1019–1026

    Article  PubMed  CAS  Google Scholar 

  83. Øksendal B (2005) Stochastic differential equations: an introduction with applications. Springer, New York

    Google Scholar 

  84. Camacho D, de la Fuente A, Mendes P (2005) The origin of correlations in metabolomics data. Metabolomics 1:53–63

    Article  CAS  Google Scholar 

  85. Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ (2011) Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol 5:21

    Article  PubMed  CAS  Google Scholar 

  86. Schäfer J, Strimmer K, Jos’ FF et al (2005) Learning large-scale graphical Gaussian models from genomic data. AIP Conf Proc 776:263–276

    Article  Google Scholar 

  87. Lee JM, Gianchandani EP, Eddy JA, Papin JA (2008) Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput Biol 4:e1000086

    Article  PubMed  CAS  Google Scholar 

  88. de la Fuente A, Bing N, Hoeschele I, Mendes P (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20:3565–3574

    Article  PubMed  CAS  Google Scholar 

  89. Magwene PM, Kim J (2004) Estimating genomic coexpression networks using first-order conditional independence. Genome Biol 5:R100

    Article  PubMed  Google Scholar 

  90. Wille A, Zimmerman P, Vranová E et al (2004) Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana. Genome Biol 5:R92

    Article  PubMed  Google Scholar 

  91. Freudenberg J, Wang M, Yang Y, Li W (2009) Partial correlation analysis indicates causal relationships between GC-content, exon density and recombination rate in the human genome. BMC Bioinformatics 10(Suppl 1):S66

    Article  PubMed  CAS  Google Scholar 

  92. Keurentjes Joost JB, Fu J, Ric de Vos CH et al (2006) The genetics of plant metabolism. Nat Genet 38:842–849

    Article  PubMed  CAS  Google Scholar 

  93. Liebermeister W, Klipp E (2006) Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data. Theor Biol Med Model 3:42

    Article  PubMed  CAS  Google Scholar 

  94. Berg JM, Tymoczko JL, Stryer L (2006) Biochemistry, 6th edn. W. H. Freeman, Cranbury

    Google Scholar 

  95. Holle R, Happich M, Löwel H, Wichmann HE, MONICA/KORA Study Group (2005) KORA–a research platform for population based health research. Gesundheitswesen 67(Suppl 1):S19–S25

    Article  PubMed  Google Scholar 

  96. Illig T, Gieger C, Zhai G et al (2010) A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42:137–141

    Article  PubMed  CAS  Google Scholar 

  97. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113

    Article  CAS  Google Scholar 

  98. Matsuzaka T, Shimano H, Yahagi N et al (2007) Crucial role of a long-chain fatty acid elongase, Elovl6, in obesity-induced insulin resistance. Nat Med 13:1193–1202

    Article  PubMed  CAS  Google Scholar 

  99. Eaton S, Bartlett K, Pourfarzam M (1996) Mammalian mitochondrial beta-oxidation. Biochem J 320:345–357

    PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  102. Ma H, Sorokin A, Mazein A et al (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol 3:135

    Article  PubMed  Google Scholar 

  103. Van Rijsbergen CJ (1979) Information retrieval, 2nd edn. Butterworth, London

    Google Scholar 

  104. Suhre K, Petersen AK, Mohney RP et al (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477:54–60

    Article  PubMed  CAS  Google Scholar 

  105. Altmaier E, Ramsay SL, Graber A et al (2008) Bioinformatics analysis of targeted metabolomics–uncovering old and new tales of diabetic mice under medication. Endocrinology 149:3478–3489

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabian J. Theis Dipl.-Mathematiker, Dipl.-Phys .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Krumsiek, J., Stückler, F., Kastenmüller, G., Theis, F.J. (2012). Systems Biology Meets Metabolism. In: Suhre, K. (eds) Genetics Meets Metabolomics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1689-0_17

Download citation

Publish with us

Policies and ethics