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Metabolic networks: biology meets engineering sciences

  • A. Kremling
  • J. Stelling
  • K. Bettenbrock
  • S. Fischer
  • E.D. Gilles
Chapter
Part of the Topics in Current Genetics book series (TCG, volume 13)

Abstract

A hallmark of systems biology is the interdisciplinary approach to the complexity of biological systems, in which mathematical modeling constitutes an important part. Here, we use the example of sugar metabolism in the simple bacterium Escherichia coli and its associated control to illustrate the process of model development. Even for this well-characterized biological system, a close interaction between experimentation and theoretical analysis revealed novel, unexpected features. Additionally, the example shows how concepts from engineering sciences can facilitate the formal investigation of biological networks. More generally, we argue that analogies between complex biological and technical systems such as modular structures and common design principles provide crystallization points for fruitful research in both domains.

Keywords

Metabolic Network Intracellular Glucose Diauxic Growth Lactose Permease Lactose Operon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1. Alm E, Arkin AP (2003) Biological networks. Curr Opin Struct Biol 13:193–202 CrossRefPubMedGoogle Scholar
  2. 2. Angeli D, Ferrell Jr. JE, Sontag ED (2004) Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc Natl Acad Sci USA 101:1822–1827 CrossRefPubMedGoogle Scholar
  3. 3. Arita M (2004) The metabolic world of Escherichia coli is not small. Proc Natl Acad Sci USA 101:1543–1547CrossRefPubMedGoogle Scholar
  4. 4. Barkai N, Leibler S (1997) Robustness in simple biochemical networks. Nature 387Google Scholar
  5. 5. Bergmann S, Ihmels J, Barkai N (2004) Similarities and differences in genome-wide expression data of six organisms. PLoS Biol 2:E9 CrossRefPubMedGoogle Scholar
  6. 6. Csete ME, Doyle JC (2002) Reverse engineering of biological complexity. Science 295:1664–1669CrossRefPubMedGoogle Scholar
  7. 7. Endy D, Brent R (2001) Modelling cellular behaviour. Nature 409:391–395 CrossRefPubMedGoogle Scholar
  8. 8. Gilman A, Arkin AP (2002) Genetic “code”: representations and dynamical models of genetic components and networks. Annu Rev Genomics Hum Genet 3:341–369 CrossRefPubMedGoogle Scholar
  9. 9. Hanamura A, Aiba H (1992) A new aspect of transcriptional control of the Escherichia coli crp gen: positive autoregulation. Mol Microbiol 6:2489–2497PubMedGoogle Scholar
  10. 10. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402 (Supp.):C47–C52 Google Scholar
  11. 11. Hogema BM, Arents JC, Bader R, Eijkemanns K, Yoshida H, Takahashi H, Aiba H, Postma PW (1998) Inducer exclusion in Escherichia coli by non-PTS substrates: the role of the PEP to pyruvate ratio in determining the phosphorylation state of enzyme IIAGlc. Mol Microbiol 30:487–498 CrossRefPubMedGoogle Scholar
  12. 12. Huntington SP (1993) The clash of civilizations. Foreign Affairs 72:22–28Google Scholar
  13. 13. Ibarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420:186–89CrossRefPubMedGoogle Scholar
  14. 14. Ideker T, Lauffenburger D (2003) Building with a scaffold: emerging strategies for high- and low-level cellular modeling. Trends Biotechnol 21:255–262 CrossRefPubMedGoogle Scholar
  15. 15. Inada T, Kimata K, Aiba H (1996) Mechanism responsible for glucose-lactose diauxie in Escherichia coli: challenge to the camp model. Genes Cells 1:293–301 CrossRefPubMedGoogle Scholar
  16. 16. Kitano H (2002a) Computational systems biology. Nature 420:206–210CrossRefPubMedGoogle Scholar
  17. 17. Kitano H (2002b) Systems biology: a brief overview. Science 295:1662–1664 CrossRefPubMedGoogle Scholar
  18. 18. Kremling A, Bettenbrock K, Laube B, Jahreis K, Lengeler JW, Gilles ED (2001) The organization of metabolic reaction networks: III. Application for diauxic growth on glucose and lactose. Metab Eng 3(4):362–379 CrossRefPubMedGoogle Scholar
  19. 19. Kremling A, Fischer S, Sauter T, Bettenbrock K, Gilles ED (2004) Time hierarchies in the Escherichia coli carbohydrate uptake and metabolism. BioSystems 73(1):57–71CrossRefPubMedGoogle Scholar
  20. 20. Kremling A, Gilles ED (2001) The organization of metabolic reaction networks: II. Signal processing in hierarchical structured functional units. Metab Eng 3(2):138–150CrossRefPubMedGoogle Scholar
  21. 21. Kremling S, Jahreis K, Lengeler JW, Gilles ED (2000) The organization of metabolic reaction networks: A signal-oriented approach to cellular models. Metab Eng 2(3):190–200 CrossRefPubMedGoogle Scholar
  22. 22. Lazebnik Y. (2002) Can a biologist fix a radio? – Or what I learned while studying apoptosis. Cancer Cell 2:179–182CrossRefPubMedGoogle Scholar
  23. 23. Lee E, Salic A, Kruger R, Heinrich R, Kirschner MW (2003). The roles of APC and axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol 1:E10CrossRefPubMedGoogle Scholar
  24. 24. Lee SB, Bailey JE (1984a) Genetically structured models for lac promotor-operator function in the Escherichia coli chromosome and in multicopy plasmids: lac operator function. Biotechnology and Bioengineering 26:1372–1382 CrossRefGoogle Scholar
  25. 25. Lee SB, Bailey JE (1984b) Genetically structured models for lac promotor-operator function in the Escherichia coli chromosome and in multicopy plasmids: lac promotor function. Biotechnology and Bioengineering 26:1383–1389CrossRefGoogle Scholar
  26. 26. Ljung L (1999) System identification: theory for the user. 2nd edn. Prentice Hall PTR, Upper Saddle River, NJGoogle Scholar
  27. 27. Mangan S, Alon U (2003) Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 100:11980–11985CrossRefPubMedGoogle Scholar
  28. 28. D'haeseleer P, Liang S, Somogy R (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16:707–726 CrossRefPubMedGoogle Scholar
  29. 29. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P (2002) Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417:399–403 PubMedGoogle Scholar
  30. 30. Morita T, El-Kazzar W, Tanaka Y, Inada T, Aiba H (2003) Accumulation of glucose 6-phosphate or fructose 6-phosphate is responsible for destabilization of glucose transporter mRNA in Escherichia coli. J Biol Chem 278(18):15608–15614CrossRefPubMedGoogle Scholar
  31. 31. Novak B, Tyson JJ (1993) Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte extracts and intact embryos. J Cell Sci 106:1153–1168PubMedGoogle Scholar
  32. 32. Nurse P (2003) Understanding cells. Nature 424:883CrossRefPubMedGoogle Scholar
  33. 33. Plumbridge J (1998) Expression of ptsG, the gene for the major glucose pts transporter in Escherichia coli, is repressed by Mlc and induced by growth on glucose. Mol Microbiol 29(4):1053–1063CrossRefPubMedGoogle Scholar
  34. 34. Pomerening JR, Sontag ED, Ferrell Jr. JE (2003) Building a cell cycle oscillator: hysteresis and bistability in the activation of Cdc2. Nat Cell Biol 5: 346–351 CrossRefPubMedGoogle Scholar
  35. 35. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási A-L (2002) Hierarchical organization of modularity in metabolic networks. Science 297:1551–1555CrossRefPubMedGoogle Scholar
  36. 36. Rohwer JM, Bader R, Westerhoff HV, Postma PW (1998) Limits to inducer exclusion: Inhibition of the bacterial phosphotransferase system by glycerol kinase. Mol Microbiol 29:641–652CrossRefPubMedGoogle Scholar
  37. 37. Rohwer JM, Meadow ND, Roseman S, Westerhoff HV, Postma PW (2000) Understanding glucose tranport by the bacterial phosphoenolpyruvate:glycose phosphotransferase system on the basis of kinetic measurements in vitro. J Biol Chem 275:34909–34921 CrossRefPubMedGoogle Scholar
  38. 38. Rohwer JM, Schuster S, Westerhoff HV. How to recognize monofunctional units in a metabolic system. Journal of Theoretical Biology 179:214–228 Google Scholar
  39. 39. Saez-Rodriguez J, Kremling A, Gilles ED (2004) Dissecting the puzzle of life: Modularization of signal transduction networks. Computers & Chemical Engineering, accepted Google Scholar
  40. 40. 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 CrossRefPubMedGoogle Scholar
  41. 41. Schusterm S, Kahn D, Westerhoff HV (1993) Modular analysis of the control of complex metabolic pathways. Biophys Chem 48:1–17. CrossRefPubMedGoogle Scholar
  42. 42. Selinger DW, Wright MA, Church GM (2003) On the complete determination of biological systems. Trends Biotechnol 21:251–254CrossRefPubMedGoogle Scholar
  43. 43. Sha W, Moore J, Chen K, Lassaletta AD, Yi CS, Tyson JJ, Sible JC (2003) Hysteresis drives cell-cycle transitions in Xenopus laevis egg extracts. Proc Natl Acad Sci USA 100 Google Scholar
  44. 44. Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31(1):64–68CrossRefPubMedGoogle Scholar
  45. 45. Stelling J, Klamt S, Bettenbrock K, Schuster S, Gilles ED (2002) Metabolic network structure determines key aspects of functionality and regulation. Nature 420:190–193 CrossRefPubMedGoogle Scholar
  46. 46. Stelling J, Kremling A, Ginkel M, Bettenbrock K, Gilles ED (2001) Towards a Virtual Biological Laboratory. In: Kitano H (ed), Foundations of Systems Biology, pp 189–212. MIT Press, Cambridge, MAGoogle Scholar
  47. 47. Tyson JJ, Chen K, Novak B (2001) Network dynamics and cell physiology. Nat Rev Mol Cell Biol 2:908–916CrossRefPubMedGoogle Scholar
  48. 48. Willems JC (1991) Paradigms and puzzles in the theory of dynamical systems. IEEE Transac Automat Control 36(3):259–294CrossRefGoogle Scholar
  49. 49. Yi T-M, Huang Y, Simon MI, Doyle J (2000) Robust perfect adaptation in bacterial chemotaxis through integral feedback control. Proc Natl Acad Sci USA 97(9):4649–4653 CrossRefPubMedGoogle Scholar
  50. 50. Zak DE, Gonye GE, Schwaber JS, Doyle III FJ (2003) Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: Insights from an identifiability analysis of an in silico network. Genome Res 13:2396–2405CrossRefPubMedGoogle Scholar

Authors and Affiliations

  • A. Kremling
    • 1
  • J. Stelling
    • 2
  • K. Bettenbrock
    • 1
  • S. Fischer
    • 1
  • E.D. Gilles
    • 1
  1. 1.Systems biology group, Max-Planck-Institut für Dynamik, komplexer technischer Systeme, Sandtorstr. 1, 39106 MagdeburgGermany
  2. 2.Institut für Computational Science, ETH Zentrum HRS H 28, Hirschengraben 84, 8092 ZürichSwitzerland

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