Metabolic control analysis to identify optimal drug targets

  • Jorrit J. Hornberg
  • Frank J. Bruggeman
  • Barbara M. Bakker
  • Hans V. Westerhoff
Part of the Progress in Drug Research book series (PDR, volume 64)


This chapter describes the basic principles of Metabolic Control Analysis (MCA) which is a quantitative methodology to evaluate the importance and relative contribution of individual metabolic steps in the overall functioning of a particular system. The control on the flux through a metabolic pathway or subsystem can be quantified by the control coefficients of the individual enzymes or components which reflects the extent to which the component is rate-limiting. The perturbation of an individual step is measured by its elasticity coefficient. The effect of perturbation of a single step on the entire pathway or subsystemis, in turn, measured by the response coefficient. Differential control analysis can be used to compare flux through a single metabolic pathway in a pathogen with the same pathway in its host to identify uniquely vulnerable steps with the greatest potential for specifically inhibiting flux through the pathogen metabolic pathway. The utility of this methodology is illustrated with the glycolysis in Trypanosomes and with oncogenic signaling.


Triose Phosphate Isomerase Theor Biol Trypanosoma Brucei Signal Transduction Network Bloodstream Form 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Westerhoff HV, Palsson BO (2004) The evolution of molecular biology into systems biology. Nat Biotechnol 22: 1249–1252PubMedCrossRefGoogle Scholar
  2. 2.
    Bakker BM, Assmus HE, Bruggeman F, Haanstra JR, Klipp E, Westerhoff H (2002) Network-based selectivity of antiparasitic inhibitors. Mol Biol Rep 29: 1–5PubMedCrossRefGoogle Scholar
  3. 3.
    Alberghina L, Westerhoff HV (2005) Systems Biology. Definitions and Perspectives, Springer, Berlin, GermanyGoogle Scholar
  4. 4.
    Olivier BG, Snoep JL (2004) Web-based kinetic modelling using JWS Online. Bioinformatics 20: 2143–2144PubMedCrossRefGoogle Scholar
  5. 5.
    Westerhoff HV (2001) The silicon cell, not dead but live! Metab Eng 3: 207–210PubMedCrossRefGoogle Scholar
  6. 6.
    Snoep JL (2005) The Silicon Cell initiative: working towards a detailed kinetic description at the cellular level. Curr Opin Biotechnol 16: 336–343PubMedCrossRefGoogle Scholar
  7. 7.
    Teusink B, Passarge J, Reijenga CA, Esgalhado E, van der Weijden CC, Schepper M, Walsh MC, Bakker BM, van Dam K, Westerhoff HV et al (2000) Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem 267: 5313–5329PubMedCrossRefGoogle Scholar
  8. 8.
    Tyson JJ, Chen K, Novak B (2001) Network dynamics and cell physiology. Nat Rev Mol Cell Biol 2: 908–916PubMedCrossRefGoogle Scholar
  9. 9.
    Teusink B, Walsh MC, van Dam K, Westerhoff HV (1998) The danger of metabolic pathways with turbo design. Trends Biochem Sci 23: 162–169PubMedCrossRefGoogle Scholar
  10. 10.
    Bakker BM, Mensonides FI, Teusink B, van Hoek P, Michels PA, Westerhoff HV (2000) Compartmentation protects trypanosomes from the dangerous design of glycolysis. Proc Natl Acad Sci USA 97: 2087–2092PubMedCrossRefGoogle Scholar
  11. 11.
    Shulman RG, Rothman DL (2005) Metabolomics by in vivo NMR. John Wiley & Sons, Hoboken, NJ, USAGoogle Scholar
  12. 12.
    Bruggeman FJ, Boogerd FC, Westerhoff HV (2005) The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica. Febs J 272: 1965–1985PubMedCrossRefGoogle Scholar
  13. 13.
    Kacser H, Burns JA (1973) The control of flux. Symp Soc Exp Biol 27: 65–104PubMedGoogle Scholar
  14. 14.
    Heinrich R, Rapoport TA (1974) A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. Eur J Biochem 42: 89–95PubMedCrossRefGoogle Scholar
  15. 15.
    Westerhoff HV, Van Dam K (1987) Thermodynamics and Control of Biological Free-Energy Transduction. Elsevier, Amsterdam, The NetherlandsGoogle Scholar
  16. 16.
    Fell DA (1992) Metabolic control analysis: a survey of its theoretical and experimental development. Biochem J 286: 313–330PubMedGoogle Scholar
  17. 17.
    Fell DA (1997) Understanding the control of metabolism. Portland Press, London, UKGoogle Scholar
  18. 18.
    Heinrich R, Schuster S (1996) The regulation of cellular systems. Chapman & Hall, New York, USAGoogle Scholar
  19. 19.
    Kholodenko BN, Westerhoff HV (1993) Metabolic channelling and control of the flux. FEBS Lett 320: 71–74PubMedCrossRefGoogle Scholar
  20. 20.
    Groen AK, Wanders RJ, Westerhoff HV, van der Meer R, Tager JM (1982) Quantification of the contribution of various steps to the control of mitochondrial respiration. J Biol Chem 257: 2754–2757PubMedGoogle Scholar
  21. 21.
    Bakker BM, Michels PA, Opperdoes FR, Westerhoff HV (1999) What controls glycolysis in bloodstream form Trypanosoma brucei? J Biol Chem 274: 14551–14559PubMedCrossRefGoogle Scholar
  22. 22.
    Hornberg JJ, Bruggeman FJ, Binder B, Geest CR, Bij de Vaate AJM, Lankelma J, Heinrich R, Westerhoff HV (2005) Principles behind the multifarious control of signal transduction: ERK phosphorylation and kinase/phosphatase control. FEBS J 272: 244–258PubMedCrossRefGoogle 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 Biology 1: e10PubMedCrossRefGoogle Scholar
  24. 24.
    Ihekwaba AE, Broomhead DS, Grimley RL, Benson N, Kell DB (2004) Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kB pathway: the roles of IKK and IkBa. Syst Biol 1: 93–103CrossRefGoogle Scholar
  25. 25.
    Hornberg JJ, Binder B, Bruggeman FJ, Schoeberl B, Heinrich R, Westerhoff HV (2005) Control of MAPK signalling: from complexity to what really matters. Oncogene 24: 5533–5542PubMedCrossRefGoogle Scholar
  26. 26.
    Peletier MA, Westerhoff HV, Kholodenko BN (2003) Control of spatially heterogeneous and time-varying cellular reaction networks: a new summation law. J Theor Biol 225: 477–487PubMedCrossRefGoogle Scholar
  27. 27.
    Schuster S, Kahn D, Westerhoff HV (1993) Modular analysis of the control of complex metabolic pathways. Biophys Chem 48: 1–17PubMedCrossRefGoogle Scholar
  28. 28.
    Kahn D, Westerhoff HV (1991) Control theory of regulatory cascades. J Theor Biol 153: 255–285PubMedCrossRefGoogle Scholar
  29. 29.
    Westerhoff HV, Koster JG, Van Workum M, Rudd KE (1989) On the control of gene expression. In: A Cornish-Bowden, ML Cardenas (eds): Control ofmetabolic processes. Plenum Press, New York, USA. pp. 399–413Google Scholar
  30. 30.
    Bruggeman F, Westerhoff H, Hoek J, Kholodenko B (2002) Modular response analysis of cellular regulatory networks. J Theor Biol 218: 507PubMedGoogle Scholar
  31. 31.
    Kholodenko BN (1988) How do external parameters control fluxes and concentrations of metabolites? An additional relationship in the theory of metabolic control. FEBS Lett 232: 383–386PubMedCrossRefGoogle Scholar
  32. 32.
    Bakker BM, Westerhoff HV, Opperdoes FR, Michels PA (2000) Metabolic control analysis of glycolysis in trypanosomes as an approach to improve selectivity and effectiveness of drugs. Mol Biochem Parasitol 106: 1–10PubMedCrossRefGoogle Scholar
  33. 33.
    Rigoulet M, Averet N, Mazat JP, Guerin B, Cohadon F (1988) Redistribution of the flux-control coefficients in mitochondrial oxidative phosphorylations in the course of brain edema. Biochim Biophys Acta 932: 116–123PubMedCrossRefGoogle Scholar
  34. 34.
    Mazat JP, Rossignol R, Malgat M, Rocher C, Faustin B, Letellier T (2001) What do mitochondrial diseases teach us about normal mitochondrial functions that we already knew: threshold expression of mitochondrial defects. Biochim Biophys Acta 1504: 20–30PubMedCrossRefGoogle Scholar
  35. 35.
    Verlinde CL, Hannaert V, Blonski C, Willson M, Perie JJ, Fothergill-Gilmore LA, Opperdoes FR, Gelb MH, Hol WG, Michels PA (2001) Glycolysis as a target for the design of new anti-trypanosome drugs. Drug Resist Updat 4: 50–65PubMedCrossRefGoogle Scholar
  36. 36.
    Eisenthal R, Cornish-Bowden A (1998) Prospects for antiparasitic drugs. The case of Trypanosoma brucei, the causative agent of African sleeping sickness. J Biol Chem 273: 5500–5505PubMedCrossRefGoogle Scholar
  37. 37.
    Bakker BM, Michels PA, Opperdoes FR, Westerhoff HV (1997) Glycolysis in bloodstream from Trypanosoma brucei can be understood in terms of the kinetics of the glycolytic enzymes. J Biol Chem 272: 3207–3215PubMedCrossRefGoogle Scholar
  38. 38.
    Bakker BM, Walsh MC, ter Kuile BH, Mensonides FI, Michels PA, Opperdoes FR, Westerhoff HV (1999) Contribution of glucose transport to the control of the glycolytic flux in Trypanosoma brucei. Proc Natl Acad Sci USA 96: 10098–10103PubMedCrossRefGoogle Scholar
  39. 39.
    Albert MA, Haanstra JR, Hannaert V, Van Roy J, Opperdoes FR, Bakker BM, Michels PA (2005) Experimental and in silico analyses of glycolytic flux control in bloodstream from Trypanosoma brucei. J Biol Chem 280: 28306–28315PubMedCrossRefGoogle Scholar
  40. 40.
    Mulquiney PJ, Kuchel PW (1999) Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: equations and parameter refinement. Biochem J 342 Pt 3: 581–596PubMedCrossRefGoogle Scholar
  41. 41.
    Joshi A, Palsson BO (1989) Metabolic dynamics in the human red cell. Part I-A comprehensive kinetic model. J Theor Biol 141: 515–528PubMedCrossRefGoogle Scholar
  42. 42.
    Schuster R, Holzhutter HG (1995) Use of mathematical models for predicting the metabolic effect of large-scale enzyme activity alterations. Application to enzyme deficiencies of red blood cells. Eur J Biochem 229: 403–418PubMedCrossRefGoogle Scholar
  43. 43.
    Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR (2004) A census of human cancer genes. Nat Rev Cancer 4: 177–183PubMedCrossRefGoogle Scholar
  44. 44.
    Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100: 57–70PubMedCrossRefGoogle Scholar
  45. 45.
    Vogelstein B, Kinzler KW (2004) Cancer genes and the pathways they control. Nat Med 10: 789–799PubMedCrossRefGoogle Scholar
  46. 46.
    Gatenby RA, Maini PK (2003) Mathematical oncology: cancer summed up. Nature 421: 321PubMedCrossRefGoogle Scholar
  47. 47.
    Kitano H (2003) Cancer robustness: tumour tactics. Nature 426: 125PubMedCrossRefGoogle Scholar
  48. 48.
    Butcher EC, Berg EL, Kunkel EJ (2004) Systems biology in drug discovery. Nat Biotechnol 22: 1253–1259PubMedCrossRefGoogle Scholar
  49. 49.
    Christopher R, Dhiman A, Fox J, Gendelman R, Haberitcher T, Kagle D, Spizz G, Khalil IG, Hill C (2004) Data-driven computer simulation of human cancer cell. Ann NY Acad Sci 1020: 132–153PubMedCrossRefGoogle Scholar
  50. 50.
    Khalil IG, Hill C (2005) Systems biology for cancer. Curr Opin Oncol 17: 44–48PubMedCrossRefGoogle Scholar
  51. 51.
    Hornberg JJ, Bruggeman FJ, Westerhoff HV, Lankelma J (2006) Cancer: A Systems Biology Disease. Biosystems 83: 81–90PubMedCrossRefGoogle Scholar
  52. 52.
    Alberghina L, Chiaradonna F, Vanoni M (2004) Systems biology and the molecular circuits of cancer. Chembiochem 5: 1322–1333PubMedCrossRefGoogle Scholar
  53. 53.
    DeVita VT, Hellman S, Rosenberg SA (2001) Cancer: Principles & Practice of Oncology. 6th edition. Lippincott Williams & Wilkins, Philidelphia, PA, USAGoogle Scholar
  54. 54.
    Krause DS, Van Etten RA (2005) Tyrosine kinases as targets for cancer therapy. N Engl J Med 353: 172–187PubMedCrossRefGoogle Scholar
  55. 55.
    Mendelsohn J, Baselga J (2000) The EGF receptor family as targets for cancer therapy. Oncogene 19: 6550–6565PubMedCrossRefGoogle Scholar
  56. 56.
    Sebolt-Leopold JS, Herrera R (2004) Targeting the mitogen-activated protein kinase cascade to treat cancer. Nat Rev Cancer 4: 937–947PubMedCrossRefGoogle Scholar
  57. 57.
    Shawver LK, Slamon D, Ullrich A (2002) Smart drugs: tyrosine kinase inhibitors in cancer therapy. Cancer Cell 1: 117–123PubMedCrossRefGoogle Scholar
  58. 58.
    Schoeberl B, Eichler-Jonsson C, Gilles ED, Muller G (2002) Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 20: 370–375PubMedCrossRefGoogle Scholar
  59. 59.
    Chiaradonna F, Magnani C, Sacco E, Manzoni R, Alberghina L, Vanoni M (2005) Acquired glucose sensitivity of k-ras transformed fibroblasts. Biochem Soc Trans 33: 297–299PubMedCrossRefGoogle Scholar
  60. 60.
    Cascante M, Boros LG, Comin-Anduix B, de Atauri P, Centelles JJ, Lee PW (2002) Metabolic control analysis in drug discovery and disease. Nat Biotechnol 20: 243–249PubMedCrossRefGoogle Scholar
  61. 61.
    Comin-Anduix B, Boren J, Martinez S, Moro C, Centelles JJ, Trebukhina R, Petushok N, Lee WN, Boros LG, Cascante M (2001) The effect of thiamine supplementation on tumour proliferation. A metabolic control analysis study. Eur J Biochem 268: 4177–4182PubMedCrossRefGoogle Scholar

Copyright information

© Birkhäuser Verlag 2007

Authors and Affiliations

  • Jorrit J. Hornberg
    • 1
    • 4
  • Frank J. Bruggeman
    • 1
    • 2
  • Barbara M. Bakker
    • 1
  • Hans V. Westerhoff
    • 1
    • 2
    • 3
  1. 1.Department of Molecular Cell Physiology, Institute for Molecular Cell Biology, Faculty of Earth and Life SciencesVrije UniversiteitAmsterdamThe Netherlands
  2. 2.Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary BioCentre, Faculty of Engineering and Physical SciencesUniversity of ManchesterManchesterUK
  3. 3.Department of Mathematical BiochemistryUniversity of AmsterdamAmsterdamThe Netherlands
  4. 4.Molecular Parmacology UnitNV OrganonOssThe Netherlands

Personalised recommendations