Skip to main content
SpringerLink
Log in
Book cover

Systems Biology pp 349–372Cite as

Systems biology of apoptosis

  • Chapter
  • First Online:

Part of the book series: Topics in Current Genetics ((TCG,volume 13))

Abstract

New approaches are required for the mathematical modelling and system identification of complex signal transduction networks, which are characterized by a large number of unknown parameters and partially poorly understood mechanisms. Here, a new quantitative system identification method is described, which applies the novel concept of ’Sensitivity of Sensitivities’ revealing two important system properties: high robustness and modular structures of the dependency between state variables and parameters. This is the key to reduce the system’s dimensionality and to estimate unknown parameters on the basis of experimental data. The approach is applied to CD95-induced apoptosis, also called programmed cell death. Defects in the regulation of apoptosis result in a number of serious diseases such as cancer. With the estimated parameters, it becomes possible to reproduce the observed system behaviour and to predict important system properties. Thereby, a novel regulatory mechanism was revealed, i.e. a threshold between cell death and cell survival.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • 1. Albert R, Jeong H, Barabasi A (2000) Error and attack tolerance of complex networks. Nature 406:378-382

    Article  PubMed  Google Scholar 

  • 2. Alberts B, Bray D, Lewis J, Raff M, Roberts K, Watson JD (2002) Molecular Biology of the Cell. Garland, New York

    Google Scholar 

  • 3. Alon U, Surette MG, Barkai N, Leibler S (1999) Robustness in bacterial chemotaxis. Nature 397:168-171

    Article  PubMed  Google Scholar 

  • 4. Ashkenazi A, Dixit V (1999) Apoptosis control by death and decoy receptors. Curr Opin Cell Biol 11:255-260

    Article  PubMed  Google Scholar 

  • 5. Barabasi A, Albert R (1999) Emergence of scaling in random networks. Science 286:509-512

    Article  PubMed  MathSciNet  Google Scholar 

  • 6. Barkai N, Leibler S (1997) Robustness in simple biochemical networks. Nature 387:913-917

    Article  PubMed  Google Scholar 

  • 7. Bentele M, Lavrik I, Ulrich M, Stößer S, Heermann D, Kalthoff H, Krammer P, Eils R (2004) Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J Cell Biol 166:839-851

    Article  PubMed  Google Scholar 

  • 8. Bertalanffy L (1973) General System Theory. Penguin Books, Harmondsworth.

    Google Scholar 

  • 9. Bhalla US, Iyengar R (1999) Emergent properties of networks of biological signaling pathways. Science 283:381-387

    Article  PubMed  Google Scholar 

  • 10. Bock H (1981) Numerical treatment of inverse problems in chemical reaction kinetics. Modelling of Chemical Reaction Systems. K Ebert, P DeuflhardW Jäger. New York, Springer. 8:102-125

    Google Scholar 

  • 11. Brown K, Sethna J (2003) Statistical mechanical approaches to models with many poorly known parameters. Phys Rev E 68:21904:1-9

    Google Scholar 

  • 12. Chao S, Korsmeyer S (1998) BCL-2 family: Regulators of cell death. Annu Rev Immunol 16:395-419

    Article  PubMed  Google Scholar 

  • 13. Csete ME, Doyle JC (2002) Reverse engineering of biological complexity. Science 295:1664-1669

    Article  PubMed  Google Scholar 

  • 14. Danial N, Korsmeyer S (2004) Cell death: Critical control points. Cell 116:205-219

    Article  PubMed  Google Scholar 

  • 15. Deuflhard P (1983) Numerical treatment of inverse problems in differential and integral equations. Birkhäuser, Basel

    Google Scholar 

  • 16. Deuflhard P, Bornemann F (2002) Scientific Computing with Ordinary Differential Equations. Applied Mathematics. New York, Springer

    Google Scholar 

  • 17. Eissing T, Conzelmann H, Gilles E, Allgower F, Bullinger E, Scheurich P (2004) Bistability analyses of a caspase activation model for receptor-induced apoptosis. J Biol Chem 279:36892-36897

    Article  PubMed  Google Scholar 

  • 18. Evan G, Littlewood T (1998) A matter of life and cell death. Science 281:1317-1322

    Article  PubMed  Google Scholar 

  • 19. Fell D (1992) Metabolic control analysis: a survey of its theoretical and experimental development. Biochem J 286:313-330

    PubMed  Google Scholar 

  • 20. Fussenegger M, Bailey J, Varner J (2000) A mathematical model of caspase function in apoptosis. Nature Biotech 18:768-774

    Article  Google Scholar 

  • 21. Garfinkel D (1968) The role of computer simulation in biochemistry. Comput Biomed Res 2:i-ii

    Article  PubMed  Google Scholar 

  • 22. Garfinkel D, Hess B (1964) Metabolic control mechanisms. J Biol Chem 239:971-983

    PubMed  Google Scholar 

  • 23. Gershenfeld N (1999) The Nature of Mathematical Modeling. Cambridge University Press, Cambridge, UK

    Google Scholar 

  • 24. Goldstein JC, Waterhouse N, Juin P, Evan G, Green D (2000) The coordinate release of cytochrome c during apoptosis is rapid, complete and kinetically invariant. Nature Cell Biol 2:156-162

    Article  PubMed  Google Scholar 

  • 25. Heinrich R, Schuster S (1996) The regulation of cellular systems. Chapman & Hall, New York

    Google Scholar 

  • 26. Kell DB, Westerhoff HV (1986) Metabolic control theory: its role in microbiology and biotechnology. FEMS Microbiol Rev 39:305-320

    Article  Google Scholar 

  • 27. Kitano H (2002) Systems biology: a brief overview. Science 295:1662-1664

    Google Scholar 

  • 28. Krammer P (2000) CD95's deadly mission in the immune system. Nature 407:789-795

    Article  PubMed  Google Scholar 

  • 29. Krueger A, Baumann S, Krammer P, Kirchhoff S (2001) FLICE-Inhibitory proteins: Regulators of death receptor-mediated apoptosis. Mol Cell Biol 21(24):8247-8254

    Article  PubMed  Google Scholar 

  • 30. Lauffenburger DA (2000) Cell signaling pathways as control modules: complexity for simplicity? PNAS 97(10):5031-5033

    Article  PubMed  Google Scholar 

  • 31. Lavrik I, Krueger A, Schmitz I, Baumann S, Weyd H, Krammer P, Kirchhoff S (2003) The active caspase-8 heterotetramer is formed at the CD95 DISC. Cell Death Differ 10:144-145

    Article  PubMed  Google Scholar 

  • 32. Li G, Rosenthal C, Rabitz H (2001) High dimensional model representations. J Phys Chem A 105:7765-7777

    Article  Google Scholar 

  • 33. Meir E, von Dassow G, Munro E, Odell G (2002) Robustness, flexibility, and the role of lateral inhibition in the neurogenic network. Curr Biol 12:778-786

    Article  PubMed  Google Scholar 

  • 34. Mendes P (1993) GEPASI: A software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput Applic Biosci 9:563-571

    Google Scholar 

  • 35. Mendes P (1997) Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends Biochem Sciences 22(9):361-363

    Article  Google Scholar 

  • 36. Mendes P, Kell D (1998) Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 14(10):869-883

    Article  PubMed  Google Scholar 

  • 37. Nagata S (1997) Apoptosis by death factor. Cell 88:355-365

    Article  PubMed  Google Scholar 

  • 38. Nagata S (1999) Fas ligand-induced apoptosis. Annu Rev Genet 33:29-55

    Article  PubMed  Google Scholar 

  • 39. Peter M, Krammer P (2003) The CD95(APO-1/Fas) DISC and beyond. Cell Death Differ 10:26-35

    Article  PubMed  Google Scholar 

  • 40. Reisig W (1985) Petri Nets, An Introduction. Springer-Verlag, Berlin

    Google Scholar 

  • 41. Salvesen GS (2002) Caspases: opening the boxes and interpreting the arrows. Cell Death Differ 9(1):3-5

    Article  PubMed  Google Scholar 

  • 42. Salvesen GS, Duckett CS (2002) IAP Proteins: blocking the road to death's door. Nature Rev Mol Cel Biol 3(6):401-410

    Article  Google Scholar 

  • 43. Sauro HM, Fell DA (1991) SCAMP: A metabolic simulator and control analysis program. Math Comput Modelling 15:15-28

    Article  Google Scholar 

  • 44. Scaffidi C, Fulda S, Srinivasan A, Friesen C, F L, Tomaselli K, Debatin K, Krammer P, ME P (1998) Two CD95 (APO-1/Fas) signaling pathways. EMBO J 17(6):1675-1687

    Article  PubMed  Google Scholar 

  • 45. Schacherer F, Choi C, Götze U, Krull M, Pistor S, Wingender E (2001) The TRANSPATH signal transduction database: a knowledge base on signal transduction networks. Bioinformatics 17(11):1053-1057

    Article  PubMed  Google Scholar 

  • 46. Schilling CH, Schuster S, Palsson BO, Heinrich R (1999) Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol Prog 15(3):296-303

    Article  PubMed  Google Scholar 

  • 47. Schmitz I, Walczak H, Krammer P, Peter M (1999) Differences between CD95 type I and tpye II cells detected with the CD95 ligand. Cell Death Differ 6(9):821-822

    Article  PubMed  Google Scholar 

  • 48. Smuts J (1926) Holism and Evolution. Macmillan & Co Ldt., London

    Google Scholar 

  • 49. Stelling J, Sauer U, Szallasi Z, Doyle F, Doyle J (2004) Robustness of cellular functions. Cell 118:675-685

    Article  PubMed  Google Scholar 

  • 50. Stennicke H, Salvesen G (1999) Catalytic properties of the caspases. Cell Death Differ 6:1054-1059

    Article  PubMed  Google Scholar 

  • 51. Swameye I, Müller TG, Timmer J, Sandra O, Klingmüller U (2003) Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling. PNAS 100(3):1028-1033

    Article  PubMed  Google Scholar 

  • 52. Thornberry N, Lazebnik Y (1998) Caspases: Enemies within. Science 281: 1312-1316

    Article  PubMed  Google Scholar 

  • 53. Tomita M, Hashimoto K, Takahashi K, Shimizu T, Matsuzaki Y, Miyoshi F, Saito K, Tanida S, Yugi K, Venter J, Hutchison C (1999) E-CELL: Software environment for whole cell simulation”. Bioinformatics 15:72-84

    Article  PubMed  Google Scholar 

  • 54. Vacheva I, Bentele M, Eils R (2005) Optimal experiment design for discriminating between competing signal transduction models. (in preparation)

    Google Scholar 

  • 55. Varma A, Morbidelli M, Wu H (1999) Parametric Sensitivity in Chemical Systems. Cambridge University Press, New York

    Google Scholar 

  • 56. Westphal S, Kalthoff H (2003) Apoptosis: targets in pancreatic cancer. Mol Cancer 2(1):6

    Article  PubMed  Google Scholar 

  • 57. Wolfram S (1994) Cellular Automata and Complexity: Collected Papers. Addison-Wesley, Reading, MA

    Google Scholar 

  • 58. Zou H, Yang R, Hao J, Wang J, Sun C, Fesik S, Wu J, Tomaselli K, Armstrong R (2002) Regulation of the Apaf-1/caspase-9 apoptosome by caspase-3 and XIAP. J Biol Chem 278(10):8091-8098

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

    Martin Bentele & Roland Eils

Authors
  1. Martin Bentele
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roland Eils
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Lila Alberghina H.V. Westerhoff

Rights and permissions

Reprints and permissions

About this chapter

Cite this chapter

Bentele, M., Eils, R. Systems biology of apoptosis. In: Alberghina, L., Westerhoff, H. (eds) Systems Biology. Topics in Current Genetics, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/b137746

Download citation

Publish with us

Policies and ethics

Search

Navigation