Cell Death and Life in Cancer: Mathematical Modeling of Cell Fate Decisions

  • Andrei ZinovyevEmail author
  • Simon Fourquet
  • Laurent Tournier
  • Laurence Calzone
  • Emmanuel Barillot
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)


Tumor development is characterized by a compromised balance between cell life and death decision mechanisms, which are tightly regulated in normal cells. Understanding this process provides insights for developing new treatments for fighting with cancer. We present a study of a mathematical model describing cellular choice between survival and two alternative cell death modalities: apoptosis and necrosis. The model is implemented in discrete modeling formalism and allows to predict probabilities of having a particular cellular phenotype in response to engagement of cell death receptors. Using an original parameter sensitivity analysis developed for discrete dynamic systems, we determine variables that appear to be critical in the cellular fate decision and discuss how they are exploited by existing cancer therapies.


Fragile Site Cellular Phenotype Logical Rule Priority Class Cell Fate Decision 
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.



We would like to acknowledge support by the APO-SYS EU FP7 project. A. Zinovyev, S. Fourquet, L. Calzone and E. Barillot are members of the team “Systems Biology of Cancer”, Equipe labellisee par la Ligue Nationale Contre le Cancer. L. Tournier is member of the Systems Biology team in the laboratory MIG of INRA (French Institute for Agronomical Research). The study was also funded by the Projet Incitatif Collaboratif “Bioinformatics and Biostatistics of Cancer” at Institut Curie.


  1. 1.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell;144(5): 646–674.PubMedCrossRefGoogle Scholar
  2. 2.
    McCormick F (2004) Cancer: survival pathways meet their end. Nature 428(6980):267–269.PubMedCrossRefGoogle Scholar
  3. 3.
    Kroemer G., et al. (2008) Classification of cell death: recommendations of the Nomenclature Committee on Cell Death 2009. Cell Death Differ 16(1):3–11.PubMedCrossRefGoogle Scholar
  4. 4.
    Calzone L, Tournier L, Fourquet S, Thieffry D, Zhivotovsky B, Barillot E, Zinovyev A. (2010) Mathematical modelling of cell-fate decision in response to death receptor engagement. PLoS Comput Biol 6(3):e1000702.PubMedCrossRefGoogle Scholar
  5. 5.
    Van Herreweghe F, Festjens N, Declercq W, Vandenabeele P (2010) Tumor necrosis factor-mediated cell death: to break or to burst, that’s the question. Cell Mol Life Sci 67(10): 1567–1579.PubMedCrossRefGoogle Scholar
  6. 6.
    Balazsi G, van Oudenaarden A, Collins JJ (2011) Cellular decision making and biological noise: from microbes to mammals. Cell 144(6):910–925.PubMedCrossRefGoogle Scholar
  7. 7.
    Naldi A, Remy E, Thieffry D, Chaouiya C (2009) A reduction method for logical regulatory graphs preserving essential dynamical properties. Lecture Notes in Computer Science 5688:266–280.CrossRefGoogle Scholar
  8. 8.
    Chaouiya C, de Jong H, Thieffry D. (2006) Dynamical modeling of biological regulatory networks. Biosystems 84(2):77–80.PubMedCrossRefGoogle Scholar
  9. 9.
    Tournier L. and Chaves M. (2009) Uncovering operational interactions in genetic networks using asynchronous boolean dynamics. J Theor Biol 260(2):196–209.PubMedCrossRefGoogle Scholar
  10. 10.
    Feller W (1968) An introduction to probability theory and its applications, vol. 1 Wiley, New York.Google Scholar
  11. 11.
    Turanyi, T (1990). Sensitivity analysis of complex kinetic systems. Tools and applications. J Math Chem 5:203–248.Google Scholar
  12. 12.
    Fauré A, Naldi A, Chaouiya C, Thieffry D (2006) Dynamical analysis of a generic boolean model for the control of the mammalian cell cycle. Bioinformatics 22(14):e124–e131.PubMedCrossRefGoogle Scholar
  13. 13.
    Naldi A, Berenguier D, Faure A, Lopez F, Thieffry D, Chaouiya C. (2009) Logical modelling of regulatory networks with GINsim 2.3. Biosystems 97(2):134–139.Google Scholar
  14. 14.
    Rampino N, Yamamoto H, Ionov Y, Li Y, Sawai H, et al. (1997) Somatic frameshift mutations in the BAX gene in colon cancers of the microsatellite mutator phenotype. Science 275: 967–969.PubMedCrossRefGoogle Scholar
  15. 15.
    Lissat A, Vraetz T, Tsokos M, Klein R, Braun M, et al. (2007) Interferon-gamma sensitizes resistant Ewing’s sarcoma cells to tumor necrosis factor apoptosis-inducing ligand-induced apoptosis by up-regulation of caspase-8 without altering chemosensitivity. Am J Pathol 170:1917–1930.PubMedCrossRefGoogle Scholar
  16. 16.
    Teitz T, Lahti JM, Kidd VJ (2001) Aggressive childhood neuroblastomas do not express caspase-8: an important component of programmed cell death. J Mol Med 79:428–436.PubMedCrossRefGoogle Scholar
  17. 17.
    Shivapurkar N, Toyooka S, Eby MT, Huang CX, Sathyanarayana UG, et al. (2002) Differential inactivation of caspase-8 in lung cancers. Cancer Biol Ther 1:65–69.PubMedCrossRefGoogle Scholar
  18. 18.
    Croce CM (2008) Oncogenes and cancer. N Engl J Med 358:502–511.PubMedCrossRefGoogle Scholar
  19. 19.
    Karin M, Cao Y, Greten FR, Li ZW (2002) NF-kappaB in cancer: from innocent bystander to major culprit. Nat Rev Cancer 2:301–310.PubMedCrossRefGoogle Scholar
  20. 20.
    Dai Z, Zhu WG, Morrison CD, Brena RM, Smiraglia DJ, et al. (2003) A comprehensive search for DNA amplification in lung cancer identifies inhibitors of apoptosis cIAP1 and cIAP2 as candidate oncogenes. Hum Mol Genet 12:791–801.PubMedCrossRefGoogle Scholar
  21. 21.
    Imoto I, Tsuda H, Hirasawa A, Miura M, Sakamoto M, et al. (2002) Expression of cIAP1, a target for 11q22 amplification, correlates with resistance of cervical cancers to radiotherapy. Cancer Res 62:4860–4866.PubMedGoogle Scholar
  22. 22.
    Imoto I, Yang ZQ, Pimkhaokham A, Tsuda H, Shimada Y, et al. (2001) Identification of cIAP1 as a candidate target gene within an amplicon at 11q22 in esophageal squamous cell carcinomas. Cancer Res 61:6629–6634.PubMedGoogle Scholar
  23. 23.
    Chen DJ, Huerta S (2009) Smac mimetics as new cancer therapeutics. Anticancer Drugs 20(8): 646–658.PubMedCrossRefGoogle Scholar
  24. 24.
    Ready N, Karaseva NA, Orlov SV, Luft AV, Popovych O, Holmlund JT, Wood BA, Leopold L (2011) Double-blind, placebo-controlled, randomized phase 2 study of the proapoptotic agent AT-101 plus docetaxel, in second-line non-small cell lung cancer. J Thorac Oncol 6(4): 781–785.PubMedCrossRefGoogle Scholar
  25. 25.
    Lavrik IN (2010) Systems biology of apoptosis signaling networks. Curr Opin Biotechnol 21(4):551–555.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Andrei Zinovyev
    • 1
    Email author
  • Simon Fourquet
    • 1
  • Laurent Tournier
    • 2
  • Laurence Calzone
    • 1
  • Emmanuel Barillot
    • 1
  1. 1.U900 INSERM/Institut Curie/Ecole de MinesInstitut CurieParisFrance
  2. 2.INRA, Unit MIG (Mathématiques, Informatique et Génome)Domaine VilvertParisFrance

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