Free Access
Med Sci (Paris)
Volume 33, Number 12, Décembre 2017
Page(s) 1055 - 1062
Section M/S Revues
Published online 20 December 2017
  1. Martincorena I, Campbell PJ. Somatic mutation in cancer and normal cells. Science 2015 ; 349 : 1483–1489. [Google Scholar]
  2. Egeblad M, Nakasone ES, Werb Z. Tumors as organs: complex tissues that interface with the entire organism. Dev Cell 2010 ; 18 : 884–901. [CrossRef] [PubMed] [Google Scholar]
  3. Fey D, Matallanas D, Rauch J, et al. The complexities and versatility of the RAS-to-ERK signalling system in normal and cancer cells. Semin Cell Dev Biol 2016 ; 58 : 96–107. [CrossRef] [PubMed] [Google Scholar]
  4. Chapman PB, Hauschild A, Robert C, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med 2011 ; 364 : 2507–2516. [Google Scholar]
  5. Das Thakur M, Salangsang F, Landman AS, et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 2013; 494 : 251–255. [CrossRef] [PubMed] [Google Scholar]
  6. Jordan NV, Bardia A, Wittner BS, et al. HER2 expression identifies dynamic functional states within circulating breast cancer cells. Nature 2016 ; 537 : 102–106. [CrossRef] [PubMed] [Google Scholar]
  7. Barillot E, Calzone L, Zinovyev A. Biologie des systèmes appliquée aux cancers. Med Sci (Paris) 2009 ; 25 : 601–607. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  8. Janes KA, Lauffenburger DA. Models of signalling networks: what cell biologists can gain from them and give to them. J Cell Sci 2013 ; 126 : 1913–1921. [Google Scholar]
  9. Kolch W, Halasz M, Granovskaya M, Kholodenko BN. The dynamic control of signal transduction networks in cancer cells. Nat Rev Cancer 2015 ; 15 : 515–527. [Google Scholar]
  10. Altrock PM, Liu LL, Michor F. The mathematics of cancer: integrating quantitative models. Nat Rev Cancer 2015 ; 15 : 730–745. [Google Scholar]
  11. Martin B, Mahé F. Un modèle mathématique pour l’étude des interactions bactériennes en biofilm Med Sci (Paris) 2017 ; 33 : 1035–1038. [Google Scholar]
  12. Orton RJ, Sturm OE, Vyshemirsky V, et al. Computational modelling of the receptor-tyrosine-kinase-activated MAPK pathway. Biochem J 2005 ; 392 : 249–261. [CrossRef] [PubMed] [Google Scholar]
  13. Huang CY, Ferrell JE Jr. Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc Natl Acad Sci USA 1996 ; 93 : 10078–10083. [CrossRef] [Google Scholar]
  14. Ferrell JE Jr, Machleder EM. The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 1998 ; 280 : 895–898. [Google Scholar]
  15. Levchenko A, Bruck J, Sternberg PW. Scaffold proteins may biphasically affect the levels of mitogen-activated protein kinase signaling and reduce its threshold properties. Proc Natl Acad Sci USA 2000 ; 97 : 5818–5823. [CrossRef] [Google Scholar]
  16. Sasagawa S, Ozaki Y, Fujita K, Kuroda S. Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nat Cell Biol 2005 ; 7 : 365–373. [CrossRef] [PubMed] [Google Scholar]
  17. Nakakuki T, Birtwistle MR, Saeki Y, et al. Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Cell 2010 ; 141 : 884–896. [CrossRef] [PubMed] [Google Scholar]
  18. Saidak Z, Giacobbi AS, Louandre C, et al. Mathematical modelling unveils the essential role played by cellular phosphatases in the inhibition of RAF-MEK-ERK signalling by sorafenib in Hepatocellular carcinoma cells. Cancer Lett 2017 ; 392 : 1–8. [Google Scholar]
  19. Jensen KJ, Moyer CB, Janes KA. Network architecture predisposes an enzyme to either pharmacologic or genetic targeting. Cell Syst 2016 ; 2 : 112–121. [CrossRef] [PubMed] [Google Scholar]
  20. Klinger B, Sieber A, Fritsche-Guenther R, et al. Network quantification of EGFR signaling unveils potential for targeted combination therapy. Mol Syst Biol 2013 ; 9 : 673. [Google Scholar]
  21. Halasz M, Kholodenko BN, Kolch W, Santra T. Integrating network reconstruction with mechanistic modeling to predict cancer therapies. Sci Signal 2016; 9 : ra114. [Google Scholar]
  22. Albeck JG, Mills GB, Brugge JS. Frequency-modulated pulses of ERK activity transmit quantitative proliferation signals. Mol Cell 2013 ; 49 : 249–261. [CrossRef] [PubMed] [Google Scholar]
  23. Voliotis M, Perrett RM, McWilliams C, et al. Information transfer by leaky, heterogeneous, protein kinase signaling systems. Proc Natl Acad Sci USA 2014 ; 111 : E326–E333. [CrossRef] [Google Scholar]
  24. Ryu H, Chung M, Dobrzyn´ski M, et al. Frequency modulation of ERK activation dynamics rewires cell fate. Mol Syst Biol 2015 ; 11 : 838. [Google Scholar]
  25. Filippi S, Barnes CP, Kirk PD, et al. Robustness of MEK-ERK dynamics and origins of cell-to-cell variability in MAPK signaling. Cell Rep 2016 ; 15 : 2524–2535. [CrossRef] [PubMed] [Google Scholar]
  26. Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature 2013 ; 501 : 328–337. [CrossRef] [PubMed] [Google Scholar]
  27. Abou-Jaoudé W, Ouattara DA, Kaufman M. From structure to dynamics: frequency tuning in the p53-Mdm2 network I. Logical approach. J Theor Biol 2009 ; 258 : 561–577. [CrossRef] [Google Scholar]
  28. von der Heyde S, Bender C, Henjes F, et al. Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines. BMC Syst Biol 2014 ; 8 : 75. [PubMed] [Google Scholar]
  29. Abou-Jaoudé W, Traynard P, Monteiro PT, et al. Logical modeling and dynamical analysis of cellular networks. Front Genet 2016 ; 7 : 94. [PubMed] [Google Scholar]
  30. Udyavar AR, Wooten DJ, Hoeksema M, et al. Novel hybrid phenotype revealed in small cell lung cancer by a transcription factor network model that can explain tumor heterogeneity. Cancer Res 2017 ; 77 : 1063–1074. [Google Scholar]
  31. Eduati F, Doldàn-Martelli V, Klinger B, et al. Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic models. Cancer Res 2017 ; 77 : 3364–3375. [Google Scholar]
  32. Yankeelov TE, Quaranta V, Evans KJ, Rericha EC. Toward a science of tumor forecasting for clinical oncology. Cancer Res 2015 ; 75 : 918–923. [Google Scholar]
  33. Tuncbag N, Milani P, Pokorny JL, et al. Network modeling identifies patient-specific pathways in glioblastoma. Sci Rep 2016 ; 6 : 28668. [CrossRef] [PubMed] [Google Scholar]
  34. Fey D, Halasz M, Dreidax D, et al. Signaling pathway models as biomarkers: Patient-specific simulations of JNK activity predict the survival of neuroblastoma patients. Sci Signal 2015; 8 : ra130. [Google Scholar]
  35. Marusyk A, Tabassum DP, Altrock PM, et al. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature 2014 ; 514 : 54–58. [CrossRef] [PubMed] [Google Scholar]
  36. Almendro V, Cheng YK, Randles A, et al. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Rep 2014 ; 6 : 514–527. [CrossRef] [PubMed] [Google Scholar]
  37. Chisholm RH, Lorenzi T, Lorz A, et al. Emergence of drug tolerance in cancer cell populations: an evolutionary outcome of selection, nongenetic instability, and stress-induced adaptation. Cancer Res 2015 ; 75 : 930–939. [Google Scholar]
  38. Bhang HE, Ruddy DA, Krishnamurthy Radhakrishna V, et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat Med 2015 ; 21 : 440–448. [CrossRef] [PubMed] [Google Scholar]
  39. Barbolosi D, Ciccolini J, Lacarelle B, et al. Computational oncology: mathematical modelling of drug regimens for precision medicine. Nat Rev Clin Oncol 2016 ; 13 : 242–254. [PubMed] [Google Scholar]

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