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Modelling Tumour-Immunity Interactions With Different Stimulation Functions

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Tumour immunotherapy is aimed at the stimulation of the otherwise inactive immune system to remove, or at least to restrict, the growth of the original tumour and its metastases. The tumour-immune system interactions involve the stimulation of the immune response by tumour antigens, but also the tumour induced death of lymphocytes. A system of two non-linear ordinary differential equations was used to describe the dynamic process of interaction between the immune system and the tumour. Three different types of stimulation functions were considered: (a) Lotka-Volterra interactions, (b) switching functions dependent on the tumour size in the Michaelis-Menten form, and (c) Michaelis-Menten switching functions dependent on the ratio of the tumour size to the immune capacity. The linear analysis of equilibrium points yielded several different types of asymptotic behaviour of the system: unrestricted tumour growth, elimination of tumour or stabilization of the tumour size if the initial tumour size is relatively small, otherwise unrestricted tumour growth, global stabilization of the tumour size, and global elimination of the tumour. Models with switching functions dependent on the tumour size and the tumour to the immune capacity ratio exhibited qualitatively similar asymptotic behaviour.
Rocznik
Strony
307--315
Opis fizyczny
Bibliogr. 12 poz., wykr.
Twórcy
autor
  • Institute of Applied Mathematics and Mechanics, Warsaw University, ul. Banacha 2, 02–097 Warsaw, Poland
autor
  • Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland; Interdisciplinary Center for Mathematical and Computer Modelling Warsaw University, Warsaw, Poland
Bibliografia
  • [1] Bell G. (1973): Predator-prey equation simulating an immune response. — Math. Biosci., Vol. 16, pp. 291–314.
  • [2] Chen Ch-H. and Wu T.C. (1998): Experimental vaccines strategies for cancer immunotherapy.—J. Biomed. Sci., Vol. 5, No. 5, pp. 231–252.
  • [3] de Pillis L.G. and Radunskaya A.E. (2001): A mathematical tumor model with immune resistance and drug therapy: An optimal control approach. — J. Theor. Med., Vol. 3, pp. 79–100.
  • [4] Fong L. and Engleman E. (2000): Dendritic cells in cancer immunology.— Ann. Rev. Immunol., Vol. 18, pp. 245–273.
  • [5] Ginzburg L.R. and Akcakaya H.R. (1992): Consequences of ratio-dependent predation for steady-state properties of ecosystems.—Ecology, Vol. 73, pp. 1536–1543.
  • [6] Kuby J. (1998): Immunology.—New York: Freeman & Co.
  • [7] Mayer H., Zaenker K.S. and an der Heiden U. (1995): A basic mathematical model of the immune response.—Chaos, Vol. 5, No. 1, pp. 155–161.
  • [8] Moingeon P. (2001): Cancer vaccines. — Vaccines, Vol. 19, No. 11–12, pp. 1305–1326.
  • [9] Prikrylova D., Jilek M. and Waniewski J. (1992): Mathematical Modelling of the Immune Response. — Boca Raton: CRC Press.
  • [10] Romanovski I., Stepanova N. and Chernavski D. (1975): Mathematical Modelling in Biophysics.—Moscow: Nauka, (in Russian).
  • [11] Rosenberg St. (2001): Progress in human tumour immunology and immunotherapy. — Nature, Vol. 411, No. 6835, pp. 380–385.
  • [12] William E. (Ed.) (1984): Fundamental Immunology. — New York: Raven Press.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0002-0028
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