Free Access
Issue
Math. Model. Nat. Phenom.
Volume 6, Number 7, 2011
Mathematical modeling in biomedical applications
Page(s) 2 - 12
DOI https://doi.org/10.1051/mmnp/20116701
Published online 15 June 2011
  1. A. R.A. Anderson.A hybrid multiscale model of solid tumour growth and invasion: Evolution and the microenvironment. in Single-Cell-Based Models in Biology and Medicine (Ed. A.R.A. Anderson, M.A.J. Chaplain and K.A. Rejniak), Series Mathematics and Biosciences in Interaction, Springer, Birkhauser Basel, 2007, 3–28. [Google Scholar]
  2. A.R.A. Anderson, M. Chaplain, K.A. Rejniak. Single cell based models in biology and medicine, Mathematics and Biosciences in Interaction. Springer, Birkhauser Basel, 2007. [Google Scholar]
  3. A. R. A. Anderson, K.A. Rejniak, P. Gerlee, V. Quaranta. Modelling of cancer growth, evolution and invasion: bridging scales and models. Math. Model. Nat. Phenom., 2(3) (2007), 1–29. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  4. J. Bélair, M.C. Mackey, J.M. Mahaffy. Age-structured and two delay models for erythropoiesis. Math. Biosci., 128 (1995), 317–346. [CrossRef] [PubMed] [Google Scholar]
  5. N. Bessonov, L. Pujo-Menjouet, V. Volpert. Cell modelling of hematopoiesis. Math. Model. Nat. Phenom., 1 (2006), No. 2, 81–103. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  6. N. Bessonov, I. Demin, L. Pujo-Menjouet, V. Volpert. A multi-agent model describing self-renewal or differentiation effect of blood cell population. Mathematical and Computer Modelling, 49 (2009), 2116–2127. [CrossRef] [MathSciNet] [Google Scholar]
  7. N. Bessonov, P. Kurbatova, V. Volpert. Particle dynamics modelling of cell populations. Prooceedings of the conference JANO, Mohamadia 2008, Math. Model. Nat. Phenom., 5 (2010), No. 7, 42–47. [Google Scholar]
  8. N. Bessonov, P. Kurbatova, V. Volpert. Dynamics of growing cell populations. CRM, preprint num. 931 for Mathematical Biology, February 2010. [Google Scholar]
  9. J.A. Chasis, N. Mohandas. Erythroblastic islands: niches for erythropoiesis. Blood, 112 (2008), pp. 470-478. [Google Scholar]
  10. F. Crauste, I. Demin, O. Gandrillon, V. Volpert. Mathematical study of feedback control roles and relevance in stress erythropoiesis. J. Theo. Biol., 263 (2010), 303–316. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  11. F. Crauste, L. Pujo-Menjouet, S. Génieys, C. Molina, O. Gandrillon. Adding self-renewal in committed erythroid progenitors improves the biological relevance of a mathematical model of erythropoiesis. J. Theor. Biol., 250 (2008), 322–338. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  12. I. Demin, F. Crauste, O. Gandrillon, V. Volpert. A multi-scale model of erythropoiesis, J. Biol. Dyn. 4 (2010), pp. 59–70. [Google Scholar]
  13. D. Drasdo.Center-based single-cell models: An approach to multi-cellular organization based on a conceptual analogy to colloidal particles. In: Single-Cell-Based Models in Biology and Medicine (Ed. A.R.A. Anderson, M.A.J. Chaplain and K.A. Rejniak), Series Mathematics and Biosciences in Interaction, Springer, Birkhauser Basel, 2007, 171-196. [Google Scholar]
  14. O. Gandrillon, U. Schmidt, H. Beug, J. Samarut. TGF-beta cooperates with TGF-alpha to induce the self-renewal of normal erythrocytic progenitors: evidence for an autocrine mechanism. EMBO J., 18 (1999), 2764–2781. [CrossRef] [PubMed] [Google Scholar]
  15. M. Karttunen, I. Vattulainen, A.Lukkarinen. A novel methods in soft matter simulations, Springer, Berlin, 2004. [Google Scholar]
  16. M.J. Koury, M.C. Bondurant. Erythropoietin retards DNA breakdown and prevents programmed death in erythroid progenitor cells, Science, 248 (1990), 378–381. [CrossRef] [PubMed] [Google Scholar]
  17. C. Rubiolo, D. Piazzolla, K. Meissl, H. Beug, J.C. Huber, A. Kolbus, M. Baccarini. A balance between Raf-1 and Fas expression sets the pace of erythroid differentiation. Blood, 108 (2006), 152–159. [CrossRef] [PubMed] [Google Scholar]

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