[Rythme circadien et croissance tumorale]
L'objet de cette Note est de questionner, sur des bases mathématiques, le fait expérimental que les populations de cellules de souris cancéreuses échappant au contrôle circadien, ont tendance à se développer plus vite. Pour cela nous considérons un modèle du cycle cellulaire avec contrôle des taux de mort (apoptose) et de transition entre phases, et deux valeurs propres. L'une est associée aux coefficients périodiques via la théorie de Floquet (dans une version de dimension infinie), l'autre est associée au problème stationnaire avec des coefficients moyens. Nous montrons par une preuve directe que, de façon inattendue si l'on considère l'observation expérimentale évoquée plus haut, la valeur propre périodique est plus grande que la valeur propre stationnaire dans le cas où le contrôle périodique est effectué sur l'apoptose. Nous montrons aussi, par des tests numériques dans le cas où le contrôle périodique est effectué sur le taux de transition d'une phase à l'autre du cycle cellulaire, qu'il n'existe alors aucune hiérarchie naturelle entre les deux types de valeurs propres. Ceci montre au moins que, pour que de tels modèles puissent rendre compte des observations expérimentales ci-dessus, le seul contrôle des taux de mort dans chaque phase est insuffisant, et que les taux de transition entre phases sont une cible clef pour le contrôle de la prolifération.
We address the following question: can one sustain, on the basis of mathematical models, that for cancer cells, the loss of control by circadian rhythm favours a faster growth? This question, which comes from the observation that tumour growth in mice is enhanced by experimental disruption of the circadian rhythm, may be tackled by mathematical modelling of the cell cycle. For this purpose we consider an age-structured population model with control of death (apoptosis) rates and phase transitions, and two eigenvalues: one for periodic control coefficients (via a variant of Floquet theory in infinite dimension) and one for constant coefficients (taken as the time average of the periodic case). We show by a direct proof that, surprisingly enough considering the above-mentioned observation, the periodic eigenvalue is always greater than the steady state eigenvalue when the sole apoptosis rate is concerned. We also show by numerical simulations when transition rates between the phases of the cell cycle are concerned, that, without further hypotheses, no natural hierarchy between the two eigenvalues exists. This at least shows that, if such models are to take account of the above-mentioned observation, control of death rates inside phases is not sufficient, and that transition rates between phases are a key target in proliferation control.
Accepté le :
Publié le :
Jean Clairambault 1, 2 ; Philippe Michel 3, 4 ; Benoît Perthame 1, 3
@article{CRMATH_2006__342_1_17_0, author = {Jean Clairambault and Philippe Michel and Beno{\^\i}t Perthame}, title = {Circadian rhythm and tumour growth}, journal = {Comptes Rendus. Math\'ematique}, pages = {17--22}, publisher = {Elsevier}, volume = {342}, number = {1}, year = {2006}, doi = {10.1016/j.crma.2005.10.029}, language = {en}, }
Jean Clairambault; Philippe Michel; Benoît Perthame. Circadian rhythm and tumour growth. Comptes Rendus. Mathématique, Volume 342 (2006) no. 1, pp. 17-22. doi : 10.1016/j.crma.2005.10.029. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2005.10.029/
[1] A survey of structured cell population dynamics, Acta Biotheor., Volume 43 (1995), pp. 3-25
[2] A mathematical model for analysis of the cell cycle in cell lines derived from human tumors, J. Math. Biol., Volume 47 (2003) no. 4, pp. 295-312
[3] Desynchronization rate in cell populations: mathematical modeling and experimental data, J. Theor. Biol., Volume 208 (2001), pp. 185-199
[4] J. Clairambault, B. Laroche, S. Mischler, B. Perthame, A mathematical model of the cell cycle and its control, INRIA Research Report # 4892, 2003
[5] Effect of light and food schedules on liver and tumor molecular clocks in mice, J. Natl. Cancer Inst., Volume 97 (2005) no. 7, pp. 507-517
[6] The circadian gene Per2 plays an important role in tumor suppression and DNA damage response in vivo, Cell, Volume 111 (2002), pp. 41-50
[7] Biochemical Oscillations and Cellular Rhythms, Cambridge University Press, 1996
[8] A theory for the age and generation time distribution of a microbial population, J. Math. Biol., Volume 1 (1977), pp. 17-36
[9] Cancer Chronotherapeutics, Chronobiol. Int., 19 (2002) no. 1 (Special issue)
[10] Control mechanism of the circadian clock for timing of cell division in vivo, Science, Volume 302 (2003), pp. 255-259
[11] The dynamics of physiologically structured populations, Lecture Notes in Biomath., vol. 68, Springer-Verlag, 1986
[12] The entropy structure of models of structured population dynamics. General relative entropy inequality: an illustration on growth models, J. Math. Pures Appl., Volume 84 (2005) no. 9, pp. 1235-1260
[13] Stability in a nonlinear population maturation model, M3AS, Volume 12 (2002) no. 12, pp. 1751-1772
[14] Cancer chronotherapy: principles, applications and perspectives, Cancer, Volume 97 (2003) no. 1, pp. 155-169
[15] Transport theory for growing cell populations, J. Theor. Biol., Volume 103 (1983), pp. 181-199
[16] Liver regeneration clocks on, Science, Volume 302 (2003), pp. 234-235
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- Qualitative Properties of a Cell Proliferating Model with Multi-phase Transition and Age Structure, Advances in Science, Technology and Engineering Systems Journal, Volume 5 (2020) no. 6, p. 01 | DOI:10.25046/aj050601
- Bang–Bang Growth Rate Optimization in a Coupled McKendrick Model, Journal of Optimization Theory and Applications, Volume 183 (2019) no. 1, p. 332 | DOI:10.1007/s10957-019-01556-1
- Mathematical modeling of circadian rhythms, WIREs Systems Biology and Medicine, Volume 11 (2019) no. 2 | DOI:10.1002/wsbm.1439
- A multiscale modelling approach for the regulation of the cell cycle by the circadian clock, Journal of Theoretical Biology, Volume 426 (2017), p. 117 | DOI:10.1016/j.jtbi.2017.05.021
- How does variability in cell aging and growth rates influence the Malthus parameter?, Kinetic Related Models, Volume 10 (2017) no. 2, p. 481 | DOI:10.3934/krm.2017019
- Physiologically Structured Cell Population Dynamic Models with Applications to Combined Drug Delivery Optimisation in Oncology, Mathematical Modelling of Natural Phenomena, Volume 11 (2016) no. 6, p. 45 | DOI:10.1051/mmnp/201611604
- Cell Cycle as an Object of Control, System Engineering Approach to Planning Anticancer Therapies (2016), p. 9 | DOI:10.1007/978-3-319-28095-0_2
- Discrete limit and monotonicity properties of the Floquet eigenvalue in an age structured cell division cycle model, Journal of Mathematical Biology, Volume 71 (2015) no. 6-7, p. 1663 | DOI:10.1007/s00285-015-0874-3
- Deterministic Mathematical Modelling for Cancer Chronotherapeutics: Cell Population Dynamics and Treatment Optimization, Mathematical Oncology 2013 (2014), p. 265 | DOI:10.1007/978-1-4939-0458-7_9
- Synchronisation and control of proliferation in cycling cell population models with age structure, Mathematics and Computers in Simulation, Volume 96 (2014), p. 66 | DOI:10.1016/j.matcom.2012.03.005
- Designing proliferating cell population models with functional targets for control by anti-cancer drugs, Discrete Continuous Dynamical Systems - B, Volume 18 (2013) no. 4, p. 865 | DOI:10.3934/dcdsb.2013.18.865
- Modeling Biological Rhythms in Cell Populations, Mathematical Modelling of Natural Phenomena, Volume 7 (2012) no. 6, p. 107 | DOI:10.1051/mmnp/20127606
- Circadian rhythm and cell population growth, Mathematical and Computer Modelling, Volume 53 (2011) no. 7-8, p. 1558 | DOI:10.1016/j.mcm.2010.05.034
- Tumor Growth Rate Determines the Timing of Optimal Chronomodulated Treatment Schedules, PLoS Computational Biology, Volume 6 (2010) no. 3, p. e1000712 | DOI:10.1371/journal.pcbi.1000712
- Mathematical modeling as a tool for planning anticancer therapy, European Journal of Pharmacology, Volume 625 (2009) no. 1-3, p. 108 | DOI:10.1016/j.ejphar.2009.08.041
- Comparison of Perron and Floquet Eigenvalues in Age Structured Cell Division Cycle Models, Mathematical Modelling of Natural Phenomena, Volume 4 (2009) no. 3, p. 183 | DOI:10.1051/mmnp/20094308
- A Step Toward Optimization of Cancer Therapeutics [Chronobiological Investigations], IEEE Engineering in Medicine and Biology Magazine, Volume 27 (2008) no. 1, p. 20 | DOI:10.1109/memb.2007.907363
- An age-and-cyclin-structured cell population model for healthy and tumoral tissues, Journal of Mathematical Biology, Volume 57 (2008) no. 1, p. 91 | DOI:10.1007/s00285-007-0147-x
- Analysis of a molecular structured population model with possible polynomial growth for the cell division cycle, Mathematical and Computer Modelling, Volume 47 (2008) no. 7-8, p. 699 | DOI:10.1016/j.mcm.2007.06.008
- A cell cycle automaton model for probing circadian patterns of anticancer drug delivery, Advanced Drug Delivery Reviews, Volume 59 (2007) no. 9-10, p. 1036 | DOI:10.1016/j.addr.2006.09.022
- Modeling oxaliplatin drug delivery to circadian rhythms in drug metabolism and host tolerance, Advanced Drug Delivery Reviews, Volume 59 (2007) no. 9-10, p. 1054 | DOI:10.1016/j.addr.2006.08.004
- Optimizing Temporal Patterns of Anticancer Drug Delivery by Simulations of a Cell Cycle Automaton, Biosimulation in Drug Development (2007), p. 273 | DOI:10.1002/9783527622672.ch10
- A Mathematical Model of the Cell Cycle and Its Circadian Control, Mathematical Modeling of Biological Systems (2007), p. 239 | DOI:10.1007/978-0-8176-4558-8_21
- , 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (2006), p. 173 | DOI:10.1109/iembs.2006.260855
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