Analysis of a System Describing Proliferative-Quiescent Cell Dynamics

Jean Clairambault , Benoît Perthame , Andrada Quillas Maran

Chinese Annals of Mathematics, Series B ›› 2018, Vol. 39 ›› Issue (2) : 345 -356.

PDF
Chinese Annals of Mathematics, Series B ›› 2018, Vol. 39 ›› Issue (2) : 345 -356. DOI: 10.1007/s11401-018-1068-2
Article

Analysis of a System Describing Proliferative-Quiescent Cell Dynamics

Author information +
History +
PDF

Abstract

Systems describing the dynamics of proliferative and quiescent cells are commonly used as computational models, for instance for tumor growth and hematopoiesis. Focusing on the very earliest stages of hematopoiesis, stem cells and early progenitors, the authors introduce a new method, based on an energy/Lyapunov functional to analyze the long time behavior of solutions. Compared to existing works, the method in this paper has the advantage that it can be extended to more complex situations. The authors treat a system with space variable and diffusion, and then adapt the energy functional to models with three equations.

Keywords

Stability analysis / Lyapunov functional / Energy method / Parabolic systems / Proliferative and quiescent cells / Tumor growth / Hematopoiesis

Cite this article

Download citation ▾
Jean Clairambault, Benoît Perthame, Andrada Quillas Maran. Analysis of a System Describing Proliferative-Quiescent Cell Dynamics. Chinese Annals of Mathematics, Series B, 2018, 39(2): 345-356 DOI:10.1007/s11401-018-1068-2

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Adimy M., Crauste F.. Mathematical model of hematopoiesis dynamics with growth factordependent apoptosis and proliferation regulations. Math. Comput. Modelling, 2009, 49(11–12): 2128-2137

[2]

Adimy M., Crauste F., Abdllaoui A. E.. Discrete maturity-structured model of cell differentiation with applications to acute myelogenous leukemia. Journal of Biological Systems, 2008, 16(3): 395-424

[3]

Alexandra J., Ryan N. G.. Effect of Dedifferentiation on Time to Mutation Acquisition in Stem Cell-Driven Cancers. PLoS Computational Biology, 2014

[4]

Bresch D., Colin T., Grenier E. Computational modeling of solid tumor growth: The avascular stage. SIAM J. Sci. Comput., 2010, 32(4): 2321-2344

[5]

Cai S., Fu X., Sheng Z.. Dedifferentiation: A new approach in stem cell research. AIBS Bulletin, 2007, 57(8): 655-662

[6]

DiBenedetto E.. Partial Differential Equations, Cornerstones, 2010 2 Boston: Birkhäuser

[7]

Dingli D., Pacheco J. M.. Allometric scaling of the active hematopoietic stem cell pool across mammals. PLoS One, 2006

[8]

Dingli D., Traulsen A., Pacheco J. M.. Compartmental architecture and dynamics of hematopoiesis. PLoS One, 2007

[9]

Drobnjak I., Fowler A. C., Mackey M. C.. Oscillations in a maturation model of blood cell production. SIAM J. Appl. Math., 2006, 66(6): 2027-2048

[10]

Dyson J., Villella-Bressan R., Webb G.. A maturity structured model of a population of proliferating and quiescent cells. Arch. Control Sci., 1999, 9(1–2): 201-225

[11]

Evans L. C.. Partial differential equations, Graduate Studies in Mathematics, 2010, Providence, RI: American Mathematical Society

[12]

Friedmann-Morvinski D., Verma I. M.. Dedifferentiation and reprogramming: Origins of cancer stem cells. EMBO Reports, 2014, 15: 244-253

[13]

Gyllenberg M., Webb G. F.. Quiescence as an explanation of gompertzian tumor growth, Growth, Development. and Aging: GDA, 1989, 53(1–2): 25-33

[14]

Gyllenberg M., Webb G. F.. A nonlinear structured population model of tumor growth with quiescence. J. Math. Biol., 1990, 28(6): 671-694

[15]

Gyllenberg M., Webb G. F.. Quiescence in structured population dynamics: Applications to tumor growth. Mathematical Population Dynamics, 1991 45-62

[16]

Hartung N.. Parameter non-identifiability of the Gyllenberg-Webb ODE model. J. Math. Biol., 2014, 68(1–2): 41-55

[17]

Hirsch P., Zhang Y., Tang R. Genetic hierarchy and temporal variegation in the clonal history of acute myeloid leukaemia. Nature Communications, 2016

[18]

Leder K., Holland E. C., Michor F.. The therapeutic implications of plasticity of the cancer stem cell phenotype. PLoS One, 2010

[19]

Mackey M. C.. Unified hypothesis for the origin of aplastic anemia and periodic hematopoiesis. Blood, 1978, 51(5): 941-956

[20]

Perthame B.. Parabolic Equations in Biology, Lecture Notes on Mathematical Modelling in the Life Sciences, 2015, Cham: Springer-Verlag

[21]

Pujo-Menjouet L.. Blood cell dynamics: Half of a century of modelling. Mathematical Modelling of Natural Phenomena, 2016, 11(1): 92-115

[22]

Quittner P., Souplet P.. Superlinear parabolic problems, Birkhäuser Advanced Texts: Basler Lehrbücher, 2007, Basel: Birkhäuser

[23]

Ribba B., Saut O., Colin T. A multiscale mathematical model of avascular tumor growth to investigate the therapeutic benefit of anti-invasive agents. J. Theoret. Biol., 2006, 243(4): 532-541

[24]

Stiehl T., Baran N., Ho A. D., Marciniak-Czochra A.. Clonal selection and therapy resistance in acute leukaemias: Mathematical modelling explains different proliferation patterns at diagnosis and relapse. Journal of The Royal Society Interface, 2014

[25]

Yamada Y., Haga H., Yamada Y.. Concise review: Dedifferentiation meets cancer development: Proof of concept for epigenetic cancer. Stem Cells Translational Medicine, 2014, 3: 1182-1187

AI Summary AI Mindmap
PDF

122

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/