Parameters identification for a model of T cell homeostasis
-
1.
IMB UMR CNRS 5251, Bordeaux University, 3 Place de la Victoire, 33076 Bordeaux Cedex
-
2.
INSERM U897, ISPED, Bordeaux University, Bordeaux
-
Received:
01 November 2014
Accepted:
29 June 2018
Published:
01 June 2015
-
-
MSC :
Primary: 92B05, 58J45; Secondary: 93B30.
-
-
In this study, we consider a model of T cell homeostasis based on the Smith-Martin model. This nonlinear model is structured by age and CD44 expression. First, we establish the mathematical well-posedness of the model system. Next, we prove the theoretical identifiability regarding the up-regulation of CD44, the proliferation time phase and the rate of entry into division, by using the experimental data. Finally, we compare two versions of the Smith-Martin model and we identify which model fits the experimental data best.
Citation: Houssein Ayoub, Bedreddine Ainseba, Michel Langlais, Rodolphe Thiébaut. Parameters identification for a model of T cell homeostasis[J]. Mathematical Biosciences and Engineering, 2015, 12(5): 917-936. doi: 10.3934/mbe.2015.12.917
Related Papers:
[1] |
Awatif Jahman Alqarni, Azmin Sham Rambely, Sana Abdulkream Alharbi, Ishak Hashim .
Dynamic behavior and stabilization of brain cell reconstitution after stroke under the proliferation and differentiation processes for stem cells. Mathematical Biosciences and Engineering, 2021, 18(5): 6288-6304.
doi: 10.3934/mbe.2021314
|
[2] |
Mohammad A. Tabatabai, Wayne M. Eby, Karan P. Singh, Sejong Bae .
T model of growth and its application in systems of tumor-immunedynamics. Mathematical Biosciences and Engineering, 2013, 10(3): 925-938.
doi: 10.3934/mbe.2013.10.925
|
[3] |
Lingli Zhou, Fengqing Fu, Yao Wang, Ling Yang .
Interlocked feedback loops balance the adaptive immune response. Mathematical Biosciences and Engineering, 2022, 19(4): 4084-4100.
doi: 10.3934/mbe.2022188
|
[4] |
A. M. Elaiw, N. H. AlShamrani, A. D. Hobiny .
Stability of an adaptive immunity delayed HIV infection model with active and silent cell-to-cell spread. Mathematical Biosciences and Engineering, 2020, 17(6): 6401-6458.
doi: 10.3934/mbe.2020337
|
[5] |
Eduardo Ibarguen-Mondragon, Lourdes Esteva, Leslie Chávez-Galán .
A mathematical model for cellular immunology of tuberculosis. Mathematical Biosciences and Engineering, 2011, 8(4): 973-986.
doi: 10.3934/mbe.2011.8.973
|
[6] |
Azmy S. Ackleh, Jeremy J. Thibodeaux .
Parameter estimation in a structured erythropoiesis model. Mathematical Biosciences and Engineering, 2008, 5(4): 601-616.
doi: 10.3934/mbe.2008.5.601
|
[7] |
Glenn Webb .
The force of cell-cell adhesion in determining the outcome in a nonlocal advection diffusion model of wound healing. Mathematical Biosciences and Engineering, 2022, 19(9): 8689-8704.
doi: 10.3934/mbe.2022403
|
[8] |
Atefeh Afsar, Filipe Martins, Bruno M. P. M. Oliveira, Alberto A. Pinto .
A fit of CD4+ T cell immune response to an infection by lymphocytic choriomeningitis virus. Mathematical Biosciences and Engineering, 2019, 16(6): 7009-7021.
doi: 10.3934/mbe.2019352
|
[9] |
A. M. Elaiw, N. H. AlShamrani .
Stability of HTLV/HIV dual infection model with mitosis and latency. Mathematical Biosciences and Engineering, 2021, 18(2): 1077-1120.
doi: 10.3934/mbe.2021059
|
[10] |
Khaphetsi Joseph Mahasa, Rachid Ouifki, Amina Eladdadi, Lisette de Pillis .
A combination therapy of oncolytic viruses and chimeric antigen receptor T cells: a mathematical model proof-of-concept. Mathematical Biosciences and Engineering, 2022, 19(5): 4429-4457.
doi: 10.3934/mbe.2022205
|
-
Abstract
In this study, we consider a model of T cell homeostasis based on the Smith-Martin model. This nonlinear model is structured by age and CD44 expression. First, we establish the mathematical well-posedness of the model system. Next, we prove the theoretical identifiability regarding the up-regulation of CD44, the proliferation time phase and the rate of entry into division, by using the experimental data. Finally, we compare two versions of the Smith-Martin model and we identify which model fits the experimental data best.
References
[1]
|
Mathematical biosciences, 251 (2014), 63-71.
|
[2]
|
Biophysical Journal, 84 (2003), 3414-3424.
|
[3]
|
Bulletin of Mathematical Biology, 68 (2006), 1011-1031.
|
[4]
|
Cell Proliferation, 3 (1970), 321-334.
|
[5]
|
1932.
|
[6]
|
Microbes and Infection, 4 (2002), 529-530.
|
[7]
|
Annual Review of Immunology, 18 (2000), 83-111.
|
[8]
|
The Journal of Immunology, 179 (2007), 950-957.
|
[9]
|
Journal of Immunological Methods, 298 (2005), 183-200.
|
[10]
|
Proceedings of the National Academy of Sciences of the United States of America, 101 (2004), 16885-16890.
|
[11]
|
Nat Immunol, 7 (2006), 475-481.
|
[12]
|
The Journal of Immunology, 190 (2013), 3985-3993.
|
[13]
|
Seminars in Immunology, 17 (2005), 231-237.
|
[14]
|
Nature Reviews Immunology, 2 (2002), 547-556.
|
[15]
|
Bulletin of Mathematical Biology, 71 (2009), 1649-1670.
|
[16]
|
Bulletin of Mathematical Biology, 70 (2008), 21-44.
|
[17]
|
Journal of Theoretical Biology, 225 (2003), 275-283.
|
[18]
|
Immunol Cell Biol, 77 (1999), 499-508.
|
[19]
|
Proceedings of the National Academy of Sciences, 70 (1973), 1263-1267.
|
[20]
|
Science, 276 (1997), 2057-2762.
|
[21]
|
Visual Numerics, 1996.
|
[22]
|
The Journal of Immunology, 180 (2008), 1414-1422.
|
-
-
-
-