American Institute of Mathematical Sciences

Advanced
2005, 2(1): 153-168. doi: 10.3934/mbe.2005.2.153

The Effects of Affinity Mediated Clonal Expansion of Premigrant Thymocytes on the Periphery T-Cell Repertoire

Guanyu Wang 1,

1. 

Department of Pathology and laboratory Medicine, The University of Texas Medical School at Houston, Houston, TX 77030

Received  July 2004 Revised  October 2004 Published  November 2004

The immune system maintains a highly diverse T-cell repertoire, which is shaped by active interactions between developing thymocytes and endogenous peptide/MHC molecules through the principle of positive and negative selections. Detours et al. developed a quantitative model addressing key immunologic notions such as selection, alloreactivity, and self-restriction. The model was based on the assumption that the clone size is uniformly distributed in the naive T-cell repertoire. However, recent biological findings have indicated that the naive T-cell repertoire is highly skewed, due to the uneven proliferation of premigrant single-positive thymocytes. In this paper, the model is revised to include these new findings. The effects of the uneven clonal expansion are investigated in detail and their biological significance is discussed. It is found that the uneven clonal expansion can significantly enhance the self-MHC restriction, while avoiding decreasing the alloreactivity. The clonal expansion therefore appears to be an additional selection event, resulting in fine tuning of the repertoire. In this way, T-cells reaching the periphery pool can fulfill maximum competence: both high self-restriction and high alloreactivity.
Keywords: mathematical modeling, probability theory, positive/negative selection, clonal expansion, T-cell development..
Mathematics Subject Classification: 92B1.
Citation: Guanyu Wang. The Effects of Affinity Mediated Clonal Expansion of Premigrant Thymocytes on the Periphery T-Cell Repertoire. Mathematical Biosciences & Engineering, 2005, 2 (1) : 153-168. doi: 10.3934/mbe.2005.2.153
[1]

D. Criaco, M. Dolfin, L. Restuccia. Approximate smooth solutions of a mathematical model for the activation and clonal expansion of T cells. Mathematical Biosciences & Engineering, 2013, 10 (1) : 59-73. doi: 10.3934/mbe.2013.10.59
[2]

Lisette dePillis, Trevor Caldwell, Elizabeth Sarapata, Heather Williams. Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment. Discrete & Continuous Dynamical Systems - B, 2013, 18 (4) : 915-943. doi: 10.3934/dcdsb.2013.18.915
[3]

Hongjing Shi, Wanbiao Ma. An improved model of t cell development in the thymus and its stability analysis. Mathematical Biosciences & Engineering, 2006, 3 (1) : 237-248. doi: 10.3934/mbe.2006.3.237
[4]

Yueping Dong, Rinko Miyazaki, Yasuhiro Takeuchi. Mathematical modeling on helper T cells in a tumor immune system. Discrete & Continuous Dynamical Systems - B, 2014, 19 (1) : 55-72. doi: 10.3934/dcdsb.2014.19.55
[5]

Amy H. Lin Erickson, Alison Wise, Stephen Fleming, Margaret Baird, Zabeen Lateef, Annette Molinaro, Miranda Teboh-Ewungkem, Lisette dePillis. A preliminary mathematical model of skin dendritic cell trafficking and induction of T cell immunity. Discrete & Continuous Dynamical Systems - B, 2009, 12 (2) : 323-336. doi: 10.3934/dcdsb.2009.12.323
[6]

Reihaneh Mostolizadeh, Zahra Afsharnezhad, Anna Marciniak-Czochra. Mathematical model of Chimeric Anti-gene Receptor (CAR) T cell therapy with presence of cytokine. Numerical Algebra, Control & Optimization, 2018, 8 (1) : 63-80. doi: 10.3934/naco.2018004
[7]

Houssein Ayoub, Bedreddine Ainseba, Michel Langlais, Rodolphe Thiébaut. Parameters identification for a model of T cell homeostasis. Mathematical Biosciences & Engineering, 2015, 12 (5) : 917-936. doi: 10.3934/mbe.2015.12.917
[8]

Cliburn Chan, Andrew J.T. George, Jaroslav Stark. T cell sensitivity and specificity - kinetic proofreading revisited. Discrete & Continuous Dynamical Systems - B, 2003, 3 (3) : 343-360. doi: 10.3934/dcdsb.2003.3.343
[9]

Yinghui Dong, Guojing Wang. Ruin probability for renewal risk model with negative risk sums. Journal of Industrial & Management Optimization, 2006, 2 (2) : 229-236. doi: 10.3934/jimo.2006.2.229
[10]

Oleg U. Kirnasovsky, Yuri Kogan, Zvia Agur. Resilience in stem cell renewal: development of the Agur--Daniel--Ginosar model. Discrete & Continuous Dynamical Systems - B, 2008, 10 (1) : 129-148. doi: 10.3934/dcdsb.2008.10.129
[11]

Bing Liu, Ming Zhou. Robust portfolio selection for individuals: Minimizing the probability of lifetime ruin. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020005
[12]

Avner Friedman, Wenrui Hao. Mathematical modeling of liver fibrosis. Mathematical Biosciences & Engineering, 2017, 14 (1) : 143-164. doi: 10.3934/mbe.2017010
[13]

Guanyu Wang, Gerhard R. F. Krueger. A General Mathematical Method for Investigating the Thymic Microenvironment, Thymocyte Development, and Immunopathogenesis. Mathematical Biosciences & Engineering, 2004, 1 (2) : 289-305. doi: 10.3934/mbe.2004.1.289
[14]

Fadoua El Moustaid, Amina Eladdadi, Lafras Uys. Modeling bacterial attachment to surfaces as an early stage of biofilm development. Mathematical Biosciences & Engineering, 2013, 10 (3) : 821-842. doi: 10.3934/mbe.2013.10.821
[15]

Liancheng Wang, Sean Ellermeyer. HIV infection and CD4+ T cell dynamics. Discrete & Continuous Dynamical Systems - B, 2006, 6 (6) : 1417-1430. doi: 10.3934/dcdsb.2006.6.1417
[16]

Yu-Hsien Chang, Guo-Chin Jau. The behavior of the solution for a mathematical model for analysis of the cell cycle. Communications on Pure & Applied Analysis, 2006, 5 (4) : 779-792. doi: 10.3934/cpaa.2006.5.779
[17]

Mostafa Adimy, Oscar Angulo, Catherine Marquet, Leila Sebaa. A mathematical model of multistage hematopoietic cell lineages. Discrete & Continuous Dynamical Systems - B, 2014, 19 (1) : 1-26. doi: 10.3934/dcdsb.2014.19.1
[18]

Gang Bao. Mathematical modeling of nonlinear diffracvtive optics. Conference Publications, 1998, 1998 (Special) : 89-99. doi: 10.3934/proc.1998.1998.89
[19]

Michael Leguèbe. Cell scale modeling of electropermeabilization by periodic pulses. Mathematical Biosciences & Engineering, 2015, 12 (3) : 537-554. doi: 10.3934/mbe.2015.12.537
[20]

A. Chauviere, T. Hillen, L. Preziosi. Modeling cell movement in anisotropic and heterogeneous network tissues. Networks & Heterogeneous Media, 2007, 2 (2) : 333-357. doi: 10.3934/nhm.2007.2.333

2018 Impact Factor: 1.313

Tools

Metrics

  • PDF downloads (12)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences