Tuberculosis (TB) is a global emergency. The World Health Organization reports about 9.2 million new infections each year, with an average of 1.7 million people killed by the disease. The causative agent is Mycobacterium tuberculosis (Mtb), whose main target are the macrophages, important immune system cells. Macrophages and T cell populations are the main responsible for fighting the pathogen. A better understanding of the interaction between Mtb, macrophages and T cells will contribute to the design of strategies to control TB. The purpose of this study is to evaluate the impact of the response of T cells and macrophages in the control of Mtb. To this end, we propose a system of ordinary differential equations to model the interaction among non-infected macrophages, infected macrophages, T cells and Mtb bacilli. Model analysis reveals the existence of two equilibrium states, infection-free equilibrium and the endemically infected equilibrium which can represent a state of latent or active infection, depending on the amount of bacteria.
Citation: Eduardo Ibarguen-Mondragon, Lourdes Esteva, Leslie Chávez-Galán. A mathematical model for cellular immunology of tuberculosis[J]. Mathematical Biosciences and Engineering, 2011, 8(4): 973-986. doi: 10.3934/mbe.2011.8.973
Related Papers:
[1] |
Eduardo Ibargüen-Mondragón, Lourdes Esteva, Edith Mariela Burbano-Rosero .
Mathematical model for the growth of Mycobacterium tuberculosis in the granuloma. Mathematical Biosciences and Engineering, 2018, 15(2): 407-428.
doi: 10.3934/mbe.2018018
|
[2] |
P. van den Driessche, Lin Wang, Xingfu Zou .
Modeling diseases with latency and relapse. Mathematical Biosciences and Engineering, 2007, 4(2): 205-219.
doi: 10.3934/mbe.2007.4.205
|
[3] |
Abba B. Gumel, Baojun Song .
Existence of multiple-stable equilibria for a multi-drug-resistant model of mycobacterium tuberculosis. Mathematical Biosciences and Engineering, 2008, 5(3): 437-455.
doi: 10.3934/mbe.2008.5.437
|
[4] |
Carlos Castillo-Chavez, Baojun Song .
Dynamical Models of Tuberculosis and Their Applications. Mathematical Biosciences and Engineering, 2004, 1(2): 361-404.
doi: 10.3934/mbe.2004.1.361
|
[5] |
Tao-Li Kang, Hai-Feng Huo, Hong Xiang .
Dynamics and optimal control of tuberculosis model with the combined effects of vaccination, treatment and contaminated environments. Mathematical Biosciences and Engineering, 2024, 21(4): 5308-5334.
doi: 10.3934/mbe.2024234
|
[6] |
Sebastian Builes, Jhoana P. Romero-Leiton, Leon A. Valencia .
Deterministic, stochastic and fractional mathematical approaches applied to AMR. Mathematical Biosciences and Engineering, 2025, 22(2): 389-414.
doi: 10.3934/mbe.2025015
|
[7] |
Xu Zhang, Dongdong Chen, Wenmin Yang, JianhongWu .
Identifying candidate diagnostic markers for tuberculosis: A critical role of co-expression and pathway analysis. Mathematical Biosciences and Engineering, 2019, 16(2): 541-552.
doi: 10.3934/mbe.2019026
|
[8] |
Ya-Dong Zhang, Hai-Feng Huo, Hong Xiang .
Dynamics of tuberculosis with fast and slow progression and media coverage. Mathematical Biosciences and Engineering, 2019, 16(3): 1150-1170.
doi: 10.3934/mbe.2019055
|
[9] |
Benjamin H. Singer, Denise E. Kirschner .
Influence of backward bifurcation on interpretation of $R_0$ in a model of epidemic tuberculosis with reinfection. Mathematical Biosciences and Engineering, 2004, 1(1): 81-93.
doi: 10.3934/mbe.2004.1.81
|
[10] |
Juan Pablo Aparicio, Carlos Castillo-Chávez .
Mathematical modelling of tuberculosis epidemics. Mathematical Biosciences and Engineering, 2009, 6(2): 209-237.
doi: 10.3934/mbe.2009.6.209
|
Abstract
Tuberculosis (TB) is a global emergency. The World Health Organization reports about 9.2 million new infections each year, with an average of 1.7 million people killed by the disease. The causative agent is Mycobacterium tuberculosis (Mtb), whose main target are the macrophages, important immune system cells. Macrophages and T cell populations are the main responsible for fighting the pathogen. A better understanding of the interaction between Mtb, macrophages and T cells will contribute to the design of strategies to control TB. The purpose of this study is to evaluate the impact of the response of T cells and macrophages in the control of Mtb. To this end, we propose a system of ordinary differential equations to model the interaction among non-infected macrophages, infected macrophages, T cells and Mtb bacilli. Model analysis reveals the existence of two equilibrium states, infection-free equilibrium and the endemically infected equilibrium which can represent a state of latent or active infection, depending on the amount of bacteria.