A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes

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2015, 12(3): 625-642. doi: 10.3934/mbe.2015.12.625

A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes

Chang Gong ^1, , Jennifer J. Linderman ^2, and Denise Kirschner ^3,

1.	6775 Medical Science Building II, Ann Arbor, MI 48109-5620, United States
2.	B28-G045W NCRC, Ann Arbor, MI 48109-5620, United States
3.	6730 Medical Science Building II, Ann Arbor, MI 48109-5620, United States

Received January 2015 Revised January 2015 Published February 2015

Abstract
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Granulomas play a centric role in tuberculosis (TB) infection progression. Multiple granulomas usually develop within a single host. These granulomas are not synchronized in size or bacteria load, and will follow different trajectories over time. How the fate of individual granulomas influence overall infection outcome at host scale is not understood, although computational models have been developed to predict single granuloma behavior. Here we present a within-host population model that tracks granulomas in two key organs during Mycobacteria tuberculosis (Mtb) infection: lung and lymph nodes (LN). We capture various time courses of TB progression, including latency and reactivation. The model predicts that there is no steady state; rather it is a continuous process of progressing to active disease over differing time periods. This is consistent with recently posed ideas suggesting that latent TB exists as a spectrum of states and not a single state. The model also predicts a dual role for granuloma development in LNs during Mtb infection: in early phases of infection granulomas suppress infection by providing additional antigens to the site of immune priming; however, this induces a more rapid reactivation at later stages by disrupting immune responses. We identify mechanisms that strongly correlate with better host-level outcomes, including elimination of uncontained lung granulomas by inducing low levels of lung tissue damage and inhibition of bacteria dissemination within the lung.

Keywords: within-host model., TB, granuloma, ODE model, infectious disease.

Mathematics Subject Classification: Primary: 97M60; Secondary: 92B0.

Citation: Chang Gong, Jennifer J. Linderman, Denise Kirschner. A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes. Mathematical Biosciences & Engineering, 2015, 12 (3) : 625-642. doi: 10.3934/mbe.2015.12.625

References:

[1]	I. Y. Adamson, Drug-induced pulmonary fibrosis, Environmental health perspectives, 55 (1984), 25-36.
[2]	C. E. Barry, H. I. Boshoff, V. Dartois, T. Dick, S. Ehrt, J. Flynn, D. Schnappinger, R. J. Wilkinson and D. Young, The spectrum of latent tuberculosis: Rethinking the biology and intervention strategies, Nature reviews. Microbiology, 7 (2009), 845-855. doi: 10.1038/nrmicro2236.
[3]	S. M. Blower, A. R. McLean, T. C. Porco, P. M. Small, P. C. Hopewell, M. A. Sanchez and A. R. Moss, The intrinsic transmission dynamics of tuberculosis epidemics, Nature Medicine, 1 (1995), 815-821. doi: 10.1038/nm0895-815.
[4]	P.-J. Cardona, New insights on the nature of latent tuberculosis infection and its treatment, Inflammation & allergy drug targets, 6 (2007), 27-39. doi: 10.2174/187152807780077282.
[5]	C. Castillo-Chávez and J. Aparicio, Mathematical modelling of tuberculosis epidemics, Mathematical Biosciences and Engineering, 6 (2009), 209-237. doi: 10.3934/mbe.2009.6.209.
[6]	C. Castillo-Chavez and Z. Feng, To treat or not to treat: The case of tuberculosis, Journal of mathematical biology, 35 (1997), 629-656. doi: 10.1007/s002850050069.
[7]	A. A. Chackerian, J. M. Alt, T. V. Perera, C. C. Dascher and S. M. Behar, Dissemination of Mycobacterium tuberculosis Is Influenced by Host Factors and Precedes the Initiation of T-Cell Immunity, Infection and Immunity, 70 (2002), 4501-4509. doi: 10.1128/IAI.70.8.4501-4509.2002.
[8]	N. A. Cilfone, C. R. Perry, D. E. Kirschner and J. J. Linderman, Multi-scale modeling predicts a balance of tumor necrosis factor-$\alpha$ and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection, PloS one, 8 (2013), e68680. doi: 10.1371/journal.pone.0068680.
[9]	M. T. Coleman, R. Y. Chen, M. Lee, P. L. Lin, L. E. Dodd, P. Maiello, L. E. Via, Y. Kim, G. Marriner, V. Dartois, C. Scanga, C. Janssen, J. Wang, E. Klein, S. N. Cho, C. E. Barry 3rd and J. L. Flynn, PET/CT imaging reveals a therapeutic response to oxazolidinones in macaques and humans with tuberculosis, Sci Transl Med, 6 (2014), p265ra167. doi: 10.1126/scitranslmed.3009500.
[10]	J. A. Cooper, D. A. White and R. A. Matthay, Drug-induced pulmonary disease. Part 1: Cytotoxic drugs, The American review of respiratory disease, 133 (1986), 321-340.
[11]	E. L. Corbett, C. J. Watt, N. Walker, D. Maher, B. G. Williams, M. C. Raviglione and C. Dye, The growing burden of tuberculosis: Global trends and interactions with the HIV epidemic, Archives of internal medicine, 163 (2003), 1009-1021. doi: 10.1001/archinte.163.9.1009.
[12]	M. H. Daba, K. E. El-Tahir, M. N. Al-Arifi and O. A. Gubara, Drug-induced pulmonary fibrosis, 2004.
[13]	O. Diekmann, J. A. P. Heesterbeek and M. G. Roberts, The construction of next-generation matrices for compartmental epidemic models, Journal of the Royal Society, Interface / the Royal Society, 7 (2010), 873-885. doi: 10.1098/rsif.2009.0386.
[14]	C. Dye, G. P. Garnett, K. Sleeman and B. G. Williams, Prospects for worldwide tuberculosis control under the WHO DOTS strategy, The Lancet, 352 (1998), 1886-1891. doi: 10.1016/S0140-6736(98)03199-7.
[15]	M. Fallahi-Sichani, J. L. Flynn, J. J. Linderman and D. E. Kirschner, Differential risk of tuberculosis reactivation among anti-TNF therapies is due to drug binding kinetics and permeability, Journal of immunology (Baltimore, Md. : 1950), 188 (2012), 3169-3178. doi: 10.4049/jimmunol.1103298.
[16]	M. Fallahi-Sichani, D. E. Kirschner and J. J. Linderman, NF-$\kappa$B Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis, Frontiers in physiology, 2012. doi: 10.3389/fphys.2012.00170.
[17]	M. Fallahi-Sichani, M. A. Schaller, D. E. Kirschner, S. L. Kunkel and J. J. Linderman, Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma, PLoS computational biology, 6 (2010), e1000778, 19pp. doi: 10.1371/journal.pcbi.1000778.
[18]	Z. Feng, C. Castillo-Chavez and A. F. Capurro, A model for tuberculosis with exogenous reinfection, Theoretical population biology, 57 (2000), 235-247. doi: 10.1006/tpbi.2000.1451.
[19]	J. L. Flynn and J. Chan, Immunology of tuberculosis, Annual review of immunology, 19 (2001), 93-129.
[20]	C. Gong, J. T. Mattila, M. Miller, J. L. Flynn, J. J. Linderman and D. Kirschner, Predicting lymph node output efficiency using systems biology, Journal of theoretical biology, 335 (2013), 169-184. doi: 10.1016/j.jtbi.2013.06.016.
[21]	G. Guzzetta, M. Ajelli, Z. Yang, S. Merler, C. Furlanello and D. Kirschner, Modeling socio-demography to capture tuberculosis transmission dynamics in a low burden setting, Journal of theoretical biology, 289 (2011), 197-205. doi: 10.1016/j.jtbi.2011.08.032.
[22]	D. Kirschner, Dynamics of co-infection with M. Tuberculosis and HIV-1, Theoretical population biology, 55 (1999), 94-109.
[23]	D. E. Kirschner, S. T. Chang, T. W. Riggs, N. Perry and J. J. Linderman, Toward a multiscale model of antigen presentation in immunity, Immunological reviews, 216 (2007), 93-118.
[24]	P. L. Lin, T. Coleman, J. P. J. Carney, B. J. Lopresti, J. Tomko, D. Fillmore, V. Dartois, C. Scanga, L. J. Frye, Ch. Janssen, E. Klein, C. E. Barry and Joanne L Flynn, Radiologic responses in cynomolgous macaques for assessing tuberculosis chemotherapy regimens, Antimicrobial agents and chemotherapy, 57 (2013), 4237-4244. doi: 10.1128/AAC.00277-13.
[25]	P. L. Lin, C. B. Ford, M. T. Coleman, A. J. Myers, R. Gawande, T. Ioerger, J. Sacchettini, S. M. Fortune and J. L. Flynn, Sterilization of granulomas is common in active and latent tuberculosis despite within-host variability in bacterial killing, Nature medicine, 20 (2014), 75-79. doi: 10.1038/nm.3412.
[26]	P. L. Lin, M. Rodgers, L. Smith, M. Bigbee, A. Myers, C. Bigbee, I. Chiosea, S. V. Capuano, C. Fuhrman, E. Klein and J. L. Flynn, Quantitative comparison of active and latent tuberculosis in the cynomolgus macaque model, Infection and immunity, 77 (2009), 4631-4642. doi: 10.1128/IAI.00592-09.
[27]	J. J. Linderman, T. Riggs, M. Pande, M. Miller, S. Marino and D. E. Kirschner, Characterizing the dynamics of CD4+ T cell priming within a lymph node, Journal of immunology (Baltimore, Md. : 1950), 184 (2010), 2873-2885. doi: 10.4049/jimmunol.0903117.
[28]	G. Magombedze, W. Garira and E. Mwenje, Modelling the human immune response mechanisms to mycobacterium tuberculosis infection in the lungs, Mathematical biosciences and engineering : MBE, 3 (2006), 661-682. doi: 10.3934/mbe.2006.3.661.
[29]	G. Magombedze and N. Mulder, A mathematical representation of the development of Mycobacterium tuberculosis active, latent and dormant stages, Journal of theoretical biology, 292 (2012), 44-59. doi: 10.1016/j.jtbi.2011.09.025.
[30]	S. Marino, M. El-Kebir and D. Kirschner, A hybrid multi-compartment model of granuloma formation and T cell priming in tuberculosis, Journal of theoretical biology, 280 (2011), 50-62. doi: 10.1016/j.jtbi.2011.03.022.
[31]	S. Marino, I. B. Hogue, C. J. Ray and D. E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, Journal of theoretical biology, 254 (2008), 178-196. doi: 10.1016/j.jtbi.2008.04.011.
[32]	S. Marino and D. E. Kirschner, The human immune response to Mycobacterium tuberculosis in lung and lymph node, Journal of theoretical biology, 227 (2004), 463-486. doi: 10.1016/j.jtbi.2003.11.023.
[33]	S. Marino, J. J. Linderman and D. E. Kirschner, A multifaceted approach to modeling the immune response in tuberculosis, Wiley interdisciplinary reviews. Systems biology and medicine, 3 (2011), 479-489. doi: 10.1002/wsbm.131.
[34]	F. A. Milner, M. Iannelli and Z. Feng, A Two-Strain Tuberculosis Model with Age of Infection, SIAM Journal on Applied Mathematics, 62 (2002), 1634-1656. doi: 10.1137/S003613990038205X.
[35]	B. M. Murphy, B. H. Singer, S. Anderson and D. Kirschner, Comparing epidemic tuberculosis in demographically distinct heterogeneous populations, Mathematical Biosciences, 180 (2002), 161-185. doi: 10.1016/S0025-5564(02)00133-5.
[36]	B. M. Murphy, B. H. Singer and D. Kirschner, On treatment of tuberculosis in heterogeneous populations, Journal of Theoretical Biology, 223 (2003), 391-404. doi: 10.1016/S0022-5193(03)00038-9.
[37]	A. O'Garra, P. S. Redford, F. W. McNab, C. I. Bloom, R. J. Wilkinson and M. P. R. Berry, The immune response in tuberculosis, Annual review of immunology, 31 (2013), 475-527. doi: 10.1146/annurev-immunol-032712-095939.
[38]	W. H. Organization, Global Tuberculosis Report 2013, 2013.
[39]	R. Pabst, J. Westermann and H. J. Rothkotter, Immunoarchitecture of regenerated splenic and lymph node transplants, Int Rev Cytol, 128 (1991), 215-260. doi: 10.1016/S0074-7696(08)60500-8.
[40]	T. H. Petersen, E. A. Calle, L. Zhao, E. J. Lee, L. Gui, M. B. Raredon, K. Gavrilov, T. Yi, Z. W. Zhuang, C. Breuer, E. Herzog and L. E. Niklason, Tissue-engineered lungs for in vivo implantation, Science, 329 (2010), 538-541. doi: 10.1126/science.1189345.
[41]	L. Ramakrishnan, Revisiting the role of the granuloma in tuberculosis, Nature reviews. Immunology, 12 (2012), 352-366. doi: 10.1038/nri3211.
[42]	J. Rengarajan, B. R. Bloom and E. J. Rubin, Genome-wide requirements for Mycobacterium tuberculosis adaptation and survival in macrophages, Proceedings of the National Academy of Sciences of the United States of America, 102 (2005), 8327-8332. doi: 10.1073/pnas.0503272102.
[43]	J. L. Segovia-Juarez, S. Ganguli and D. Kirschner, Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model, Journal of theoretical biology, 231 (2004), 357-376. doi: 10.1016/j.jtbi.2004.06.031.
[44]	B. H. Singer and D. E. Kirschner, Influence of backward bifurcation on interpretation of r(0) in a model of epidemic tuberculosis with reinfection, Mathematical biosciences and engineering: MBE, 1 (2004), 81-93. doi: 10.3934/mbe.2004.1.81.
[45]	L. E. Via, D. M. Weiner, D. Schimel, P. L. Lin, E. Dayao, S. L. Tankersley, Y. Cai, M. T. Coleman, J. Tomko, P. Paripati, M. Orandle, R. J. Kastenmayer, M. Tartakovsky, A. Rosenthal, D. Portevin, S. Y. Eum, S. Lahouar, S. Gagneux, D. B. Young, J. L. Flynn and C. E. Barry, Differential virulence and disease progression following Mycobacterium tuberculosis complex infection of the common marmoset (Callithrix jacchus), Infection and immunity, 81 (2013), 2909-2919. doi: 10.1128/IAI.00632-13.
[46]	J. E. Wigginton and D. Kirschner, A Model to Predict Cell-Mediated Immune Regulatory Mechanisms During Human Infection with Mycobacterium tuberculosis, The Journal of Immunology, 166 (2001), 1951-1967. doi: 10.4049/jimmunol.166.3.1951.
[47]	P. Ye and D. E. Kirschner, Reevaluation of T Cell Receptor Excision Circles as a Measure of Human Recent Thymic Emigrants, The Journal of Immunology, 168 (2002), 4968-4979. doi: 10.4049/jimmunol.168.10.4968.
[48]	D. Young, J. Stark and D. Kirschner, Systems biology of persistent infection: Tuberculosis as a case study, Nature reviews. Microbiology, 6 (2008), 520-528. doi: 10.1038/nrmicro1919.

show all references

References:

[1]	I. Y. Adamson, Drug-induced pulmonary fibrosis, Environmental health perspectives, 55 (1984), 25-36.
[2]	C. E. Barry, H. I. Boshoff, V. Dartois, T. Dick, S. Ehrt, J. Flynn, D. Schnappinger, R. J. Wilkinson and D. Young, The spectrum of latent tuberculosis: Rethinking the biology and intervention strategies, Nature reviews. Microbiology, 7 (2009), 845-855. doi: 10.1038/nrmicro2236.
[3]	S. M. Blower, A. R. McLean, T. C. Porco, P. M. Small, P. C. Hopewell, M. A. Sanchez and A. R. Moss, The intrinsic transmission dynamics of tuberculosis epidemics, Nature Medicine, 1 (1995), 815-821. doi: 10.1038/nm0895-815.
[4]	P.-J. Cardona, New insights on the nature of latent tuberculosis infection and its treatment, Inflammation & allergy drug targets, 6 (2007), 27-39. doi: 10.2174/187152807780077282.
[5]	C. Castillo-Chávez and J. Aparicio, Mathematical modelling of tuberculosis epidemics, Mathematical Biosciences and Engineering, 6 (2009), 209-237. doi: 10.3934/mbe.2009.6.209.
[6]	C. Castillo-Chavez and Z. Feng, To treat or not to treat: The case of tuberculosis, Journal of mathematical biology, 35 (1997), 629-656. doi: 10.1007/s002850050069.
[7]	A. A. Chackerian, J. M. Alt, T. V. Perera, C. C. Dascher and S. M. Behar, Dissemination of Mycobacterium tuberculosis Is Influenced by Host Factors and Precedes the Initiation of T-Cell Immunity, Infection and Immunity, 70 (2002), 4501-4509. doi: 10.1128/IAI.70.8.4501-4509.2002.
[8]	N. A. Cilfone, C. R. Perry, D. E. Kirschner and J. J. Linderman, Multi-scale modeling predicts a balance of tumor necrosis factor-$\alpha$ and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection, PloS one, 8 (2013), e68680. doi: 10.1371/journal.pone.0068680.
[9]	M. T. Coleman, R. Y. Chen, M. Lee, P. L. Lin, L. E. Dodd, P. Maiello, L. E. Via, Y. Kim, G. Marriner, V. Dartois, C. Scanga, C. Janssen, J. Wang, E. Klein, S. N. Cho, C. E. Barry 3rd and J. L. Flynn, PET/CT imaging reveals a therapeutic response to oxazolidinones in macaques and humans with tuberculosis, Sci Transl Med, 6 (2014), p265ra167. doi: 10.1126/scitranslmed.3009500.
[10]	J. A. Cooper, D. A. White and R. A. Matthay, Drug-induced pulmonary disease. Part 1: Cytotoxic drugs, The American review of respiratory disease, 133 (1986), 321-340.
[11]	E. L. Corbett, C. J. Watt, N. Walker, D. Maher, B. G. Williams, M. C. Raviglione and C. Dye, The growing burden of tuberculosis: Global trends and interactions with the HIV epidemic, Archives of internal medicine, 163 (2003), 1009-1021. doi: 10.1001/archinte.163.9.1009.
[12]	M. H. Daba, K. E. El-Tahir, M. N. Al-Arifi and O. A. Gubara, Drug-induced pulmonary fibrosis, 2004.
[13]	O. Diekmann, J. A. P. Heesterbeek and M. G. Roberts, The construction of next-generation matrices for compartmental epidemic models, Journal of the Royal Society, Interface / the Royal Society, 7 (2010), 873-885. doi: 10.1098/rsif.2009.0386.
[14]	C. Dye, G. P. Garnett, K. Sleeman and B. G. Williams, Prospects for worldwide tuberculosis control under the WHO DOTS strategy, The Lancet, 352 (1998), 1886-1891. doi: 10.1016/S0140-6736(98)03199-7.
[15]	M. Fallahi-Sichani, J. L. Flynn, J. J. Linderman and D. E. Kirschner, Differential risk of tuberculosis reactivation among anti-TNF therapies is due to drug binding kinetics and permeability, Journal of immunology (Baltimore, Md. : 1950), 188 (2012), 3169-3178. doi: 10.4049/jimmunol.1103298.
[16]	M. Fallahi-Sichani, D. E. Kirschner and J. J. Linderman, NF-$\kappa$B Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis, Frontiers in physiology, 2012. doi: 10.3389/fphys.2012.00170.
[17]	M. Fallahi-Sichani, M. A. Schaller, D. E. Kirschner, S. L. Kunkel and J. J. Linderman, Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma, PLoS computational biology, 6 (2010), e1000778, 19pp. doi: 10.1371/journal.pcbi.1000778.
[18]	Z. Feng, C. Castillo-Chavez and A. F. Capurro, A model for tuberculosis with exogenous reinfection, Theoretical population biology, 57 (2000), 235-247. doi: 10.1006/tpbi.2000.1451.
[19]	J. L. Flynn and J. Chan, Immunology of tuberculosis, Annual review of immunology, 19 (2001), 93-129.
[20]	C. Gong, J. T. Mattila, M. Miller, J. L. Flynn, J. J. Linderman and D. Kirschner, Predicting lymph node output efficiency using systems biology, Journal of theoretical biology, 335 (2013), 169-184. doi: 10.1016/j.jtbi.2013.06.016.
[21]	G. Guzzetta, M. Ajelli, Z. Yang, S. Merler, C. Furlanello and D. Kirschner, Modeling socio-demography to capture tuberculosis transmission dynamics in a low burden setting, Journal of theoretical biology, 289 (2011), 197-205. doi: 10.1016/j.jtbi.2011.08.032.
[22]	D. Kirschner, Dynamics of co-infection with M. Tuberculosis and HIV-1, Theoretical population biology, 55 (1999), 94-109.
[23]	D. E. Kirschner, S. T. Chang, T. W. Riggs, N. Perry and J. J. Linderman, Toward a multiscale model of antigen presentation in immunity, Immunological reviews, 216 (2007), 93-118.
[24]	P. L. Lin, T. Coleman, J. P. J. Carney, B. J. Lopresti, J. Tomko, D. Fillmore, V. Dartois, C. Scanga, L. J. Frye, Ch. Janssen, E. Klein, C. E. Barry and Joanne L Flynn, Radiologic responses in cynomolgous macaques for assessing tuberculosis chemotherapy regimens, Antimicrobial agents and chemotherapy, 57 (2013), 4237-4244. doi: 10.1128/AAC.00277-13.
[25]	P. L. Lin, C. B. Ford, M. T. Coleman, A. J. Myers, R. Gawande, T. Ioerger, J. Sacchettini, S. M. Fortune and J. L. Flynn, Sterilization of granulomas is common in active and latent tuberculosis despite within-host variability in bacterial killing, Nature medicine, 20 (2014), 75-79. doi: 10.1038/nm.3412.
[26]	P. L. Lin, M. Rodgers, L. Smith, M. Bigbee, A. Myers, C. Bigbee, I. Chiosea, S. V. Capuano, C. Fuhrman, E. Klein and J. L. Flynn, Quantitative comparison of active and latent tuberculosis in the cynomolgus macaque model, Infection and immunity, 77 (2009), 4631-4642. doi: 10.1128/IAI.00592-09.
[27]	J. J. Linderman, T. Riggs, M. Pande, M. Miller, S. Marino and D. E. Kirschner, Characterizing the dynamics of CD4+ T cell priming within a lymph node, Journal of immunology (Baltimore, Md. : 1950), 184 (2010), 2873-2885. doi: 10.4049/jimmunol.0903117.
[28]	G. Magombedze, W. Garira and E. Mwenje, Modelling the human immune response mechanisms to mycobacterium tuberculosis infection in the lungs, Mathematical biosciences and engineering : MBE, 3 (2006), 661-682. doi: 10.3934/mbe.2006.3.661.
[29]	G. Magombedze and N. Mulder, A mathematical representation of the development of Mycobacterium tuberculosis active, latent and dormant stages, Journal of theoretical biology, 292 (2012), 44-59. doi: 10.1016/j.jtbi.2011.09.025.
[30]	S. Marino, M. El-Kebir and D. Kirschner, A hybrid multi-compartment model of granuloma formation and T cell priming in tuberculosis, Journal of theoretical biology, 280 (2011), 50-62. doi: 10.1016/j.jtbi.2011.03.022.
[31]	S. Marino, I. B. Hogue, C. J. Ray and D. E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, Journal of theoretical biology, 254 (2008), 178-196. doi: 10.1016/j.jtbi.2008.04.011.
[32]	S. Marino and D. E. Kirschner, The human immune response to Mycobacterium tuberculosis in lung and lymph node, Journal of theoretical biology, 227 (2004), 463-486. doi: 10.1016/j.jtbi.2003.11.023.
[33]	S. Marino, J. J. Linderman and D. E. Kirschner, A multifaceted approach to modeling the immune response in tuberculosis, Wiley interdisciplinary reviews. Systems biology and medicine, 3 (2011), 479-489. doi: 10.1002/wsbm.131.
[34]	F. A. Milner, M. Iannelli and Z. Feng, A Two-Strain Tuberculosis Model with Age of Infection, SIAM Journal on Applied Mathematics, 62 (2002), 1634-1656. doi: 10.1137/S003613990038205X.
[35]	B. M. Murphy, B. H. Singer, S. Anderson and D. Kirschner, Comparing epidemic tuberculosis in demographically distinct heterogeneous populations, Mathematical Biosciences, 180 (2002), 161-185. doi: 10.1016/S0025-5564(02)00133-5.
[36]	B. M. Murphy, B. H. Singer and D. Kirschner, On treatment of tuberculosis in heterogeneous populations, Journal of Theoretical Biology, 223 (2003), 391-404. doi: 10.1016/S0022-5193(03)00038-9.
[37]	A. O'Garra, P. S. Redford, F. W. McNab, C. I. Bloom, R. J. Wilkinson and M. P. R. Berry, The immune response in tuberculosis, Annual review of immunology, 31 (2013), 475-527. doi: 10.1146/annurev-immunol-032712-095939.
[38]	W. H. Organization, Global Tuberculosis Report 2013, 2013.
[39]	R. Pabst, J. Westermann and H. J. Rothkotter, Immunoarchitecture of regenerated splenic and lymph node transplants, Int Rev Cytol, 128 (1991), 215-260. doi: 10.1016/S0074-7696(08)60500-8.
[40]	T. H. Petersen, E. A. Calle, L. Zhao, E. J. Lee, L. Gui, M. B. Raredon, K. Gavrilov, T. Yi, Z. W. Zhuang, C. Breuer, E. Herzog and L. E. Niklason, Tissue-engineered lungs for in vivo implantation, Science, 329 (2010), 538-541. doi: 10.1126/science.1189345.
[41]	L. Ramakrishnan, Revisiting the role of the granuloma in tuberculosis, Nature reviews. Immunology, 12 (2012), 352-366. doi: 10.1038/nri3211.
[42]	J. Rengarajan, B. R. Bloom and E. J. Rubin, Genome-wide requirements for Mycobacterium tuberculosis adaptation and survival in macrophages, Proceedings of the National Academy of Sciences of the United States of America, 102 (2005), 8327-8332. doi: 10.1073/pnas.0503272102.
[43]	J. L. Segovia-Juarez, S. Ganguli and D. Kirschner, Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model, Journal of theoretical biology, 231 (2004), 357-376. doi: 10.1016/j.jtbi.2004.06.031.
[44]	B. H. Singer and D. E. Kirschner, Influence of backward bifurcation on interpretation of r(0) in a model of epidemic tuberculosis with reinfection, Mathematical biosciences and engineering: MBE, 1 (2004), 81-93. doi: 10.3934/mbe.2004.1.81.
[45]	L. E. Via, D. M. Weiner, D. Schimel, P. L. Lin, E. Dayao, S. L. Tankersley, Y. Cai, M. T. Coleman, J. Tomko, P. Paripati, M. Orandle, R. J. Kastenmayer, M. Tartakovsky, A. Rosenthal, D. Portevin, S. Y. Eum, S. Lahouar, S. Gagneux, D. B. Young, J. L. Flynn and C. E. Barry, Differential virulence and disease progression following Mycobacterium tuberculosis complex infection of the common marmoset (Callithrix jacchus), Infection and immunity, 81 (2013), 2909-2919. doi: 10.1128/IAI.00632-13.
[46]	J. E. Wigginton and D. Kirschner, A Model to Predict Cell-Mediated Immune Regulatory Mechanisms During Human Infection with Mycobacterium tuberculosis, The Journal of Immunology, 166 (2001), 1951-1967. doi: 10.4049/jimmunol.166.3.1951.
[47]	P. Ye and D. E. Kirschner, Reevaluation of T Cell Receptor Excision Circles as a Measure of Human Recent Thymic Emigrants, The Journal of Immunology, 168 (2002), 4968-4979. doi: 10.4049/jimmunol.168.10.4968.
[48]	D. Young, J. Stark and D. Kirschner, Systems biology of persistent infection: Tuberculosis as a case study, Nature reviews. Microbiology, 6 (2008), 520-528. doi: 10.1038/nrmicro1919.

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