Reaction-Diffusion Modelling of Interferon Distribution in Secondary Lymphoid Organs

G. Bocharov; A. Danilov; Yu. Vassilevski; G.I. Marchuk; V.A. Chereshnev; B. Ludewig

Mathematical Modelling of Natural Phenomena (2011)

  • Volume: 6, Issue: 7, page 13-26
  • ISSN: 0973-5348

Abstract

top
This paper proposes a quantitative model of the reaction-diffusion type to examine the distribution of interferon-α (IFNα) in a lymph node (LN). The numerical treatment of the model is based on using an original unstructured mesh generation software Ani3D and nonlinear finite volume method for diffusion equations. The study results in suggestion that due to the variations in hydraulic conductivity of various zones of the secondary lymphoid organs the spatial stationary distribution of IFNα is essentially heterogeneous across the organs. Highly protected domains such as sinuses, conduits, co-exist with the regions in which where the stationary concentration of IFNα is lower by about 100-fold. This is the first study where the spatial distribution of soluble immune factors in secondary lymphoid organs is modelled for a realistic three-dimensional geometry.

How to cite

top

Bocharov, G., et al. "Reaction-Diffusion Modelling of Interferon Distribution in Secondary Lymphoid Organs." Mathematical Modelling of Natural Phenomena 6.7 (2011): 13-26. <http://eudml.org/doc/222379>.

@article{Bocharov2011,
abstract = {This paper proposes a quantitative model of the reaction-diffusion type to examine the distribution of interferon-α (IFNα) in a lymph node (LN). The numerical treatment of the model is based on using an original unstructured mesh generation software Ani3D and nonlinear finite volume method for diffusion equations. The study results in suggestion that due to the variations in hydraulic conductivity of various zones of the secondary lymphoid organs the spatial stationary distribution of IFNα is essentially heterogeneous across the organs. Highly protected domains such as sinuses, conduits, co-exist with the regions in which where the stationary concentration of IFNα is lower by about 100-fold. This is the first study where the spatial distribution of soluble immune factors in secondary lymphoid organs is modelled for a realistic three-dimensional geometry. },
author = {Bocharov, G., Danilov, A., Vassilevski, Yu., Marchuk, G.I., Chereshnev, V.A., Ludewig, B.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {mathematical model; immune response; reaction-diffusion equation; 3D distribution of interferon-α},
language = {eng},
month = {6},
number = {7},
pages = {13-26},
publisher = {EDP Sciences},
title = {Reaction-Diffusion Modelling of Interferon Distribution in Secondary Lymphoid Organs},
url = {http://eudml.org/doc/222379},
volume = {6},
year = {2011},
}

TY - JOUR
AU - Bocharov, G.
AU - Danilov, A.
AU - Vassilevski, Yu.
AU - Marchuk, G.I.
AU - Chereshnev, V.A.
AU - Ludewig, B.
TI - Reaction-Diffusion Modelling of Interferon Distribution in Secondary Lymphoid Organs
JO - Mathematical Modelling of Natural Phenomena
DA - 2011/6//
PB - EDP Sciences
VL - 6
IS - 7
SP - 13
EP - 26
AB - This paper proposes a quantitative model of the reaction-diffusion type to examine the distribution of interferon-α (IFNα) in a lymph node (LN). The numerical treatment of the model is based on using an original unstructured mesh generation software Ani3D and nonlinear finite volume method for diffusion equations. The study results in suggestion that due to the variations in hydraulic conductivity of various zones of the secondary lymphoid organs the spatial stationary distribution of IFNα is essentially heterogeneous across the organs. Highly protected domains such as sinuses, conduits, co-exist with the regions in which where the stationary concentration of IFNα is lower by about 100-fold. This is the first study where the spatial distribution of soluble immune factors in secondary lymphoid organs is modelled for a realistic three-dimensional geometry.
LA - eng
KW - mathematical model; immune response; reaction-diffusion equation; 3D distribution of interferon-α
UR - http://eudml.org/doc/222379
ER -

References

top
  1. S. Andrew, C.T.H. Baker, G.A. Bocharov. Rival approaches to mathematical modelling in immunology. J. Comput. Appl. Math., 205 (2007), 669–686.  
  2. V. Baldazzi, P. Paci, M. Bernaschi, F. Castiglione. Modeling lymphocyte homing and encounters in lymph nodes. BMC Bioinform., 10 (2009), doi:.  URI10.1186/1471-2105-10-387
  3. C. Beauchemin, N.M. Dixit, A.S. Perelson. Characterizing T cell movement within lymph nodes in the absence of antigen. J. Immunol., 178 (2007), 5505–5512.  
  4. J.B. Beltman, A.F. Maree, J.N. Lynch, M.J. Miller, R.J. de Boer. Lymph node topology dictates T cell migration behavior. J. Exp. Med., 204 (2007), 771–780.  
  5. G.A. Bocharov, G.I. Marchuk. Applied problems of mathematical modelling in immunology. Comput. Math. Math. Phys., 40 (2000), 1905–1920.  
  6. G. Bocharov. Understanding complex regulatory systems: Integrating molecular biology and systems analysis. Transf. Med. Hemoth., 32 (2005), No. 6, 304–321.  
  7. G. Bocharov, R. Zust, L. Cervantes-Barragan, T. Luzyanina, E. Chiglintcev, V.A. Chereshnev, V. Thiel, B. Ludewig. A systems immunology approach to plasmacytoid dendritic cell function in cytopathic virus infections. PLoS Pathogens, 6(7) (2010), e1001017.doi:.  URI10.1371/journal.ppat.1001017, 1–14
  8. A.A. Danilov. Unstructured tetrahedral mesh generation technology. Comput. Math. Math. Phys., 50 (2010), 146–163.  
  9. A.A. Danilov, Yu.V. Vassilevski. A monotone nonlinear finite volume method for diffusion equations on conformal polyhedral meshes. Russ. J. Numer. Anal. Math. Modelling, 24 (2009), 207–227.  
  10. Z. Faroogi, R.R. Mohler. Distribution models of recirculating lymphocytes. IEEE Trans. Biomed. Engrg., 36 (1989), 355–362.  
  11. Z. Grossman, M. Meier-Schellersheim, W.E. Paul, L.J. Picker. Pathogenesis of HIV infection: what the virus spares is as important as what it destroys. Nat. Med., 12 (2006), 289–295.  
  12. T. Junt, E. Scandella, B. Ludewig. Form follows function: lymphoid tissues microarchitecture in antimicrobial immune defense. Nature Rev. Immunol., 8 (2008), 764–775.  
  13. J. Keener, J. Sneyd. Mathematical physiology. Springer-Verlag, New York, 1998.  
  14. T.B. Kepler, C. Chan. Spatiotemporal programming of a simple inflammatory process. Immunol. Reviews, 216 (2007), 153–163.  
  15. F. Klauschen, M. Ishii, H. Qi, M. Bajenoff, J.G. Egen, R.N. Germain, M. Meier-Schellersheim. Quantifying cellular interaction dynamics in 3D fluorescence microscopy data. Nat. Protoc., 4 (2009), 1305–1311.  
  16. T. Lammermann, M. Sixt. The microanatomy of T cell responses. Immunol. Reviews, 221 (2008), 26–43.  
  17. P. Lane, R.-P. Sekaly. HIV and the architecture of immune responses. Semin. Immunol.20 (2008), 157–158.  
  18. J.J. Linderman, T. Riggs, M. Pande, M. Miller, S. Marino, D.E. Kirschner. Characterizing the dynamics of CD4+ T cell priming within a lymph node. J. Immunol., 184 (2010), 2873–2885.  
  19. G.I. Marchuk. Mathematical modelling of immune response in infectious diseases. Kluwer Academic Publishres, Dordrecht, 1997.  
  20. G.I. Marchuk. Methods of Numerical Mathematics. Springer-Verlag, New York, 1982.  
  21. G.I. Marchuk, V. Shutyaev, G. BocharovAdjoint equations and analysis of complex systems: application to virus infection modeling. J. Comput. Appl. Math., 184 (2005), 177–204.  
  22. R.R. Mohler, Z. Faroogi, T. Heilig. Lymphocyte distribution and lymphatic dynamics. In: Vistas in Applied Mathematics: Numerical Analysis, Atmospheric Sciences, Immunology. (Eds. A.V. Balakrishnan, A.A. Dorodnitsyn, and J.-L. Lions) 1986, 317–333.  
  23. J.H. Meyers, J.S. Justement, C.W. Hallahan, E.T. Blair, Y.A. Sun, M.A. O’Shea, G. Roby, S. Kottilil, S. Moir, C.M. Kovacs, T.W. Chun, A.S. Fauci. Impact of HIV on cell survival and antiviral activity of plasmacytoid dendritic cells. PLoS ONE, 2 (2008), No. 5, e458. doi: URI10.1371/journal.pone.0000458
  24. R.R. Mohler, C. Bruni, A. Gandolfi. A systems approach to immunology. Proceedings of the IEEE, 68 (1980), 964–990 
  25. A.S. Perelson, F.W. Wiegel. Scaling aspects of lymphocyte trafficking. J. Theor. Biol., 257 (2009), 9–16.  
  26. E. Scandella, B. Bolinger, E. Lattmann, S. Miller, S. Favre, D.R. Littman, D. Finke, S.A. Luther, T. Junt, B. Ludewig. Restoration of lymphoid organ integrity through the interaction of lymphoid tissue-inducer cells with stroma of the T cell zone. Nature Immunol., 9 (2008), 667–675.  
  27. F. Pfeiffer, V. Kumar, S. Butz, D. Vestweber, B.A. Imhof, J.V. Stein, B. Engelhardt. Distinct molecular composition of blood and lymphatic vascular endothelial cell junctions establishes specific functional barriers within the peripheral lymph node. Eur. J. Immunol., 38 (2008), 2142–2155.  
  28. D.J. Stekel, C.E. Parker, M.A. Nowak. A model of lymphocyte recirculation. Immunol. Today, 18 (1997), No. 5, 216–21.  
  29. D.J. Stekel. The simulation of density-dependent effects in the recirculation of T lymphocytes. Scand. J. Immunol., 47 (1998), 426–430.  
  30. S. Stoll, J. Delon, T.M. Brotz, R.N. Germain. Dynamic imaging of T cell-dendritic cell interactions in lymph nodes. Science, 296 (2002), 1873–1876. 

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.