Immune Response Enhancement Strategy via Hybrid Control Perspective

  • Hyuk-Jun ChangEmail author
  • Alessandro Astolfi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7699)


We investigate a control method for disease dynamics, such as HIV and malaria, to boost the immune response using a model-based approach. In particular we apply the control method to select the appropriate immune response between Th1 and Th2 responses. The idea of state jump is introduced and discussed based on hybrid control systems. To implement the control idea we propose physically available methods for each biological system. The studies on malaria model and HIV model are supported by experimental data.


Hybrid systems State jump Malaria Bee venom HIV/AIDS Immunotherapy 


  1. 1.
    Adams, B.M., Banks, H.T., Kwon, H., Tran, H.T.: Dynamic multidrug therapies for HIV: optimal and STI control approaches. Math. Biosci. Eng. 1, 223–241 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bergmann, C., van Hemmen, J.L., Segel, L.E.: Th1 or Th2: How an appropriate T helper response can be made. Bull. Math. Biol. 63, 405–430 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bergmann, C., van Hemmen, J.L., Segel, L.E.: How instruction and feedback can select the appropriate T helper response. Bull. Math. Biol. 64, 425–446 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Brandt, M.E., Chen, G.: Feedback control of a biodynamical model of HIV-1. IEEE Trans. Biomed. Eng. 48(7), 754–759 (2001)CrossRefGoogle Scholar
  5. 5.
    Chang, H., Astolfi, A.: Activation of immune response in disease dynamics via controlled drug scheduling. IEEE Trans. Autom. Sci. Eng. 6, 248–255 (2009)CrossRefGoogle Scholar
  6. 6.
    Chang, H., Astolfi, A.: Enhancement of the immune response to chronic myeloid leukaemia via controlled treatment scheduling. In: Proceedings of the of 31st Annual EMBS International Conference, pp. 3889–3892 (2009)Google Scholar
  7. 7.
    Chang, H., Astolfi, A.: Control of HIV infection dynamics: Approximating high-order dynamics by adapting reduced-order model parameters. IEEE Control Syst. Mag. 28, 28–39 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chang, H., Astolfi, A., Shim, H.: A control theoretic approach to venom immunotherapy with state jumps. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 742–745, August 2010Google Scholar
  9. 9.
    Chang, H., Astolfi, A., Shim, H.: A control theoretic approach to malaria immunotherapy with state jumps. Automatica 47(6), 1271–1277 (2011). Special Issue on Systems BiologyMathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Chang, H., Moog, C.H., Astolfi, A., Rivadeneira, P.S.: A control systems analysis of HIV prevention model using impulsive input. Biomed. Sign. Process. Control 13, 123–131 (2014)CrossRefGoogle Scholar
  11. 11.
    Ge, S.S., Tian, Z., Lee, T.H.: Nonlinear control of a dynamic model of HIV-1. IEEE Trans. Biomed. Eng. 52(3), 353–361 (2005)CrossRefGoogle Scholar
  12. 12.
    Gibaldi, M., Perrier, D.: Drugs and the Pharmaceutical Sciences, vol. 1: Pharmacokinetics. Marcel Dekker Inc, New York (1975)Google Scholar
  13. 13.
    Gilead Sciences Ltd., Truvada\(^{\textregistered }\) Full Prescribing Information, 4 Dec 2011. Available at (2011)
  14. 14.
    David, B.K.: Golden. Insect sting allergy and venom immunotherapy: A model and a mystery. J. Allergy Clin. Immunol. 115(3), 439–447 (2005)CrossRefGoogle Scholar
  15. 15.
    Grant, R.M., Lama, J.R., Anderson, P.L., McMahan, V., Liu, A.Y., Vargas, L., Goicochea, P., et al.: Preexposure chemoprophylaxis for HIV prevention in men who have sex with men. New Engl. J. Med. 363(27), 2587–2599 (2010)CrossRefGoogle Scholar
  16. 16.
    Gurarie, D., McKenzie, F.E.: Dynamics of immune response and drug resistance in malaria infection. Malaria J. 5(1), 86 (2006)CrossRefGoogle Scholar
  17. 17.
    Gurarie, D., Zimmerman, P.A., King, C.H.: Dynamic regulation of single-and mixed-species malaria infection: insights to specific and non-specific mechanisms of control. J. Theor. Biol. 240(2), 185–199 (2006)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Jackson, A., Moyle, G., Watson, V., Tjia, J., Ammara, A., Back, D., Mohabeer, M., Gazzard, B., Boffito, M.: Tenofovir, emtricitabine intracellular and plasma, and efavirenz plasma concentration decay following drug intake cessation: implications for HIV treatment and prevention. JAIDS, Publish Ahead of Print (2013)Google Scholar
  19. 19.
    Jilek, B.L., Zarr, M., Sampah, M.E., Rabi, S.A., Bullen, C.K., Lai, J., Shen, L., Siliciano, R.F.: A quantitative basis for antiretroviral therapy for HIV-1 infection. Nature Med. 18, 446–451 (2012)CrossRefGoogle Scholar
  20. 20.
    Kim, K.-W., Shin, Y.-S., Kim, K.-S., Chang, Y.-C., Park, K.-K., Park, J.-B., Choe, J.-Y., Lee, K.-G., Kang, M.-S., Park, Y.-G., Kim, C.-H.: Suppressive effects of bee venom on the immune responses in collagen-induced arthritis in rats. Phytomedicine 15(12), 1099–1107 (2008)CrossRefGoogle Scholar
  21. 21.
    Mason, D.P., McKenzie, F.E.: Blood-stage dynamics and clinical implications of mixed Plasmodium vivax-Plasmodium falciparum infections. Am. J. Trop Med. Hyg. 61(3), 367–374 (1999)CrossRefGoogle Scholar
  22. 22.
    Mason, D.P., McKenzie, F.E., Bossert, W.H.: The blood-stage dynamics of mixed Plasmodium malariae-Plasmodium falciparum infections. J. Theor. Biol. 198(4), 549–566 (1999)CrossRefGoogle Scholar
  23. 23.
    McQueen, P.G., McKenzie, F.E.: Host control of malaria infections: Constraints on immune and erythropoeitic response kinetics. PLoS Comput. Biol. 4, e1000149 (2008)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Mhawej, M., Moog, C.H., Biafore, F., Brunet-François, C.: Control of the HIV infection and drug dosage. Biomed. Sign. Process. Control 5(1), 45–52 (2010)CrossRefGoogle Scholar
  25. 25.
    Nowak, M.A., May, R.M.: Virus Dynamics. Oxford University Press, New York (2000)zbMATHGoogle Scholar
  26. 26.
    Recker, M., Nee, S., Bull, P.C., Kinyanjui, S., Marsh, K., Newbold, C., Gupta, S.: Transient cross-reactive immune responses can orchestrate antigenic variation in malaria. Nature 429(6991), 555–558 (2004)CrossRefGoogle Scholar
  27. 27.
    Richter, J., Metzner, G., Behn, U.: Mathematical modelling of venom immunotherapy. J. Theor. Med. 4, 119–132 (2002)CrossRefzbMATHGoogle Scholar
  28. 28.
    Rivadeneira, P.S., Moog, C.H.: Impulsive control of single-input nonlinear systems with application to HIV dynamics. Appl. Math. Comput. 218(17), 8462–8474 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Roestenberg, M., McCall, M., Hopman, J., et al.: Protection against a malaria challenge by sporozoite inoculation. N. Engl. J. Med. 361(5), 468–477 (2009)CrossRefGoogle Scholar
  30. 30.
    Rowland, M., Tozer, T.: Clinical Pharmacokinetics: Concepts and Applications. Lea & Febiger, Philadelphia (1980)Google Scholar
  31. 31.
    Sampah, M.E.S., Shen, L., Jilek, B.L., Siliciano, R.F.: Dose-response curve slope is a missing dimension in the analysis of HIV-1 drug resistance. Proc. Natl. Acad. Sci. 108(18), 7613–7618 (2011)CrossRefGoogle Scholar
  32. 32.
    Shen, L., Peterson, S., Sedaghat, A.R., McMahon, M.A., Callender, M., Zhang, H., Zhou, Y., Pitt, E., Anderson, K.S., Acosta, E.P., Siliciano, R.F.: Dose-response curve slope sets class-specific limits on inhibitory potential of anti-HIV drugs. Nature Med. 14, 762–766 (2008)CrossRefGoogle Scholar
  33. 33.
    Shim, H., Jo, N.H., Chang, H., Seo, J.H.: A system theoretic study on a treatment of AIDS patient by achieving long-term non-progressor. Automatica 45, 611–622 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    UNAIDS, T.W.H. Organization. AIDS epidemic update: 2010. UNAIDS, Geneva (2010)Google Scholar
  35. 35.
    Wodarz, D.: Helper-dependent vs. helper-independent CTL responses in HIV infection. J. Theor. Biol. 213, 447–459 (2001)CrossRefGoogle Scholar
  36. 36.
    Wodarz, D., Nowak, M.A.: Specific therapy regimes could lead to long-term immunological control of HIV. Proc. Natl. Acad. Sci. 96(25), 14464–14469 (1999)CrossRefGoogle Scholar
  37. 37.
    Wodarz, D., May, R.M., Nowak, M.A.: The role of antigen-independent persistence of memory cytotoxic T lymphocytes. Int. Immunol. 12(4), 467–477 (2000)CrossRefGoogle Scholar
  38. 38.
    World Health Organization Expert Committee on Malaria. 20th Report. WHO Regional Office for Africa (2003)Google Scholar
  39. 39.
    Zurakowski, R., Teel, A.R.: A model predictive control based scheduling method for HIV therapy. J. Theor. Biol. 238, 368–382 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Electrical EngineeringKookmin UniversitySeoulRepublic of Korea
  2. 2.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK
  3. 3.DICIIUniversità di Roma Tor VergataRomaItaly

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