CFP last date
20 May 2024
Reseach Article

Optimal Therapeutic Control Modeling for Immune System Response

by Pramila Bajpai, Ashish Chaturvedi, A. P. Dwivedi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 21 - Number 4
Year of Publication: 2011
Authors: Pramila Bajpai, Ashish Chaturvedi, A. P. Dwivedi
10.5120/2498-3376

Pramila Bajpai, Ashish Chaturvedi, A. P. Dwivedi . Optimal Therapeutic Control Modeling for Immune System Response. International Journal of Computer Applications. 21, 4 ( May 2011), 27-30. DOI=10.5120/2498-3376

@article{ 10.5120/2498-3376,
author = { Pramila Bajpai, Ashish Chaturvedi, A. P. Dwivedi },
title = { Optimal Therapeutic Control Modeling for Immune System Response },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 21 },
number = { 4 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 27-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume21/number4/2498-3376/ },
doi = { 10.5120/2498-3376 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:07:39.190718+05:30
%A Pramila Bajpai
%A Ashish Chaturvedi
%A A. P. Dwivedi
%T Optimal Therapeutic Control Modeling for Immune System Response
%J International Journal of Computer Applications
%@ 0975-8887
%V 21
%N 4
%P 27-30
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Paper demonstrates the stochastic optimal control model to enhance immune system response. Immune system response can be amplified by agents that kill the pathogen, which stimulates the production of antibodies and implies the enhancement in the health of the organ. Imperfect measurements of the dynamic state degrade the precision of feedback adjustments to therapy; however, optimal state estimation allows the feedback strategy to be implemented with incomplete measurements and minimizes the expected effects of measurement error. The stochastic approach with genetic computing is evaluated to minimize the mutiobjective treatment cost function.

References
  1. R.F. Stengel, R. Ghigliazza, N. Kulkarni, O. Laplace, Optimal control of innate immune response, Optimal Contr. Appl. Methods 23 (2002) 91.
  2. R.F. Stengel, R. Ghigliazza, N. Kulkarni, Optimal enhancement of immune response, Bioinformatics 18 (2002) 1227.
  3. C.A. Janeway, P. Travers, M. Walport, M. Shlomchik, Immunobiology, Garland, London, 2001.
  4. P.M. Lydyard, A. Whelan, M.W. Fanger, Instant Notes in Immunology, Springer, New York, 2000.
  5. M. Thain, M. Hickman, The Penguin Dictionary of Biology, Penguin Books, London, 2000.
  6. M.A. Nowak, R.M. May, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University, Oxford, 2000.
  7. A.S. Perelson, P.W. Nelson, Mathematical analysis of HIV-1 dynamics in vivo, SIAM Rev. 41 (1999) 3.
  8. M.A. Stafford, Y. Cao, D.D. Ho, L. Corey, A.S. Perelson, Modeling plasma virus concentration and CD4+ T cell kinetics during primary HIV infection, J. Theor. Biol. 203 (2000) 285.
  9. N. Wiener, Cybernetics: or Control and Communication in the Animal and the Machine, Technology, Cambridge, 1948.
  10. R.E. Bellman, Mathematical Methods in Medicine, World Scientific Press, Singapore, 1983.
  11. G.W. Swan, Role of optimal control theory in cancer therapy, Math. Biosci. 101 (1990) 237.
  12. A.S. Perelson, Applications of optimal control theory to immunology, in: R.R. Mohler, A. Ruberti (Eds.), Recent Developments in Variable Structure Systems Economics and Biology, Springer, Berlin, 1978, p. 272.
  13. L.M. Wein, S.A. Zenios, M.A. Nowak, Dynamic multidrug therapies for HIV: a control theoretic approach, J. Theor. Biol. 185 (1997) 15.
  14. D. Wodarz, K.M. Page, R.A. Arnaout, A.R. Thomsen, J.D. Lifson, M.A. Nowak, A new theory of cytotoxic Tlymphocyte memory: implications for HIV treatment, Philos. Trans. Roy. Soc. B 355 (2000) 329.
  15. J.M. van Rossum, O. Steyger, T. van Uem, G.J. Binkhorst, R.A.A. Maes, Pharmacokinetics by using mathematical systems dynamics, in: J. Eisenfeld, M. Witten (Eds.), Modelling of Biomedical Systems, Elsevier Science Publishers, 1986, p. 121.
  16. A. Gentilini, M. Morari, C. Bieniok, R. Wymann, T. Schnider, Closed-loop control of analgesia in humans, in: Proc. IEEE Conf. Decision and Control, Orlando, 2001, p. 861.
  17. J.H. Holland. Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press; 1975.
  18. D.F. Jones DF, S.K.Mirrazavi, M. Tamiz. Multiobjective meta-heuristics: an overview of the current state-of-the-art. Eur J Oper Res 2002;137(1):1–9.
  19. J.D. Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the international conference on genetic algorithm and their applications, 1985.
  20. C.M. Fonseca, P.J. Fleming. Multiobjective genetic algorithms. In: IEEE colloquium on ‘Genetic Algorithms for Control Systems Engineering’ (Digest No. 1993/130), 28 May 1993. London, UK: IEE; 1993.
  21. P. Hajela, C. lin. Genetic search strategies in multicriterion optimal design. Struct Optimization 1992;4(2):99–107.
  22. T. Murata, H. Ishibuchi. MOGA: multi-objective genetic algorithms. In: Proceedings of the 1995 IEEE international conference on evolutionary computation, 29 November–1 December, 1995. Perth, Australia: IEEE; 1995.
  23. H. Lu, G.G. Yen. Rank-density-based multiobjective genetic algorithm and benchmark test function study. IEEE Trans Evol Comput 2003;7(4):325–43.
  24. G.G. Yen, H. Lu. Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation. IEEE Trans Evol Comput 2003;7(3):253–74.
  25. T. Murata, H. Ishibuchi, H. Tanaka. Multi-objective genetic algorithm and its applications to flowshop scheduling. Comput Ind Eng 1996;30(4):957–68.
  26. E. Zitzler, K. Deb, L. Thiele. Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 2000;8(2):173–95.
  27. C.A. Coello, 2005 http://www.lania.mx/~ccoello/EMOO/EMOObib.html,.
Index Terms

Computer Science
Information Sciences

Keywords

Immune system response stochastic optimal control Multi-objective cost function