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Generalized electro-biothermo-fluidic and dynamicalmodeling of cancer growth: state-feedback controlled cesium therapy approach

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DOI: 10.4236/jbise.2011.49073    3,543 Downloads   6,683 Views  
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ABSTRACT

This paper develops a generalized dynamical model to describe the interactive dynamics between normal cells, tumor cells, immune cells, drug therapy, electromagnetic field of the human cells, extracellular heat and fluid transfer, and intercellular fractional mass of Oxygen, cell acidity and Pancreatin enzyme. The overall dynamics stability, controllability and observability have been investigated. Moreover, Cesium therapy is considered as a control input to the 11-dimensional dynamics using state-feedback controlled system and pole placement technique. This approach is found to be effective in driving the desired rate of tumor cell kill and converging the system to healthy equilibrium state. Furthermore, the ranges of the system dynamics parameters which lead to instability and growth of tumor cells have been identified. Finally, simulation results are demonstrated to verify the effectiveness of the applied approach which can be implemented successfully to cancer patients.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Al-Shibli, M. (2011) Generalized electro-biothermo-fluidic and dynamicalmodeling of cancer growth: state-feedback controlled cesium therapy approach. Journal of Biomedical Science and Engineering, 4, 569-582. doi: 10.4236/jbise.2011.49073.

References

[1] Hanahan, D. and Weinberg, R.A. (2000) The hall-marks of cancer. Cell Journal, 100, 57-70. doi:10.1016/S0092-8674(00)81683-9
[2] Raines, J.K. (1981) Electromagnetic field interactions with the human body: Observed effects and theories. Technical Report, NASA, Goddard Space Flight Center, Washington.
[3] De Pillis, L.G. and Radunskaya, A.E. (2003.) The dynamics of an optimally controlled tumor model: A case study. Mathematical and Computer Modeling, 37, 1221-1244. doi:10.1016/S0895-7177(03)00133-X
[4] Itik M., Salamci, M.U. and Banks, S.P. (2010) SDRE optimal control of drug administration in cancer treatment. Turkish Journal of Electrical Engineering and Computer Sciences, 18, 715-729.
[5] Szymanska, Z. (2003) Analysis of immunotherapy models in the context of cancer dynamics. International Journal of Applied Mathematics and Computer Science, 13, 407-418,
[6] Byrne, H.M., Alarcon, T., Owen, R., Webb, S. D. and Maiki, P.K. (2006) Modelling aspects of cancer dynamics: A review. Philosophical Transaction of the Royal Society A, 364, 1563-1578.
[7] Kirschner, D. (2009) On the global dynamics of a model for tumor immunotherapy. Mathematical Biosciences and Engineering, 6, 573-583.
[8] U. Ledzewicz, (2005) Optimal control for a system modelling tumor anti-angiogenesis. ACSE 2005 Conference, Cairo, 19-21 December 2005.
[9] Ghaffari, A. and Nasserifar, N. (2009) Mathematical modeling and lyapunov-based drug administration in cancer chemotherapy. Journal of Electrical & Electronic Engineering, 5, 151-158.
[10] Warburg, O.H. (1969) The prime cause and prevention of cancer. 2nd Edition, Konrad Triltsch, Würzburg.
[11] Brewer A.K. (1984) The high ph therapy for cancer, tests on mice and humans. Pharmacology Biochemistry & Behavior, 21, 1-5.
[12] Aenold J. (2003) Clean out your arteries at home, without a needle, and at a fraction of the cost,” Health Sciences Institute Members Alert, 1-4.

  
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