Providing a Therapeutic Scheduling for HIV Infected Individuals with Genetic Algorithms Using a Cellular Automata Model of HIV Infection in the Peripheral Blood Stream — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Providing a Therapeutic Scheduling for HIV Infected Individuals with Genetic Algorithms Using a Cellular Automata Model of HIV Infection in the Peripheral Blood Stream
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
,
Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Science, Tehran, Iran
,
Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
1 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Science, Tehran, Iran
3 Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
The aim of this study is to develop two-dimensional cellular automata model of HIV infection that depicts the dynamics involved in the interactions between acquired immune system and HIV infection in the peripheral blood stream. The appropriate biological rules of cellular automata model have been extracted from expert knowledge and the model has been simulated with determined initial conditions. Obtained results have been validated through comparing with the accepted AIDS reference curve. The new rules and states were added to the proposed model to show the effects of applying combined antiretroviral therapy. Our results showed that by applying RTI and PI drugs with maximum drug effectiveness, comparing with cases in which no treatment was applied, the steady state concentrations of healthy (infected) CD 4 + T cells were increased (decreased) 53% (41%). Also, the use of cART with maximum drug effectiveness led to a 69% reduction in the steady state level of viral load. At this time, obtained results have been validated through comparing with available clinical data. Our results showed good agreement with both reference curve and the clinical data. In the second phase of this study, by applying genetic algorithms, a therapeutic schedule has been provided that its use, while maintaining the quality of the treatment, leads to a 47% reduction in both drug dosage and the side effects of antiretroviral drugs.
Weiss, R. (1993) How Does HIV Causes AIDS? Science, 260, 1273-1279. https://doi.org/10.1126/science.8493571
AIDS Epidemic Update, World Health Organization (WHO), 2011.
Craig, I. and Xia, X. (2005) Can HIV/AIDS Be Controlled? Applying Control Engineering Concepts outside Traditional Fields. IEEE Control System Magazine, 25, 80-83. https://doi.org/10.1109/MCS.2005.1388805
Imamichi and Tomozumi (2004) Action of Anti-HIV Drugs and Resistance: Reverse Transcriptase Inhibitors and Protease Inhibitors. Current Pharmaceutical Design, 10, 4039-4053.
Teklay, G., Legesse, B. and Legesse, M. (2013) Adverse Effects and Regimen Switch among Patients on Antiretroviral Treatment in a Resource Limited Setting in Ethiopia. Pharmacovigilance, 1. https://doi.org/10.4172/2329-6887.1000115
Isham, V. (1989) Mathematical Modeling of the Transmission Dynamics of HIV Infection and AIDS. Mathematical and Computer Modeling, 12, 1187. https://doi.org/10.1016/0895-7177(89)90269-0
Wodarz, D. and Nowak, M.A. (2000) Mathematical Models of HIV Pathogenesis and Treatment. BioEssays, 24, 1178-1187. https://doi.org/10.1002/bies.10196
Landi, A., Mazzoldi, A., Andreoni, C., Bianchi, M., Cavallini, A., Laurino, M., Ricotti, L., Iuliani, R., Matteoli, B. and Ceccherini, L. (2008) Modeling and Control of HIV Dynamics. Computer Methods and Program in Biomedicine, 89, 62-68. https://doi.org/10.1016/j.cmpb.2007.08.003
Perrin, D., Ruskin, H.J. and Crane, M. (2008) An Agent-Based Approach to Immune Modeling: Priming Individual Response. World Academy of Science, Engineering and Technology, 2, 167-173.
Gutowitz, H. (1991) Cellular Automata: Theory and Experiment. MIT Press, Cambridge, MA.
Xiao, X., Shao, S. and Chou, K. (2006) A Probability Cellular Automaton Model for Hepatitis B Viral Infection. Biochemical and Biophysical Research Communications, 342, 605-610. https://doi.org/10.1016/j.bbrc.2006.01.166
Dos Santos, R.M.Z. (2001) Dynamics of HIV Infection: A Cellular Automata Approach. Physical Review Letters, 87, 1-4.
Sloot, P., Chen, F. and Boucher, C. (2002) Cellular Automata Model of Drug Therapy for HIV Infection. Lecture Notes in Computer Science, 2493, 282-293. https://doi.org/10.1007/3-540-45830-1_27
Shi, V., Tridane, A. and Kuang, Y. (2008) A Viral Load-Based Cellular Automata Approach to Modeling HIV Dynamics and Drug Treatment. Journal of Theoretical Biology, 253, 24-35. https://doi.org/10.1016/j.jtbi.2007.11.005
Gonzalez, R.E.R., Coutinho, S., Dos Santos, R.M.Z. and Figueiredo, P.H. (2013) Dynamics of HIV Infection under Antiretroviral Therapy: A Cellular Automata Approach. Physica A: Statistical Mechanics and Its Applications, 392, 4701-4716. https://doi.org/10.1016/j.physa.2013.05.056
Gonzalez, R.E.R., Figueiredo, P.H. and Coutinho, S. (2012) Cellular Automata Approach for the Dynamics of HIV Infection under Antiretroviral Therapies: The Role of Virus Diffusion. Physica A: Statistical Mechanics and Its Applications, 392, 4717- 4725. https://doi.org/10.1016/j.physa.2012.10.036
Jafelice, R.M., Bechara, B.F.Z., Barros, L.C., Bassanezi, R.C. and Gomide, F. (2009) Cellular Automata with Fuzzy Parameters in Microscopic Study of Positive HIV Individuals. Mathematical and Computer Modeling, 50, 32-44. https://doi.org/10.1016/j.mcm.2009.01.008
Mostafa, K., Khalid, H. and Noura, Y. (2014) Modeling the Adaptive Immune Response in Hiv Infection Using a Cellular Automata. International Journal of Engineering and Computer Science, 3, 5040-5045.
Rowland-Jones, S., Pinheiro, S., Kaul, R., Hansasuta, P., Gillespie, G., Dong, T., et al. (2001) How Important Is the “Quality” of the Cytotoxic T Lymphocyte (CTL) Response in Protection against HIV Infection? Immunology Letters, 79, 15-20. https://doi.org/10.1016/S0165-2478(01)00261-9
Yang, O., Sarkis, P., Ali, A., Harlow, J., Brander, C., Kalams, S., et al. (2003) Determinants of HIV-1 Mutational Escape from Cytotoxic T Lymphocytes. The Journal of Experimental Medicine, 197, 1365-1375. https://doi.org/10.1084/jem.20022138
Connick, E., Marr, D.G., Zhang, X.Q., Clark, S.J., Saag, M.S., Schooley, R.T., et al. (2009) HIV-Specific Cellular and Humoral Immune Response in Primary HIV Infection. AIDS Research and Human Retroviruses, 12, 1129-1140. https://doi.org/10.1089/aid.1996.12.1129
Loscalzo, J., Kasper, D., Jameson, J., Hauser, S., Fauci, A. and Longo, D. (2015) Harrison’s Principal of Internal Medicine—Infectious Diseases Part. 19th Edition, McGraw-Hill, New York.
Alcamo, I.E. (2003) AIDS: The Biological Basis. 3rd Edition, Jones and Bartlett Publishers, London.
McCune, J.M. (2001) The Dynamics of CD4+T-Cell Depletion in HIV Disease. Nature, 410, 974-979. https://doi.org/10.1038/35073648
Tassie, J.M., Grabar, S., Lancar, R., Deloumeaux, J., Bentata, M. and Costagliola, D. (2002) Time to AIDS from 1992 to 1999 in HIV-1 Infected Subjects with Known Date of Infection. Journal of Acquired Immune Deficiency Syndromes, 30, 81-87. https://doi.org/10.1097/00042560-200205010-00011
Althaus, C.L. and De Boer, R.J. (2011) Implications of CTL-Mediated Killing of HIV-Infected Cells during the Non-Productive Stage of Infection. PLoS ONE, 6, e16468. https://doi.org/10.1371/journal.pone.0016468
Pat Bucy, R. (2004) Viral and Cellular Dynamics in HIV Disease. Current HIV/ AIDS Reports, 1, 41-46.
Caetano Marco, L.A., de Souza Jam, F. and Yoneyama, T. (2008) Optimal Medication in HIV Seropositive Patient Treatment Using Fuzzy Cost Function. American Control Conference, Seattle, 11-13 June 2008, 2227-2232.
Xia, X. (2007) Modelling of HIV Infection: Vaccine Readiness, Drug Effectiveness and Therapeutical Failures. Journal of Process Control, 17, 253-260. https://doi.org/10.1016/j.jprocont.2006.10.007
Charbonneau, P. (2002) An Introduction to Genetic Algorithms for Numerical Optimization. NCAR Technical Note, Boulder, Colorado.
Roeva, O. (2005) Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison. Bioautomation, 3, 19-28.
Autran, B., Carcelain, G., Li, T.S., Blanc, C., Mathez, D. and Tubiana, R. (1997) Positive Effects of Combined Antiretroviral Therapy on CD4+T Cell Homeostasis and Function in Advanced HIV Disease. Science, 277, 112-116. https://doi.org/10.1126/science.277.5322.112
Castiglione, F., Pappalardo, F., Bernaschi, M. and Motta, S. (2007) Optimization of HAART with Genetic Algorithms and Agent-Based Models of HIV Infection. Bioinformatics, 23, 3350-3355. https://doi.org/10.1093/bioinformatics/btm408
Goldberg, D.E. and Deb, K. (1991) A Comparative Analysis of Selection Schemes Used in Genetic Algorithms. In: Rawlins, G., Ed., Foundations of Genetic Algorithms, Morgan Kaufmann Publishers, San Mateo, CA, 69-93.
Zhang, Z., Notermans, D.W., Sedgewick, G., Cavert, W., Wietgrefe, S., Zupancic, M., et al. (1998) Kinetics of CD4+T Cell Repopulation of Lymphoid Tissues after Treatment of HIV-1 Infection. PNAS, 95, 1154-1159. https://doi.org/10.1073/pnas.95.3.1154
Notermans, D.W., Staskus, K., Wietgrefe, S.W. and Zupancic, M. (1997) Kinetics of Response in Lymphoid Tissues to Antiretroviral Therapy of HIV-1 Infection. Science, 276, 960-964. https://doi.org/10.1126/science.276.5314.960
Ruffault, A., Michelet, C., Jacquelinet, C., Guist’au, O., Genetet, N., Bariou, C., Colimon, R. and Cartier, F. (1995) The Prognostic Value of Plasma Viremia in HIV- Infected Patients under AZT Treatment: A Two-Year Follow-Up Study. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology, 9, 243-248. https://doi.org/10.1097/00042560-199507000-00004
Adams, B.M., Banks, H.T., Davidian, M., Kwon, H., Tran, H.T., Wynne, S.N., et al. (2005) HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols. Journal of Computational and Applied Mathematics, 18, 10-49. https://doi.org/10.1016/j.cam.2005.02.004
Pappalardo, F., Mastriani, E., Lollini, P.L. and Motta, S. (2006) Genetic Algorithm against Cancer. Lectures Notes in Computer Science, 3849, 223-228. https://doi.org/10.1007/11676935_27