Providing a Therapeutic Scheduling for HIV Infected Individuals with Genetic Algorithms Using a Cellular Automata Model of HIV Infection in the Peripheral Blood Stream

Abstract

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.

An epidemiological model for HIV infection in a population using type-2 fuzzy sets and cellular automaton

“…An important feature to be noticed is that in a cellular automaton with simple rules, as those used in the simulations of the study, can capture a more complex behaviour which is closer to the reality. Thus, as in papers already published (Mielke and Pandey 1998;Santos and Coutinho 2001;Sloot et al 2002;Benyoussef et al 2003;Jafelice et al 2009;Mostafa et al 2014;Jafelice et al 2015), and Golpayegani et al 2017, as well as in this research, great usefulness of the simulations for knowledge and control of AIDS has been demonstrated.…”