Cellular automata with fuzzy parameters in microscopic study of positive HIV individuals

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The aim of this paper is to introduce a model to simulate the evolution of HIV in the bloodstream of positive individuals subject to medical treatment and monitoring of the medication potency and treatment adhesion. For this purpose, a cellular automata approach coupled with fuzzy set theory is developed to study the HIV evolution. The study is conducted using two cellular automata models in two corresponding steps. The first step concerns HIV dynamics in individuals with no antiretroviral therapy. In this case, the trajectory developed by the cellular automaton model depicts all phases shown in the known history of HIV dynamics. The main purpose of the first step is to serve as a model validation step. The second step extends the model developed in the first step to consider HIV dynamics in individuals under antiretroviral therapy. The effects of antiretroviral therapy in the cellular automaton model are modeled using a fuzzy rule-based system with two inputs, the medication potency and treatment adhesion rate of the individuals to the therapy. The fuzzy rule-based system is used to compute the number of HIV infected CD4+ cells and the viral replication. The results developed by the cellular automaton model with antiretroviral therapy are close to the ones reported in the literature and agree with the behavior expected by experts [J. Guedj, R. Thiebaut, D. Commenges, Practical identifiability of HIV dynamics models, Bulletin of Mathematical Biology 69 (8) (2007) 2493-2513], [R. A. Filter, X. Xia, C. M. Gray, Dynamic HIV/AIDS parameter estimation with application to a vaccine readiness study in southern Africa, IEEE Transactions on Biomedical Engineering 52 (5) (2005) 784-791] and [D. A. Ouattara, M. J. Mhawej, C. H. Moog, Clinical tests of therapeutical failures based on mathematical modeling of the HIV infection, Systems Biology (2008) 230-241 (special issue)]. (C) 2009 Elsevier Ltd. All rights reserved.50416713244Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Research Foundation of the State of Minas Gerais [CEX-109/04]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Brazilian National Research Council [152068/2007-4]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Research Foundation of the State of Sao Paulo [06/05920-7]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Research Foundation of the State of Minas Gerais [CEX-109/04]Brazilian National Research Council [152068/2007-4]Research Foundation of the State of Sao Paulo [06/05920-7]CNPq [307890/2006-6, 304857/2006-8

Predicting the outcomes of HIV treatment interruptions using computational modelling

In the past 30 years, HIV infection made a transition from fatal to chronic disease due to the emergence of potent treatment largely suppressing viral replication. However, this medication must be administered life-long on a regular basis to maintain viral suppression and is not always well tolerated. Any interruption of treatment causes residual virus to be reactivated and infection to progress, where the underlying processes occurring and consequences for the immune system are still poorly understood. Nonetheless, treatment interruptions are common due to adherence issues or limited access to antiretroviral drugs. Early clinical studies, aiming at application of treatment interruptions in a structured way, gave contradictory results concerning patient safety, discouraging further trials. In-silico models potentially add to knowledge but a review of the Literature indicates most current models used for studying treatment interruptions (equation-based), neglect recent clinical findings of collagen formation in lymphatic tissue due to HIV and its crucial role in immune system stability and efficacy. The aim of this research is the construction and application of so-called ‘Bottom-Up’ models to allow improved assessment of these processes in relation to HIV treatment interruptions. In this regard, a novel computational model based on 2D Cellular Automata for lymphatic tissue depletion and associated damage to the immune system was developed. Hence, (i) using this model, the influence of spatial distribution of collagen formation on HIV infection progression speed was evaluated while discussing aspects of computational performance. Further, (ii) direct Monte Carlo simulations were employed to explore the accumulation of tissue impairment due to repeated treatment interruptions and consequences for long-term prognosis. Finally, (iii) an inverse Monte Carlo approach was used to reconstruct yet unknown characteristics of patient groups. This is based on sparse data from past clinical studies on treatment interruptions with the aim of explaining their contradictory results