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Dynamics of HIV Infection: An entropic–energetic view

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  • González, Ramón E.R.
  • Figueirêdo, P.H.
  • Coutinho, S.

Abstract

We propose a time-parametrized analogy between the thermodynamic behavior of a 3-level energy system and the progression of the HIV infection described by the cell population evolution generated by an appropriated cellular automata model. The development of internal energy and its fluctuations, and of the entropy of the 3-level energy system allows the identification of an effective temperature that uniquely characterizes the three main stages of the dynamic process of HIV infection (primary infection, clinical latency, and development of AIDS). Furthermore, this thermodynamical equivalence allows obtaining the instants associated with particular time intervals of the evolution of internal energy and entropy, which are not quantitatively accessible by the usual dynamic models based on differential equations or cellular automata. The maximum entropy point, which marks the threshold between the states of positive and negative temperatures, also corresponds to the onset of the immune system’s exhaustion and the concomitant and inexorable progression to AIDS. Such point lies in the time interval where the energy inversion mechanisms in the populations of non-infected and infected cells occur. This time lag is also characterized by considerable fluctuations of the internal energy when different (patient) samples are compared.

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  • González, Ramón E.R. & Figueirêdo, P.H. & Coutinho, S., 2020. "Dynamics of HIV Infection: An entropic–energetic view," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437120300601
    DOI: 10.1016/j.physa.2020.124239
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    References listed on IDEAS

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    1. González, Ramón E.R. & Coutinho, Sérgio & Zorzenon dos Santos, Rita Maria & de Figueirêdo, Pedro Hugo, 2013. "Dynamics of the HIV infection under antiretroviral therapy: A cellular automata approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4701-4716.
    2. Figueirêdo, P.H. & Coutinho, S. & Zorzenon dos Santos, R.M., 2008. "Robustness of a cellular automata model for the HIV infection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6545-6552.
    3. Solovey, Guillermo & Peruani, Fernando & Ponce Dawson, Silvina & Maria Zorzenon dos Santos, Rita, 2004. "On cell resistance and immune response time lag in a model for the HIV infection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 543-556.
    4. Pandey, R.B., 1991. "Cellular automata approach to interacting cellular network models for the dynamics of cell population in an early HIV infection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 179(3), pages 442-470.
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    6. González, Ramón E.R. & de Figueirêdo, Pedro Hugo & Coutinho, Sérgio, 2013. "Cellular automata approach for the dynamics of HIV infection under antiretroviral therapies: The role of the virus diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4717-4725.
    7. Benyoussef, A & HafidAllah, N.El & ElKenz, A & Ez-Zahraouy, H & Loulidi, M, 2003. "Dynamics of HIV infection on 2D cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 506-520.
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