A viral load-based cellular automata approach to modeling HIV dynamics and drug treatment

Shi, Veronica J.; Tridane, Abdessamad; Kuang, Yang

doi:10.1016/j.jtbi.2007.11.005

Cited by 28 publications

(23 citation statements)

References 25 publications

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“…5) Latently infected cells (A 0 ): this is the state of infected cells which have kept the latent infection in to themselves for a time and they are not capable of spreading the infection during this period of time. But they may be re-activated after a time delay Ƭ 2 and if it happens, they will be able to infect their own adjacent healthy cells [14] 12) Anti bodies (AB): the state of anti bodies that are responsible for killing the viruses [18].…”

Section: Proposed Cellular Automata Modelmentioning

confidence: 99%

“…Or with probability of (1 − P RTI ) × (1 − P PI ) two modes may occur: 1) in the absence of infecting factors it remains at H state and its age will be increased one unit for the next iteration, 2) in the presence of infecting factors it becomes an active infected cell (A 1 ) with age of zero with probability of P inf , or it becomes a latently infected cell (A 0 ) with age of zero with probability of (1 − P inf ) [14] [15].…”

Section: The Rules Of Proposed Ca Modelmentioning

confidence: 99%

“…Rule 5) The rules for updating the state of a latently infected cell (A 0 ): a) Each of A 0 cells after a time delay T Re-activation will be reactivated with probability of P Re-activation and becomes a new A 1 cell with the age of zero for the next iteration; or remains an A 0 with probability of (1 − P Reactivation ) and dies for the next iteration [14].…”

Section: The Rules Of Proposed Ca Modelmentioning

confidence: 99%

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

Golpayegani¹,

Jafari‬²,

Dabanloo³

2017

JBiSE

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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.

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Section: Proposed Cellular Automata Modelmentioning

confidence: 99%

Section: The Rules Of Proposed Ca Modelmentioning

confidence: 99%

Section: The Rules Of Proposed Ca Modelmentioning

confidence: 99%

See 1 more Smart Citation

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

Golpayegani¹,

Jafari‬²,

Dabanloo³

2017

JBiSE

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“…Although restrictive, the mathematical properties of these types of models were well characterised. Several types of CA models have been developed to model different aspects of the immune system Seiden and Celada, 1992;Morpurgo et al, 1995; Zorzenon dos Santos and Coutinho, 2001;Shi et al, 2008) of which the immune simulator IMMSIM ( Kleinstein and Seiden, 2000) is probably the best known. However, most of these CA models involve essential modifications of the original concepts of CA (e.g.…”

Section: Category 2: Models That Consider the Impact Of Infection On mentioning

confidence: 99%

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

Doeschl‐Wilson

2011

Animal

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Compared with the application of mathematical models to study human diseases, models that describe animal responses to pathogen challenges are relatively rare. The aim of this review is to explain and show the role of mathematical host-pathogen interaction models in providing underpinning knowledge for improving animal health and sustaining livestock production. Existing host-pathogen interaction models can be assigned to one of three categories: (i) models of the infection and immune system dynamics, (ii) models that describe the impact of pathogen challenge on health, survival and production and (iii) models that consider the co-evolution of host and pathogen. State-of-the-art approaches are presented and discussed for models belonging to the first two categories only, as they concentrate on the host-pathogen dynamics within individuals. Models of the third category fall more into the class of epidemiological models, which deserve a review by themselves. An extensive review of published models reveals a rich spectrum of methodologies and approaches adopted in different modelling studies, and a strong discrepancy between models concerning diseases in animals and models aimed at tackling diseases in humans (most of which belong to the first category), with the latter being generally more sophisticated. The importance of accounting for the impact of infection not only on health but also on production poses a considerable challenge to the study of host-pathogen interactions in livestock. This has led to relatively simplistic representations of host-pathogen interaction in existing models for livestock diseases. Although these have proven appropriate for investigating hypotheses concerning the relationships between health and production traits, they do not provide predictions of an animal's response to pathogen challenge of sufficient accuracy that would be required for the design of appropriate disease control strategies. A synthesis between the modelling methodologies adopted in categories 1 and 2 would therefore be desirable. The progress achieved in mathematical modelling to study immunological processes relevant to human diseases, together with the current advances in the generation and analysis of biological data related to animal diseases, offers a great opportunity to develop a new generation of host-pathogen interaction models that take on a fundamental role in the study and control of disease in livestock.

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“…On such consideration, it is thus reasonable to model the interaction among the immune system cells in the lymphatic tissues using a square lattice. Many articles [1], [5]- [7] have developed CA models to explain the dynamics of HIV infection. Most of these CA models only considered the dynamics in the lymph node.…”

Section: Introductionmentioning

confidence: 99%

Investigating Combined Drug and Plasma Apheresis Therapy of HIV Infection by Double Compartment Cellular Automata Simulation

Moonchai¹,

Lenbury²

2016

IJCTE

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Abstract-It has become widely acknowledged that potent combination drug therapy of patients infected with Human Immunodeficiency Virus can significantly prolong life, but exerts detrimental effect on a patient's quality of life. Lymph nodes and lymphatic tissue are busy sites of immunological activity. Germs are brought there by the cells of the immune system to be broken down and analyzed. However, clinics commonly used blood tests for viral load therein to assess the effect of drug therapy. We are therefore of the opinion that most cellular automata models in the past that only considered viral proliferation in the lymph node cannot give a sufficiently accurate picture of the dynamics of HIV infection, especially when the impact of treatments that combine both drugs and plasma apheresis are in question. We thus present a two-compartment cellular automata (CA) model, which simulates the dynamics of HIV infection in both the lymph node and blood compartments when drugs and plasma apheresis are applied simultaneously. It is found that the combined usage of these two types of therapy yields more favorable outcome than the sole use of plasma apheresis.

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A viral load-based cellular automata approach to modeling HIV dynamics and drug treatment

Cited by 28 publications

References 25 publications

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

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

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

Investigating Combined Drug and Plasma Apheresis Therapy of HIV Infection by Double Compartment Cellular Automata Simulation

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