Three-Dimensional Cellular Automaton for Modeling the Hepatitis B Virus Infection

Hepatitis B is considered as the most common hepatic in the world and may lead to cirrhosis and liver cancer. It is caused by the hepatitis B virus, which attacks and can damage the liver. In this paper we investigate a new mathematical model to study the dynamic process of HBV infection on the liver. This model is based on a three dimensional cellular automaton, which is composed of four state variables. The model takes into account the heterogeneous feature and the spatial localization of the population studied. Furthemore, since the virus doesn’t remain only on the liver surface but penetrates into the organ, our model describes better the behavior of interactions between cells and hepatitis B virus in the liver than the previous works found in the literature, which have used only two cellular automata in their models

HIV Reservoirs and Immune Surveillance Evasion Cause the Failure of Structured Treatment Interruptions: A Computational Study

Continuous antiretroviral therapy is currently the most effective way to treat HIV infection. Unstructured interruptions are quite common due to side effects and toxicity, among others, and cannot be prevented. Several attempts to structure these interruptions failed due to an increased morbidity compared to continuous treatment. The cause of this failure is poorly understood and often attributed to drug resistance. Here we show that structured treatment interruptions would fail regardless of the emergence of drug resistance. Our computational model of the HIV infection dynamics in lymphoid tissue inside lymph nodes, demonstrates that HIV reservoirs and evasion from immune surveillance themselves are sufficient to cause the failure of structured interruptions. We validate our model with data from a clinical trial and show that it is possible to optimize the schedule of interruptions to perform as well as the continuous treatment in the absence of drug resistance. Our methodology enables studying the problem of treatment optimization without having impact on human beings. We anticipate that it is feasible to steer new clinical trials using computational models

On the Dynamics of the Evolution of the HIV Infection

We use a cellular automata model to study the evolution of HIV infection and the onset of AIDS. The model takes into account the global features of the immune response to any pathogen, the fast mutation rate of the HIV and a fair amount of spatial localization. Our results reproduce quite well the three-phase pattern observed in T cell and virus counts of infected patients, namely, the primary response, the clinical latency period and the onset of AIDS. We have also found that the infected cells may organize themselves into special spatial structures since the primary infection, leading to a decrease on the concentration of uninfected cells. Our results suggest that these cell aggregations, which can be associated to syncytia, leads to AIDS.Comment: 4 pages, 3 postscript figure

Kinetics of Hepatitis B Virus Infection: A Cellular Automaton Model Study

We created a simple cellular automata (CA) model for hepatitis B infection dynamics associated with spatial structure performed under various ages of liver tissue correspond to different immune responses in order to study the effect of spatial heterogeneities on the dynamical evolution of a viral infection. The results of the simulations show biphasic nature of viral load decreases, as observed in the acute infection in real state. Our results also confirm the importance of the hepatocyte target cells, the spatial localization, and the local interactions on the dynamics of HBV infection, whereas models based on ordinary differential equations are not considered. Our model is quite simple with four states and only five parameters, however, the dynamics from the model qualitatively equivalent clinical data.

Agent-Based Modeling of Host-Pathogen Systems: The Successes and Challenges

Agent-based models have been employed to describe numerous processes in immunology. Simulations based on these types of models have been used to enhance our understanding of immunology and disease pathology. We review various agent-based models relevant to host-pathogen systems and discuss their contributions to our understanding of biological processes. We then point out some limitations and challenges of agent-based models and encourage efforts towards reproducibility and model validation.Comment: LaTeX, 12 pages, 1 EPS figure, uses document class REVTeX 4, and packages hyperref, xspace, graphics, amsmath, verbatim, and SIunit

A Simulation Framework to Investigate in vitro Viral Infection Dynamics

AbstractVirus infection is a complex biological phenomenon for which in vitro experiments provide a uniquely concise view where data is often obtained from a single population of cells, under controlled environmental conditions. Nonetheless, data interpretation and real understanding of viral dynamics is still hampered by the sheer complexity of the various intertwined spatio-temporal processes. In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. The aim of the model is to understand the key mechanisms of SARS-CoV infection dynamics during the first 24hours post infection. We interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles