On the Aging Dynamics in an Immune Network Model

Recently we have used a cellular automata model which describes the dynamics of a multi-connected network to reproduce the refractory behavior and aging effects obtained in immunization experiments performed with mice when subjected to multiple perturbations. In this paper we investigate the similarities between the aging dynamics observed in this multi-connected network and the one observed in glassy systems, by using the usual tools applied to analyze the latter. An interesting feature we show here is that the model reproduces the biological aspects observed in the experiments during the long transient time it takes to reach the stationary state. Depending on the initial conditions, and without any perturbation, the system may reach one of a family of long-period attractors. The pertrubations may drive the system from its natural attractor to other attractors of the same family. We discuss the different roles played by the small random perturbations (noise) and by the large periodic perturbations (immunizations)

Immunization and Aging: a Learning Process in the Immune Network

The immune system can be thought as a complex network of different interacting elements. A cellular automaton, defined in shape-space, was recently shown to exhibit self-regulation and complex behavior and is, therefore, a good candidate to model the immune system. Using this model to simulate a real immune system we find good agreement with recent experiments on mice. The model exhibits the experimentally observed refractory behavior of the immune system under multiple antigen presentations as well as loss of its plasticity caused by aging.Comment: 4 latex pages, 3 postscript figures attached. To be published in Physical Review Letters (Tentatively scheduled for 5th Oct. issue

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

A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance

Background: Detailed analysis of the dynamic interactions among biological, environmental, social, and economic factors that favour the spread of certain diseases is extremely useful for designing effective control strategies. Diseases like tuberculosis that kills somebody every 15 seconds in the world, require methods that take into account the disease dynamics to design truly efficient control and surveillance strategies. The usual and well established statistical approaches provide insights into the cause-effect relationships that favour disease transmission but they only estimate risk areas, spatial or temporal trends. Here we introduce a novel approach that allows figuring out the dynamical behaviour of the disease spreading. This information can subsequently be used to validate mathematical models of the dissemination process from which the underlying mechanisms that are responsible for this spreading could be inferred. Methodology/Principal Findings: The method presented here is based on the analysis of the spread of tuberculosis in a Brazilian endemic city during five consecutive years. The detailed analysis of the spatio-temporal correlation of the yearly geo-referenced data, using different characteristic times of the disease evolution, allowed us to trace the temporal path of the aetiological agent, to locate the sources of infection, and to characterize the dynamics of disease spreading. Consequently, the method also allowed for the identification of socio-economic factors that influence the process. Conclusions/Significance: The information obtained can contribute to more effective budget allocation, drug distribution and recruitment of human skilled resources, as well as guiding the design of vaccination programs. We propose that this novel strategy can also be applied to the evaluation of other diseases as well as other social processes.Instituto do Milenio REDE-TBConselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Fundacao de Amparo a Ciencia e Tecnologia do Estado de Pernambuco (FACEPE)[0012-05.03/04]Fundacao de Amparo a Ciencia e Tecnologia do Estado de Pernambuco (FACEPE)[0203-1.05/08