58 research outputs found
Long Term and Short Term Effects of Perturbations in a Immune Network Model
In this paper we review the trajectory of a model proposed by Stauffer and
Weisbuch in 1992 to describe the evolution of the immune repertoire and present
new results about its dynamical behavior. Ten years later this model, which is
based on the ideas of the immune network as proposed by Jerne, has been able to
describe a multi-connected network and could be used to reproduce immunization
and aging experiments performed with mice. Besides its biological implications,
the physical aspects of the complex dynamics of this network is very
interesting {\it per se}. The immunization protocol is simulated by introducing
small and large perturbations (damages), and in this work we discuss the role
of both. In a very recent paper we studied the aging effects by using
auto-correlation functions, and the results obtained apparently indicated that
the small perturbations would be more important than the large ones, since
their cumulative effects may change the attractor of the dynamics. However our
new results indicate that both types of perturbations are important. It is the
cooperative effects between both that lead to the complex behavior which allows
to reproduce experimental results.Comment: 15 pages, 5 figure
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
A Simple Cellular Automaton Model for Influenza A Viral Infections
Viral kinetics have been extensively studied in the past through the use of
spatially homogeneous ordinary differential equations describing the time
evolution of the diseased state. However, spatial characteristics such as
localized populations of dead cells might adversely affect the spread of
infection, similar to the manner in which a counter-fire can stop a forest fire
from spreading. In order to investigate the influence of spatial
heterogeneities on viral spread, a simple 2-D cellular automaton (CA) model of
a viral infection has been developed. In this initial phase of the
investigation, the CA model is validated against clinical immunological data
for uncomplicated influenza A infections. Our results will be shown and
discussed.Comment: LaTeX, 12 pages, 18 EPS figures, uses document class ReTeX4, and
packages amsmath and SIunit
Emergence of Hierarchy on a Network of Complementary Agents
Complementarity is one of the main features underlying the interactions in
biological and biochemical systems. Inspired by those systems we propose a
model for the dynamical evolution of a system composed by agents that interact
due to their complementary attributes rather than their similarities. Each
agent is represented by a bit-string and has an activity associated to it; the
coupling among complementary peers depends on their activity. The connectivity
of the system changes in time respecting the constraint of complementarity. We
observe the formation of a network of active agents whose stability depends on
the rate at which activity diffuses in the system. The model exhibits a
non-equilibrium phase transition between the ordered phase, where a stable
network is generated, and a disordered phase characterized by the absence of
correlation among the agents. The ordered phase exhibits multi-modal
distributions of connectivity and activity, indicating a hierarchy of
interaction among different populations characterized by different degrees of
activity. This model may be used to study the hierarchy observed in social
organizations as well as in business and other networks.Comment: 13 pages, 4 figures, submitte
Robustness of a Cellular Automata Model for the HIV Infection
An investigation was conducted to study the robustness of the results
obtained from the cellular automata model which describes the spread of the HIV
infection within lymphoid tissues [R. M. Zorzenon dos Santos and S. Coutinho,
Phys. Rev. Lett. 87, 168102 (2001)]. The analysis focussed on the dynamic
behavior of the model when defined in lattices with different symmetries and
dimensionalities. The results illustrated that the three-phase dynamics of the
planar models suffered minor changes in relation to lattice symmetry variations
and, while differences were observed regarding dimensionality changes,
qualitative behavior was preserved. A further investigation was conducted into
primary infection and sensitiveness of the latency period to variations of the
model's stochastic parameters over wide ranging values. The variables
characterizing primary infection and the latency period exhibited power-law
behavior when the stochastic parameters varied over a few orders of magnitude.
The power-law exponents were approximately the same when lattice symmetry
varied, but there was a significant variation when dimensionality changed from
two to three. The dynamics of the three-dimensional model was also shown to be
insensitive to variations of the deterministic parameters related to cell
resistance to the infection, and the necessary time lag to mount the specific
immune response to HIV variants. The robustness of the model demonstrated in
this work reinforce that its basic hypothesis are consistent with the
three-stage dynamic of the HIV infection observed in patients.Comment: 14 pages, 6 figures, 21 references, Latex style Elsar
- …