January 30, 2012
Some years ago a cellular automata model was proposed to describe the evolution of the immune repertoire of B cells and antibodies based on Jerne's immune network theory and shape-space formalism. Here we investigate if the networks generated by this model in the different regimes can be classified as complex networks. We have found that in the chaotic regime the network has random characteristics with large, constant values of clustering coefficients, while in the ordered phase, the degree distribution of the network is exponential and the clustering coefficient exhibits power law behavior. In the transition region we observed a mixed behavior (random-like and exponential) of the degree distribution as opposed to the scale-free behavior reported for other biological networks. Randomness and low connectivity in the active sites allow for rapid changes in the connectivity distribution of the immune network in order to include and/or discard information and generate a dynamic memory. However it is the availability of the low concentration nodes to change rapidly without driving the system to pathological states that allow the generation of dynamic memory and consequently a reproduction of immune system behavior in mice. Although the overall behavior of degree correlation is positive, there is an interplay between assortative and disassortative mixing in the stable and transition regions regulated by a threshold value of the node degree, which achieves a maximum value on the transition region and becomes totally assortative in the chaotic regime.
Similar papers 1
March 3, 1998
The phenomenon of immunological memory has been known for a long time. But, the underlying mechanism is poorly understood. According to the theory of clonal selection the response to a specific invading antigen (e.g., bacteria) is offered by a specific clone of the cells. Some of the lymphocytes activated during the primary response remain dormant and keep circulating in the immune system for a long time carrying the memory of the encounter and, therefore, these long-lived ce...
January 6, 2008
We argue that immune system is an adaptive complex system. It is shown that it has emergent properties. Its network structure is of the small world network type. The network is of the threshold type, which helps in avoiding autoimmunity. It has the property that every antigen (e.g.virus or bacteria) is typically attacked by more than one effector. This stabilizes the equilibrium state. Modelling complex systems is discussed. Cellular automata (CA) type models are successful b...
September 24, 1998
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 antig...
August 26, 2020
Understanding and modelling the complexity of the immune system is a challenge that is shared by the ImmunoComplexiT$^1$ thematic network from the RNSC. The immune system is a complex biological, adaptive, highly diversified, self-organized and degenerative cognitive network of entities, allowing for a robust and resilient system with emergent properties such as anamnestic responses and regulation. The adaptive immune system has evolved into a complex system of billions of hi...
February 21, 2000
We numerically study a dynamical system model of an idiotypic immune network with a small number of degrees of freedom. The model was originally introduced by Varela et.al., and describes antibodies interacting in a body in order to prepare for the invasion of external antigens. The main purpose of this paper is to investigate the direction of change in the network system when antigens invade it. We investigate three models, original model, a modified model and a modified m...
January 21, 2010
The aim of this work is to try to bridge over theoretical immunology and disordered statistical mechanics. Our long term hope is to contribute to the development of a quantitative theoretical immunology from which practical applications may stem. In order to make theoretical immunology appealing to the statistical physicist audience we are going to work out a research article which, from one side, may hopefully act as a benchmark for future improvements and developments, from...
June 5, 2003
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 phy...
July 28, 2021
The physical interpretation of the functioning of the adaptive immune system, which has been thoroughly characterized on genetic and molecular levels, provides a unique opportunity to define an adaptive self-organizing biological system in its entirety. This paper describes a configuration space model of immune function, where directed chemical potentials of the system constitute a space of interactions. In the physical sense, the humoral adaptive immune system adjusts the ch...
December 9, 2010
We are interested in modeling theoretical immunology within a statistical mechanics flavor: focusing on the antigen-independent maturation process of B-cells, in this paper we try to revise the problem of self vs non-self discrimination by mature B lymphocytes. We consider only B lymphocytes: despite this is of course an oversimplification, however such a toy model may help to highlight features of their interactions otherwise shadowed by main driven mechanisms due to i.e. he...
May 4, 2015
Many events in the vertebrate immune system are influenced by some element of chance. The objective of the present work is to describe affinity maturation of B lymphocytes (in which random events are perhaps the most characteristic), and to study a possible network model of immune memory. In our model stochastic processes govern all events. A major novelty of this approach is that it permits studying random variations in the immune process. Four basic components are simulated...