Organization and evolution of synthetic idiotypic networks

Abstract

We introduce a class of weighted graphs whose properties are meant to mimic the topological features of idiotypic networks, namely the interaction networks involving the B-core of the immune system. Each node is endowed with a bit-string representing the idiotypic specificity of the corresponding B cell and a proper distance between any couple of bit-strings provides the coupling strength between the two nodes. We show that a biased distribution of the entries in bit-strings can yield fringes in the (weighted) degree distribution, small-worlds features, and scaling laws, in agreement with experimental findings. We also investigate the role of ageing, thought of as a progressive increase in the degree of bias in bit-strings, and we show that it can possibly induce mild percolation phenomena, which are investigated too.

13 pages

References in corpus (4)

• A Hebbian approach to complex network generation
• Percolation on correlated random networks
• Mean-field cooperativity in chemical kinetics
• Equilibrium statistical mechanics on correlated random graphs

Cited by in corpus (6)

• Immune networks: multi-tasking capabilities near saturation
• Parallel processing in immune networks
• Analogue neural networks on correlated random graphs
• The role of idiotypic interactions in the adaptive immune system: a belief-propagation approach
• Anergy in self-directed B lymphocytes from a statistical mechanics perspective
• Dilution of Ferromagnets via a Random Graph-based Strategy