Immune networks: multi-tasking capabilities near saturation

TL;DR

This paper models immune networks as sparse graph spin systems, demonstrating that T-lymphocytes can coordinate responses to many antigens simultaneously, with performance degrading gracefully at high loads.

Contribution

It extends previous models by analyzing immune network capacity in the extensive load regime using advanced statistical mechanics, showing solvability despite complex graph structures.

Findings

T-lymphocytes can handle extensive antigen diversity in parallel.

System undergoes a second order transition with increasing load.

Performance degrades gracefully under high pattern dilution.

Abstract

Pattern-diluted associative networks were introduced recently as models for the immune system, with nodes representing T-lymphocytes and stored patterns representing signalling protocols between T- and B-lymphocytes. It was shown earlier that in the regime of extreme pattern dilution, a system with $N_T$ T-lymphocytes can manage a number $N_B!=!\order(N_T^\delta)$ of B-lymphocytes simultaneously, with $\delta!<!1$. Here we study this model in the extensive load regime $N_B!=!\alpha N_T$, with also a high degree of pattern dilution, in agreement with immunological findings. We use graph theory and statistical mechanical analysis based on replica methods to show that in the finite-connectivity regime, where each T-lymphocyte interacts with a finite number of B-lymphocytes as $N_T\to\infty$, the T-lymphocytes can coordinate effective immune responses to an extensive number of distinct antigen invasions in parallel. As $\alpha$ increases, the system eventually undergoes a second order transition to a phase with clonal cross-talk interference, where the system's performance degrades gracefully. Mathematically, the model is equivalent to a spin system on a finitely connected graph with many short loops, so one would expect the available analytical methods, which all assume locally tree-like graphs, to fail. Yet it turns out to be solvable. Our results are supported by numerical simulations.