Principles of Adaptive Sorting Revealed by In Silico Evolution

TL;DR

This paper uses in silico evolution to uncover principles of adaptive sorting in biological networks, particularly in immune recognition, revealing common network motifs and their design features through computational modeling.

Contribution

It introduces the concept of adaptive sorting as a key network motif and demonstrates its evolutionary emergence and relevance in immune recognition models.

Findings

Adaptive sorting motifs are predicted by computational evolution.

Identified network structures are present in existing immune models.

Deep analogy established between immune recognition and biochemical adaptation.

Abstract

Many biological networks have to filter out useful information from a vast excess of spurious interactions. We use computational evolution to predict design features of networks processing ligand categorization. The important problem of early immune response is considered as a case-study. Rounds of evolution with different constraints uncover elaborations of the same network motif we name adaptive sorting. Corresponding network substructures can be identified in current models of immune recognition. Our work draws a deep analogy between immune recognition and biochemical adaptation.