  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.
