title: Principles of adaptive sorting revealed by in silico evolution.
abstract: Many biological networks have to filter out useful information from a vast excess of spurious interactions. In this Letter, 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.
authors.affiliation: Physics Department, McGill University, Montreal, Quebec, Canada H3A 2T8.
authors.forename: Jean-Benoît
authors.initials: JB
authors.lastname: Lalanne
authors.forename: Paul
authors.initials: P
authors.lastname: François
mesh_terms: D000465:Algorithms; D000938:Antigen-Presenting Cells / Q000276:immunology*; D003198:Computer Simulation; D008024:Ligands; D008285:Major Histocompatibility Complex / Q000276:immunology; D008954:Models, Biological; D018448:Models, Immunological; D011948:Receptors, Antigen, T-Cell / Q000276:immunology; D013601:T-Lymphocytes / Q000276:immunology*
chemical_list: D008024:Ligands; D011948:Receptors, Antigen, T-Cell