How the Immune System Generates Diversity: Pathogen Space Coverage with Random and Evolved Antibody Libraries

The immune system uses many strategies to generate its enormous repertoire of diverse antibodies, but their relative importance is not understood. As part of a larger project to quantify their relative contributions, we have studied how the survival probability of an individual scales with the size of its germline-encoded antibody repertoire [11], in the context of a shape-space model [12]. Shape-space models are a popular modeling device, but it is unclear how realistic they are for studying real biological data [1]. Here, we introduce a more general framework, in which we can study many antibody-pathogen matching rules, including the shape-space model. We use the genetic algorithm as a model of evolution to explore the behavior of the evolved antibody repertoires. For the antibody/pathogen matching rules that we studied, the scaling relation between fitness and the size of the evolved antibody library is only a shifted variant of the scaling relation that we obtain with random libraries of the same size. We also analyze the type of antibodies that are evolved by our genetic algorithm, comparing them to the germline encoded antibodies that are expressed in newborns. We discuss the implications of our results for recent experiments with phage antibody libraries.