A Self-Organizing Cognitive Network of Antibody Repertoire Development

摘要

A self-organizing cognitive network is mapped here onto the Id network model. The weight-vectors in this network represent some important topographical and biophysical parameters in the antibody-antigen affinity landscape. The Kohonen layers in the network correspond to affinity clones and the involved algorithm simulates the operations of clonal selection, hy-permutation, differentiation, diversity, and affinity maturation. Two significant features of this model are: (i) a computationally feasible and biophysically informative representation of the para/epitopes, and (ii) the ability to perform simultaneous (parallel) and associative computations in a multidimensional shape-space. Computational experiments with real data have shown cognitive properties of this network. The results also indicate scope in quantitative characterization of the metadynamics of the above operations/weights in the adaptive development of the antibody repertoire.