The acquired immune system is capable of specialising a defence of an organism in response to the its antigenic environment. This complex biological system possess interesting information processing features such as learning, memory, and the ability to generalize. The clonal selection theory is a cornerstone of modern biology in understanding the acquired immune system from the perspective of B-lymphocyte cells and antibody diversity. This work presents a series of computational adaptive systems inspired by features of the biological immune system and the clonal selection theory in particular. Starting with basic clonal operators as principle components, models are presented in increasing complexity from canonical clonal selection models, to discretised architecture models, to finally advanced vaccination, evolution, and ontogenetic models. In addition to providing the basis to an interesting line investigation in the field of artificial immune system, this series of adaptive models presents a hierarchal framework from which existing and future adaptive models inspired by the acquired immune system can be interpreted and related.