The chapter describes the theory of clonal selection and its usage in designing and implementing immunological algorithms for problem solving and learning. In detail, it presents various immune algorithms based on the clonal selection principle, analyzing computational time complexity, experimental results, similarities and differences. It introduces two paradigms to model immune algorithms: noisy channel, and Turing’s reaction-diffusion systems to build artificial immune systems for effective information processing and computing. The authors show how Libchaber DNA algorithm can be interpreted as an “in vitro” implementation of the clonal selection principle by means of molecular biology technology. These similarities witness the ubiquity of such a kind of information processing in nature and give evidence of the universality of the concept of computation. The authors’ intent is to provide a general framework that can be considered as a first core for in silico and in vitro computation based on the clonal selection theory.