The human immune system uses complex mechanisms to generate enough antibody diversity to effectively protect against a wide array of potential antigens. These mechanisms obfuscate the germline predecessors of mature antibodies, making it difficult to produce a comprehensive model of the immune system. Interestingly, this problem also arises in the production of next-generation anti-viral software that use biomimicry to model malicious software as a recombination of attack patterns. Current methods fail to solve this problem in bicomputation because they ignore somatic hypermutation, one of the key methods of diversity generation. In this thesis, computational analysis is performed to develop a better model of somatic hypermutation, which is then used to improve antibody predecessor identification performance.
