That the flow of information from gene sequence to protein sequence depends on the translation of a code that could literally be described as digital is a truly incredible feat of nature. However, the process of translation is a noisy, stochastic, kinetic process that depends on many factors. The redundancy in the genetic code allows the transmission of additional, analogue information by varying some of these factors. How organisms use the redundancy is termed codon usage, and rare codons are those that are typically shunned in favour of other synonymous options. Synonymous variations to the codon usage pattern of a gene have been linked to disease, and can have huge effects on the functionality and quantity of protein produced from a gene, but the nature of these variations is complex and poorly understood. In some cases, rare codons appear to have a beneficial influence on expression. This thesis investigates the phenomenon of rare codons and attempts to elucidate their evolutionary role in optimal gene expression. It begins with the design of a novel statistical algorithm, which is used to generate a dataset of interesting genetic locations. The dataset is the subject of a hypothesis-driven investigation to discover meaningful biological correlates, and this is complemented by experimental work, to attempt to provide conclusive validation of the approach
