  Convolutional codes are error-correcting linear codes that utilize shift
registers to encode. These codes have an arbitrary block size and they can
incorporate both past and current information bits. DNA codes represent DNA
sequences and are defined as sets of words comprised of the alphabet A, C, T, G
satisfying certain mathematical bounds and constraints. The application of
convolutional code models to DNA codes is a growing field of biocomputation. As
opposed to block codes, convolutional codes factor in nearby information bits,
which makes them an optimal model for representing biological phenomena. This
study explores the properties of both convolutional codes and DNA codes, as
well as how convolutional codes are applied to DNA codes. It also proposes
revisions to improve a current convolutional code model for DNA sequences.
