Constructing parallels between engineering frameworks for communication and biological information processing mechanisms is a challenge that if embraced can contribute to our quantitative understanding and ability to control complex molecular processes. Empirically, molecular hybridization is well studied and researchers continue to explore the idea of biomolecules as information processing systems. In this work we will discuss the implications of understanding hybridization using the mathematical framework of error control coding theory and how this requires not only the use of information theoretic analysis tools, but compels us to view and model biomolecular systems as information transmission and processing systems. Using the genetic communication theory paradigm, we investigate coding theory algorithms for in silico categorization of single nucleotide polymorphisms based on the calculation of syndromes (May, Dolan, et al. 2008). We explore the use of coding theory frameworks in the design of in vitro computational biosensors (May, Lee, et al. 2008) and highlight results for monitoring mutations in biomolecular systems.