The need for processing biological information is rapidly growing, owing to the masses of new information in digital form being produced at this time. Old methodologies for processing it can no longer keep up with this rate of growth. The methods of Artificial Intelligence (AI) in general and of language processing in particular can offer much towards solving this problem. However, interdisciplinary research between language processing and molecular biology is not yet widespread, partly because of the effort needed for each specialist to understand the other one's jargon. We argue that by looking at the problems of molecular biology from a language processing perspective, and using constraint based logic methodologies we can shorten the gap and make interdisciplinary collaborations more effective. We shall discuss several sequence analysis problems in terms of constraint based formalisms such Concept Formation Rules, Constraint Handling Rules (CHR) and their grammatical counterpart, CHRG. We postulate that genetic structure analysis can also benefit from these methods, for instance to reconstruct from a given RNA secondary structure, a nucleotide sequence that folds into it. Our proposed methodologies lend direct executability to high level descriptions of the problems at hand and thus contribute to rapid while efficient prototyping.

protein structure, RNA secondary structure, gene prediction, concept formation, constraint handling rules, constraint handling rule grammars