Driving Evolution Towards Discovery of Patterns in Sets of Weakly-Conserved DNA Sequences

An evolutionary algorithm is used to evolve a population of self-driving automata (SDAs), modified state machines, that are used to produce output in the form of a DNA sequence. The SDAs are evaluated based on their ability to create sequences that closely match all DNA sequences within a given set. This is evaluated in a pairwise fashion, but attempts to match all sequences concurrently, using two fitness functions that are differentiated by whether they allow for gaps. Additionally, a secondary fitness metric, Sequence Diversity Fitness, encourages diversity among the output of the SDAs within the population throughout evolution. The target sequences are Φ-segments of dehydrin proteins, which are weakly-conserved, can vary considerably in length, and for which traditional methods fail when used to find patterns within them. The ultimate goal is to use SDAs to assist in identifying patterns within Φ-segments. Locating such a pattern could prove fruitful for understanding the functions of dehydrins and how they contribute to the protection of plants from abiotic stresses. Several sets of target sequences are used for analysis, with some sets being more closely-related than others. The evolutionary algorithm was found to produce sequences that matched (according to one of the fitness functions) up to 100% of a given set of target sequences under certain conditions, with closely-related sequences being more accurately matched.