Pattern recognition, as a subtopic of machine learning, is defined as the act of taking in raw data and acting based on the category of the data. This research explores the possibility to analyze outputs from multiple activated sensors using networks of downstream molecular logic gates with adjusted connections, and as a result obtain a classification, that is, a single output of 1 (Action) or 0 (No Action).
Logic gates based on nucleic acid catalysts (deoxyribozymes) accept and analyze one or more oligonucleotides as inputs, and produce a response, such as catalytic activity, usually visualized by an increase in fluorescence. The oligonucleotides that serve as inputs for logic gates can be incorporated into oligonucleotide-based recognition regions (aptamers). Such design can be used to construct a purely molecular expert system based on deoxyribozymes and aptamers, which will be compatible with physiological conditions, and will not use electronic components, or human inputs. The eventual goal of this research is to construct autonomous therapeutic molecular devices that recognize and correct changes in metabolic states.
