Effect of bio-inspired multi-stage regulations for diagnostic molecular automata

Bio-inspired natural computing is expected to bring new solutions to various problems confronting us. We already presented a novel concept for transcriptome diagnosis using transcription-based molecular automata. Here we introduce new multi-stage regulations to this diagnostic system in order to reduce the background noise and increase the sensitivity. We use two explicit detector probes with a stem and a memory loop at the pre-transcriptional stage, and one implicit binary aptameric sensor at the post-transcriptional stage. In addition to this we adopt the error control element to improve the detection ability. Consequently we found that the noise was reduced to one-forty-fifth and the sensitivity increased five-fold by the regulation using the combination of several strategies. Basically the living organisms control themselves by complex and dexterous manners. The incorporation of bio-inspired redundant strategies bring the all-too-simple and unstable artificial systems close to the robust and reliable ones.