Rule-based in vitro molecular classification and visualization

Abstract

Molecular computing using programmable nucleic acids has been attracting attention for use in autonomous sensing systems and information processing systems by interacting with a biological environment. Here, we introduce a rule-based in vitro molecular classification system that can classify disease patterns using several microRNA (miRNA) markers via the assembly of programmed DNA strands. The classification rules were derived by analyzing large-scale miRNA expression data obtained from a public database, and the identified rules were converted into DNA sequences. Classification was performed via the detection of miRNA markers in the rules. The classification results were reported as a binary output pattern according to their hybridization to the rule sequences, which can be conveniently visualized using gold nanoparticle aggregation. Our results demonstrate the utility of in vitro molecular classification by illustrating one of the ways in which molecular computing can be used in future biological and medical applications.

Molecular computing using programmable nucleic acids has been attracting attention for use in autonomous sensing systems and information processing systems by interacting with a biological environment. Here, we introduce a rule-based in vitro molecular classification system that can classify disease patterns using several microRNA (miRNA) markers via the assembly of programmed DNA strands. The classification rules were derived by analyzing large-scale miRNA expression data obtained from a public database, and the identified rules were converted into DNA sequences. Classification was performed via the detection of miRNA markers in the rules. The classification results were reported as a binary output pattern according to their hybridization to the rule sequences, which can be conveniently visualized using gold nanoparticle aggregation. Our results demonstrate the utility of in vitro molecular classification by illustrating one of the ways in which molecular computing can be used in future biological and medical applications.