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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. ; This work was supported by NRF(2012-0005643, 2012-0005801) and BK21-IT. KAY was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2008-532-C00031). ; OAIID:oai:osos.snu.ac.kr:snu2013-01/102/0000002410/6 ; SEQ:6 ; PERF_CD:SNU2013-01 ; EVAL_ITEM_CD:102 ; USER_ID:0000002410 ; ADJUST_YN:Y ; EMP_ID:A002014 ; DEPT_CD:458 ; CITE_RATE:.86 ; FILENAME:rule-based in vitro molecular classification.pdf ; DEPT_NM:화학생물공학부 ; EMAIL:thpark@snu.ac.kr ; SCOPUS_YN:Y ; CONFIRM:Y

Molecular beacons are efficient and useful tools for quantitative detection of specific target nucleic acids. Thanks to their simple protocol, molecular beacons have great potential as substrates for biomolecular computing. Here we present a molecular beacon-based biomolecular computing method for quantitative detection and analysis of target nucleic acids. Whereas the conventional quantitative assays using fluorescent dyes have been designed for single target detection or multiplexed detection, the proposed method enables us not only to detect multiple targets but also to compute their quantitative information by weighted-sum of the targets. The detection and computation are performed on a molecular level simultaneously, and the outputs are detected as fluorescence signals. Experimental results show the feasibility and effectiveness of our weighted detection and linear combination method using molecular beacons. Our method can serve as a primitive operation of molecular pattern analysis, and we demonstrate successful binary classifications of molecular patterns made of synthetic oligonucleotide DNA molecules. (C) 2012 Elsevier Ireland Ltd. All rights reserved. ; This research was supported in part by Molecular Evolutionary Computing (MEC) project of MKE, in part by IT R&D Program of MKE/KEIT (10035348, mLife), in part by NRF grants of MEST (No. 2012-0005643, Videome; No. 2012-0005801, BrainNet), and in part by the BK21-IT program funded by Korean Government (MEST). KAY was supported by the Korea Research Foundation (KRF-2008-532-C00031). The ICT at Seoul National University provided research facilities for this study. ; OAIID:oai:osos.snu.ac.kr:snu2013-01/102/0000002410/3 ; SEQ:3 ; PERF_CD:SNU2013-01 ; EVAL_ITEM_CD:102 ; USER_ID:0000002410 ; ADJUST_YN:Y ; EMP_ID:A002014 ; DEPT_CD:458 ; CITE_RATE:1.784 ; FILENAME:biomolecular computation with.pdf ; DEPT_NM:화학생물공학부 ; EMAIL:thpark@snu.ac.kr ; SCOPUS_YN:Y ; CONFIRM:Y

The use of DNA molecules as a physical computational material has attracted much interest, especially in the area of DNA computing. DNAs are also useful for logical control and analysis of biological systems if efficient visualization methods are available. Here we present a quick and simple visualization technique that displays the results of the DNA computing process based on a colorimetric change induced by gold nanoparticle aggregation, and we apply it to the logic-based detection of biomolecules. Our results demonstrate its effectiveness in both DNA-based logical computation and logic-based biomolecular detection. ; the Ministry of Commerce, Industry and Energy through the Molecular Evolutionary Computing (MEC) Project the Ministry of Education and Human Resources Development (MOEHRD) under the BK21-IT Program The ICT at Seoul National University provided research facilities a Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion) (KRF-2006-351-C00045)