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Expanding the rule set of DNA circuitry with associative toehold activation.

ABSTRACT: Toehold-mediated strand displacement has proven extremely powerful in programming enzyme-free DNA circuits and DNA nanomachines. To achieve multistep, autonomous, and complex behaviors, toeholds must be initially inactivated by hybridizing to inhibitor strands or domains and then relieved from inactivation in a programmed, timed manner. Although powerful and reasonably robust, this strategy has several drawbacks that limit the architecture of DNA circuits. For example, the combination between toeholds and branch migration (BM) domains is 'hard wired' during DNA synthesis thus cannot be created or changed during the execution of DNA circuits. To solve this problem, I propose a strategy called 'associative toehold activation', where the toeholds and BM domains are connected via hybridization of auxiliary domains during the execution of DNA circuits. Bulged thymidines that stabilize DNA three-way junctions substantially accelerate strand displacement reactions in this scheme, allowing fast strand displacement initiated by reversible toehold binding. To demonstrate the versatility of the scheme, I show (1) run-time combination of toeholds and BM domains, (2) run-time recombination of toeholds and BM domains, which results in a novel operation 'toehold switching', and (3) design of a simple conformational self-replicator.

SUBMITTER: Chen X

PROVIDER: S-EPMC3260326 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Expanding the rule set of DNA circuitry with associative toehold activation.

Chen Xi X

Journal of the American Chemical Society 20111214 1

Toehold-mediated strand displacement has proven extremely powerful in programming enzyme-free DNA circuits and DNA nanomachines. To achieve multistep, autonomous, and complex behaviors, toeholds must be initially inactivated by hybridizing to inhibitor strands or domains and then relieved from inactivation in a programmed, timed manner. Although powerful and reasonably robust, this strategy has several drawbacks that limit the architecture of DNA circuits. For example, the combination between to ...[more]

PMID: 22129141

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Project description:The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.

Dataset Information

Expanding the rule set of DNA circuitry with associative toehold activation.

Publications

Expanding the rule set of DNA circuitry with associative toehold activation.

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