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Chemical-to-mechanical molecular computation using DNA-based motors with onboard logic.

ABSTRACT: DNA has become the biomolecule of choice for molecular computation that may one day complement conventional silicon-based processors. In general, DNA computation is conducted in individual tubes, is slow in generating chemical outputs in response to chemical inputs and requires fluorescence readout. Here, we introduce a new paradigm for DNA computation where the chemical input is processed and transduced into a mechanical output using dynamic DNA-based motors operating far from equilibrium. We show that DNA-based motors with onboard logic (DMOLs) can perform Boolean functions (NOT, YES, AND and OR) with 15 min readout times. Because DMOLs are micrometre-sized, massive arrays of DMOLs that are identical or uniquely encoded by size and refractive index can be multiplexed and perform motor-to-motor communication on the same chip. Finally, DMOL computational outputs can be detected using a conventional smartphone camera, thus transducing chemical information into the electronic domain in a facile manner, suggesting potential applications.

SUBMITTER: Piranej S

PROVIDER: S-EPMC9119907 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Chemical-to-mechanical molecular computation using DNA-based motors with onboard logic.

Piranej Selma S Bazrafshan Alisina A Salaita Khalid K

Nature nanotechnology 20220328 5

DNA has become the biomolecule of choice for molecular computation that may one day complement conventional silicon-based processors. In general, DNA computation is conducted in individual tubes, is slow in generating chemical outputs in response to chemical inputs and requires fluorescence readout. Here, we introduce a new paradigm for DNA computation where the chemical input is processed and transduced into a mechanical output using dynamic DNA-based motors operating far from equilibrium. We s ...[more]

PMID: 35347272

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Project description:There is considerable scientific interest in understanding how cell assemblies-the long-presumed computational motif-are organized so that the brain can generate intelligent cognition and flexible behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N = 2 i -1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information. However, modulatory neurons, such as dopaminergic (DA) neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact although NMDA receptors-the synaptic switch for learning and memory-were deleted throughout adulthood, suggesting that the logic is developmentally pre-configured. Moreover, this computational logic is implemented in the cortex via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques-which preferentially encode specific and low-combinatorial features and project inter-cortically-is ideal for maximizing cross-modality novel pattern-extraction, pattern-discrimination and pattern-categorization using sparse code, consequently explaining why it requires hippocampal offline-consolidation. In contrast, the nonrandomness in layers 5/6-which consists of few specific cliques but a higher portion of more general cliques projecting mostly to subcortical systems-is ideal for feedback-control of motivation, emotion, consciousness and behaviors. These observations suggest that the brain's basic computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.

Dataset Information

Chemical-to-mechanical molecular computation using DNA-based motors with onboard logic.

Publications

Chemical-to-mechanical molecular computation using DNA-based motors with onboard logic.

Similar Datasets

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