A nonlinear neural network based on an analog DNA toehold mediated strand displacement reaction circuit

Zou, Chengye; Zhang, Qiang; Zhou, Changjun; Cao, Wenyu

doi:10.1039/d1nr06861j

Cited by 14 publications

(7 citation statements)

References 39 publications

(79 reference statements)

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“…Recent advances in DNA nanotechnology have enabled methodologies to explore the operating principles of biochemical reaction systems with artificially synthesized nucleic acids to construct specific biomolecular reaction networks . Systems with plastic adaptation have memory and learning capabilities and have been actively studied in the field of DNA computing over the past decade, − as pioneered by Qian et al These studies focus primarily on implementing well-established machine learning algorithms, such as neural networks, on DNA reaction systems (hereafter, DNA circuits), while addressing basic learning at the simulation level . In contrast, an operating principle that can alter the circuit functions depending on the history (stimulus level and timing) of the input stimuli while considering the dynamic properties of DNA circuits has been proposed, , implying further possibilities to extend the capability of biochemical reaction systems. − However, adaption to DNA circuits with memory and learning capabilities by exploiting the dynamic properties of biochemical reactions remains challenging …”

Section: Introductionmentioning

confidence: 99%

DNA Reaction System That Acquires Classical Conditioning

Nakakuki,

Toyonari,

Aso

et al. 2024

ACS Synth. Biol.

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Biochemical reaction networks can exhibit plastic adaptation to alter their functions in response to environmental changes. This capability is derived from the structure and dynamics of the reaction networks and the functionality of the biomolecule. This plastic adaptation in biochemical reaction systems is essentially related to memory and learning capabilities, which have been studied in DNA computing applications for the past decade. However, designing DNA reaction systems with memory and learning capabilities using the dynamic properties of biochemical reactions remains challenging. In this study, we propose a basic DNA reaction system design that acquires classical conditioning, a phenomenon underlying memory and learning, as a typical learning task. Our design is based on a simple mechanism of five DNA strand displacement reactions and two degradative reactions. The proposed DNA circuit can acquire or lose a new function under specific conditions, depending on the input history formed by repetitive stimuli, by exploiting the dynamic properties of biochemical reactions induced by different input timings.

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Section: Introductionmentioning

confidence: 99%

DNA Reaction System That Acquires Classical Conditioning

Nakakuki,

Toyonari,

Aso

et al. 2024

ACS Synth. Biol.

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“…Using programmable artificial molecular reaction networks, the reaction paths of complex networks can be effectively designed [ 3 ], and the transformation of information can be controlled, which enables these networks to perform their target functions. Programmable artificial molecular reaction networks are widely used in molecular self-assembly [ 4 , 5 ], disease diagnosis and treatment [ 6 , 7 , 8 ], and neural networks [ 9 , 10 ]. Therefore, it is necessary to design and study programmable artificial molecular reaction networks.…”

Section: Introductionmentioning

confidence: 99%

Programming DNA Reaction Networks Using Allosteric DNA Hairpins

et al. 2023

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The construction of DNA reaction networks with complex functions using various methods has been an important research topic in recent years. Whether the DNA reaction network can perform complex tasks and be recycled directly affects the performance of the reaction network. Therefore, it is very important to design and implement a DNA reaction network capable of multiple tasks and reversible regulation. In this paper, the hairpin allosteric method was used to complete the assembly task of different functional nucleic acids. In addition, information conversion of the network was realized. In this network, multiple hairpins were assembled into nucleic acid structures with different functions to achieve different output information through the cyclic use of trigger strands. A method of single-input dual-output information conversion was proposed. Finally, the network with signal amplification and reversible regulation was constructed. In this study, the reversible regulation of different functional nucleic acids in the same network was realized, which shows the potential of this network in terms of programmability and provides new ideas for constructing complex and multifunctional DNA reaction networks.

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“…DNA is regarded as an ideal material for synthetic molecular systems. 1,2 Based on the principle of complementary base pairing, DNA molecules are being used in frontier areas such as neural networks, 3,4 information encryption 5,6 disease detection 7,8 and DNA storage. [9][10][11] With great potential for information transfer and processing, DNA molecules enable molecular logic circuits, [12][13][14] tiles, [15][16][17] walker machines, [18][19][20] and protein interaction.…”

Section: Introductionmentioning

confidence: 99%