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Heat recharges enzyme-free DNA circuits, enabling complex logic operations and neural networks to perform multiple computations, offering a universal energy source for molecular machines and advancing autonomous behaviours in artificial chemical systems.

DNA molecules can be programmed to autonomously carry out supervised learning in vitro, with the system learning to perform pattern classification from molecular examples of inputs and desired responses.

Current DNA computational systems are constrained by integration efficiency, device structures and limited functions. Here the authors design a DNA arithmetic logic unit that uses polymerase-mediated strand displacement.

DNA strand displacement reactions can be difficult to scale up for computational tasks. Here the authors develop DNA switching circuits that achieve high-speed computing with fewer molecules.

A new DNA logic circuits architecture based on single-stranded logic gates and strand-displacing DNA polymerase requires less computation time and fewer DNA strands.

A cascade reaction network has been created that can function in a manner that is superficially similar to the most basic steps of the vertebrate adaptive immune response. This reaction network uses DNA and enzymes as simple artificial analogues of the components of the acquired immune system.

Semipermeable proteinosome membranes allow complex DNA message communication through compartmentalization and protect the DNA circuits from degradation in a biological environment.

Adding nanobodies into DNA computing has proven difficult due to there always on state. Here, the authors propose a spatial segregation-based molecular computing strategy to program nanobodies into DNA molecular computation and elucidate the kinetic mechanism of microenvironment-confined DNA molecular computation.

Molecular computing is an emerging paradigm with a crucial role in clinical diagnosis. Here, authors develop a spatially localized, DNA-integrated circuits-based classifier, DNA IC-CLA, which enables accurate cancer diagnosis for clinical samples in a faster and more effective manner.

We present a DNA self-assembly based molecular data writing strategy to enable parallel movable-type printing for scalable DNA storage.

Various methods, using DNA, have been reported for the recording of biomolecular interactions, but most are either destructive in nature or are limited to reporting pairwise interactions. Here the authors develop DNA-based motors, termed ‘crawlers’, that roam around and record their trajectories to allow the examination of molecular environments.

A DNA-based computational platform can construct a universal set of logic gates and perform addition/subtraction operations in parallel, as well as activating multilayered gate cascades and fan-out gates, in a single test tube.

DNA nanotubes can grow to connect two molecules attached to surfaces with arbitrary micron-scale separation.

Nanoscale robots made from DNA origami can dynamically interact with each other and perform logic computations in a living animal.

Nucleic acid catalysts can be used to build a multipurpose molecular automaton that can be programmed by example.

Here, the authors present a data storage and computation engine comprised of DNA adsorbed to soft dendricolloids, demonstrating end-to-end capabilities from archival storage to non-destructive file access for reading, erasing, rewriting and computing.

The heterogeneous and compartmentalized environments within living cells make it difficult to deploy theranostic agents with precise spatiotemporal accuracy. Zhao et al. demonstrate a DNA framework state machine that can switch among multiple structural states according to the temporal sequence of molecular cues, enabling temporally controlled CRISPR–Cas9 targeting in living mammalian cells.

Conformational cooperativity is a universal molecular effect mechanism and plays a critical role in signalling pathways. Here the authors present a programmable conformational cooperativity strategy to construct the oligo-protein signal transduction platform for logic operations and gene regulations which can be cooperatively regulated by conformational signals.

Artificial DNA circuits that can perform neural network-like computations have been developed, but scaling up these circuits to recognize a large number of patterns is a challenging task. Xiong, Zhu and colleagues experimentally demonstrate a convolutional neural network algorithm using a synthetic DNA-based regulatory circuit in vitro and develop a freeze–thaw approach to reduce the computation time from hours to minutes, paving the way towards more powerful biomolecular classifiers.

The design of nucleic acid hybridisation probes is important for their use in DNA nanotechnology and biomedical applications. Here the authors use a DNA equalizer gate approach that expands the detection windows for improved sequence selectivity.

Synthetic DNA constructs can to used to recognise and respond to input signals. Here the authors present complex DNA nanostructures with toehold-free strand displacement for generation of ON/OFF switches and Boolean gates.

Generic single-stranded oligonucleotides used as a uniform transmission signal can reliably integrate large-scale DNA integrated circuits with minimal leakage and high fidelity for general-purpose computing.

Cas12a is a useful alternative to Cas9 for genome editing and regulation. Here the authors design strand displacement gRNAs that can add functionality to Cas12a by acting as multi-input logic gates.

Gene expression profiling remains cost-prohibitive and challenging to implement in a clinical setting. Now, a molecular computation strategy for classifying complex gene expression signatures has been developed. Classification occurs through a series of molecular interactions between RNA inputs and engineered DNA probes designed to implement a relevant linear classification model.

DNA computing takes advantage of DNA molecular interactions to achieve information processing for liquid-phase computing. This Review discusses designing rules, implementation strategies and biomedical applications of DNA computing circuits.

 Mimicking traditional digital neural networks with DNA-encoded ‘enzymatic’ neurons overcomes issues with other chemical approaches, and could allow notable increases in miniaturization and molecular implementation of these AI models, with potential applications including DNA data storage or cancer diagnosis.

By adapting DNA strand displacement and exchange reactions to mammalian cells, DNA circuitry is developed that can directly interact with a native mRNA.

Existing DNA based circuits, designed to perform logic operations and signal processing, are generally responsive to DNA or RNA inputs. Here, the authors show that antibodies can actuate DNA logic gates, opening the way to applications of DNA computing in diagnostics and biomedicine.

DNA circuits hold promise for advancing information-based molecular technologies, yet it is challenging to design and construct them in practice. Thubagereet al. build DNA strand displacement circuits using unpurified strands whose sequences are automatically generated from a user-friendly compiler.

Examination of nucleation during self-assembly of multicomponent structures illustrates how ubiquitous molecular phenomena inherently classify high-dimensional patterns of concentrations in a manner similar to neural network computation.

Synthetic chemical networks with far-from-equilibrium dynamics akin to genetic regulatory networks in living cells could precisely regulate the kinetics of chemical synthesis or self-assembly. Now standardized excitable chemical regulatory elements, termed genelets, that enable predictive bottom-up construction of in vitro networks with designed temporal and multistable behaviour have been developed.

Gaining control over crystallization processes is challenging. Herein, the authors describe a protocol for the controlled growth of DNA nanotubes by feedback regulation: the coupling of a reversible bimolecular monomer buffering reaction delivers the optimal monomer concentration and leads to reliable crystal growth in a simple manner.

Toward more storage for less synthesis using a six-letter composite DNA alphabet.

Synthetic gene circuits encapsulated in lipid membrane compartments are often employed as artificial cell mimics, but these lack the complex behaviour of biological tissues. Now, spatial information based on chemical gradients has been used to engineer non-trivial dynamics such as signal propagation and differentiation in an artificial multicellular system.

DNA based technology holds promise for non-volatile memory and computational tasks, yet the relatively slow hybridization kinetics remain a bottleneck. Here, Song et al. have developed an electric field-induced hybridization platform that can speed up multi-bit memory and logic operations.

Biomolecular cyptography that exploits specific interactions could be used for data encryption. Here the authors use the folding of M13 DNA to encrypt information for secure communication.

DNA-based nanodevices with decision-making and information-processing capabilities have been developed. Here, a DNA-based molecular navigation system that explores a DNA origami maze for all possible solutions is reported.

Fast and scalable molecular logic circuits can be created through the spatial organization of DNA hairpins on DNA origami scaffolds.

Chemical controllers made from DNA can be programmed to implement any dynamic behaviour compatible with chemical kinetics.

Compartmentalization of complex chemical networks is an essential step towards the creation of cell-scale molecular systems. The encapsulation of a synthetic biochemical oscillating reaction system into cell-sized emulsion droplets is now demonstrated; a large variability in its oscillatory dynamics is observed, which is attributed to partitioning effects.

The programmable nature of chemical reactions enables the creation of complex networks; however, it can be difficult to redesign the underlying reactions. Here, systematic and quantitative control over the diffusivity and reactivity of DNA molecules yields highly programmable chemical reaction networks that execute macroscale pattern transformation algorithms, such as edge detection.

Dynamic nonlinear biochemical circuits are functionally rich; however, this nonlinear nature also makes programming them delicate and painstaking. Now a droplet microfluidic platform reveals precisely the bifurcations of two canonical systems: a bistable switch and a predator–prey oscillator, exposing optimal regions and mechanistic insights that inform the design of these systems.

Programming stochastic self-assembly of DNA origami tiles to create complex patterns with controlled properties.

The regulation of cellular response to stimuli by genetic regulatory networks (GRNs) suggests how in vitro chemical reaction networks might be used to direct the dynamics of synthetic materials or chemical reactions. Now, multiple functional in vitro transcriptional circuit modules have been integrated to form composite regulatory networks capable of complex features analogous to those found in cellular GRNs.

Individual particles can be programmed to chemically communicate with their neighbours, giving rise to collective behaviours such as dynamic travelling fronts and spatial pattern creation.

Primer exchange reaction (PER) cascades have now been used to grow nascent single-stranded DNA with user-specified sequences following prescribed reaction pathways. PER synthesis occurs in a programmable, autonomous, in situ and environmentally responsive fashion, providing a platform for engineering molecular circuits and devices with a wide range of sensing, monitoring, recording, signal processing and actuation capabilities.

A set of 355 self-assembling DNA ‘tiles’ can be reprogrammed to implement many different computer algorithms—including sorting, palindrome testing and divisibility by three—suggesting that molecular self-assembly could be a reliable algorithmic component in programmable chemical systems.

DNA-strand-displacement reactions are used to implement a neural network that can distinguish complex and noisy molecular patterns from a set of nine possibilities—an improvement on previous demonstrations that distinguished only four simple patterns.