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The programmable and reliable hybridization of DNA strands has enabled the preparation of a wide variety of structures. This Review discusses how, in addition to these static assemblies, the process of displacing — and ultimately replacing — strands also makes possible the construction of dynamic systems such as logic gates or autonomous walkers. The specificity and predictability of Watson–Crick base pairing make DNA a powerful and versatile material for engineering at the nanoscale. This has enabled the construction of a diverse and rapidly growing set of DNA nanostructures and nanodevices through the programmed hybridization of complementary strands. Although it had initially focused on the self-assembly of static structures, DNA nanotechnology is now also becoming increasingly attractive for engineering systems with interesting dynamic properties. Various devices, including circuits, catalytic amplifiers, autonomous molecular motors and reconfigurable nanostructures, have recently been rationally designed to use DNA strand-displacement reactions, in which two strands with partial or full complementarity hybridize, displacing in the process one or more pre-hybridized strands. This mechanism allows for the kinetic control of reaction pathways. Here, we review DNA strand-displacement-based devices, and look at how this relatively simple mechanism can lead to a surprising diversity of dynamic behaviour.

An important goal of synthetic biology is to create biochemical control systems with the desired characteristics from scratch. Srinivas et al. describe the creation of a biochemical oscillator that requires no enzymes or evolved components, but rather is implemented through DNA molecules designed to function in strand displacement cascades. Furthermore, they created a compiler that could translate a formal chemical reaction network into the necessary DNA sequences that could function together to provide a specified dynamic behavior. Science , this issue p. eaal2052 A compiler specifies dynamic response behaviors in DNA strand displacement reactions. Chemistries exhibiting complex dynamics—from inorganic oscillators to gene regulatory networks—have been long known but either cannot be reprogrammed at will or rely on the sophisticated enzyme chemistry underlying the central dogma. Can simpler molecular mechanisms, designed from scratch, exhibit the same range of behaviors? Abstract chemical reaction networks have been proposed as a programming language for complex dynamics, along with their systematic implementation using short synthetic DNA molecules. We developed this technology for dynamical systems by identifying critical design principles and codifying them into a compiler automating the design process. Using this approach, we built an oscillator containing only DNA components, establishing that Watson-Crick base-pairing interactions alone suffice for complex chemical dynamics and that autonomous molecular systems can be designed via molecular programming languages.

Background Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. Results Here, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp’s capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor. Conclusions Fast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines.

A DNA nanodevice performs pathfinding search and computes the solution of two-dimensional DNA origami puzzles.

Cells use spatial constraints to control and accelerate the flow of information in enzyme cascades and signalling networks. Synthetic silicon-based circuitry similarly relies on spatial constraints to process information. Here, we show that spatial organization can be a similarly powerful design principle for overcoming limitations of speed and modularity in engineered molecular circuits. We create logic gates and signal transmission lines by spatially arranging reactive DNA hairpins on a DNA origami. Signal propagation is demonstrated across transmission lines of different lengths and orientations and logic gates are modularly combined into circuits that establish the universality of our approach. Because reactions preferentially occur between neighbours, identical DNA hairpins can be reused across circuits. Co-localization of circuit elements decreases computation time from hours to minutes compared to circuits with diffusible components. Detailed computational models enable predictive circuit design. We anticipate our approach will motivate using spatial constraints for future molecular control circuit designs. Fast and scalable molecular logic circuits can be created through the spatial organization of DNA hairpins on DNA origami scaffolds.

This article reviews recent progress in the development of cellular DNA nanotechnology, highlighting key potential applications such as DNA-based imaging probes, smart therapeutics, and drug delivery systems. The programmability of Watson–Crick base pairing, combined with a decrease in the cost of synthesis, has made DNA a widely used material for the assembly of molecular structures and dynamic molecular devices. Working in cell-free settings, researchers in DNA nanotechnology have been able to scale up system complexity and quantitatively characterize reaction mechanisms to an extent that is infeasible for engineered gene circuits or other cell-based technologies. However, the most intriguing applications of DNA nanotechnology — applications that best take advantage of the small size, biocompatibility and programmability of DNA-based systems — lie at the interface with biology. Here, we review recent progress in the transition of DNA nanotechnology from the test tube to the cell. We highlight key successes in the development of DNA-based imaging probes, prototypes of smart therapeutics and drug delivery systems, and explore the future challenges and opportunities for cellular DNA nanotechnology.

Molecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka-Volterra oscillator, a limit-cycle oscillator, a chaotic system, and systems implementing feedback digital logic and algorithmic behavior.

DNA has recently emerged as an attractive medium for archival data storage. Recent work has demonstrated proof-of-principle prototype systems; however, very uneven (biased) sequencing coverage has been reported, which indicates inefficiencies in the storage process. Deviations from the average coverage in the sequence copy distribution can either cause wasteful provisioning in sequencing or excessive number of missing sequences. Here, we use millions of unique sequences from a DNA-based digital data archival system to study the oligonucleotide copy unevenness problem and show that the two paramount sources of bias are the synthesis and amplification (PCR) processes. Based on these findings, we develop a statistical model for each molecular process as well as the overall process. We further use our model to explore the trade-offs between synthesis bias, storage physical density, logical redundancy, and sequencing redundancy, providing insights for engineering efficient, robust DNA data storage systems. DNA is an attractive digital data storing medium due to high information density and longevity. Here the authors use millions of sequences to investigate inherent biases in DNA synthesis and PCR amplification.

The human neonatal cerebellum is one-fourth of its adult size yet contains the blueprint required to integrate environmental cues with developing motor, cognitive and emotional skills into adulthood. Although mature cerebellar neuroanatomy is well studied, understanding of its developmental origins is limited. In this study, we systematically mapped the molecular, cellular and spatial composition of human fetal cerebellum by combining laser capture microscopy and SPLiT-seq single-nucleus transcriptomics. We profiled functionally distinct regions and gene expression dynamics within cell types and across development. The resulting cell atlas demonstrates that the molecular organization of the cerebellar anlage recapitulates cytoarchitecturally distinct regions and developmentally transient cell types that are distinct from the mouse cerebellum. By mapping genes dominant for pediatric and adult neurological disorders onto our dataset, we identify relevant cell types underlying disease mechanisms. These data provide a resource for probing the cellular basis of human cerebellar development and disease. SPLiT-seq single-nucleus RNA sequencing of the developing human cerebellum reveals cell-type complexities and prolonged maturation compared to mouse with important disease implications.

As global demand for digital storage capacity grows, storage technologies based on synthetic DNA have emerged as a dense and durable alternative to traditional media. Existing approaches leverage robust error correcting codes and precise molecular mechanisms to reliably retrieve specific files from large databases. Typically, files are retrieved using a pre-specified key, analogous to a filename. However, these approaches lack the ability to perform more complex computations over the stored data, such as similarity search: e.g., finding images that look similar to an image of interest without prior knowledge of their file names. Here we demonstrate a technique for executing similarity search over a DNA-based database of 1.6 million images. Queries are implemented as hybridization probes, and a key step in our approach was to learn an image-to-sequence encoding ensuring that queries preferentially bind to targets representing visually similar images. Experimental results show that our molecular implementation performs comparably to state-of-the-art in silico algorithms for similarity search. Storage technology based on DNA is emerging as an information dense and durable medium. Here the authors use machine learning-based encoding and hybridization probes to execute similarity searches in a DNA database.