Using Strand Displacing Polymerase To Program Chemical Reaction Networks.

Chemical reaction networks (CRNs) provide a powerful abstraction to formally represent complex biochemical processes. DNA provides a promising substrate to implement the abstract representation (or programming language) of CRNs due to its programmable nature. Prior works that used DNA to implement CRNs either used DNA-only systems or multienzyme DNA circuits. Architectures with DNA-only components had the rationale of being biologically simple systems. Multienzyme systems, on the other hand, aimed at using natural enzymes to improve circuit performance, although, at the cost of increased complexity. In this work, we explore an alternative architecture that lies along the spectrum in between DNA-only systems and multienzyme DNA systems. Our architecture relies on only a strand displacing polymerase enzyme and DNA hybridization reactions for implementing CRNs. First, we briefly introduce the theory and DNA design of simple CRNs and then explore the fundamental properties of polymerase-based strand displacement systems. Finally, we engineer a catalytic amplifier in vitro as a use-case of our framework since such amplifiers require the intricate design of DNA sequences and reaction conditions.

Physical Laboratory Automation in Synthetic Biology.

Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.

What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization.

Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address this, recent work has added a calibration step, which exposes a model to its own ranked outputs to improve relevance or, in a separate line of work, contrasts positive and negative sets to improve faithfulness. While effective, much of this work has focused on how to generate and optimize these sets. Less is known about why one setup is more effective than another. In this work, we uncover the underlying characteristics of effective sets. For each training instance, we form a large, diverse pool of candidates and systematically vary the subsets used for calibration fine-tuning. Each selection strategy targets distinct aspects of the sets, such as lexical diversity or the size of the gap between positive and negatives. On three diverse scientific long-form summarization datasets (spanning biomedical, clinical, and chemical domains), we find, among others, that faithfulness calibration is optimal when the negative sets are extractive and more likely to be generated, whereas for relevance calibration, the metric margin between candidates should be maximized and surprise-the disagreement between model and metric defined candidate rankings-minimized. Code to create, select, and optimize calibration sets is available at https://github.com/griff4692/calibrating-summaries.

DNA storage in thermoresponsive microcapsules for repeated random multiplexed data access.

DNA has emerged as an attractive medium for archival data storage due to its durability and high information density. Scalable parallel random access to information is a desirable property of any storage system. For DNA-based storage systems, however, this still needs to be robustly established. Here we report on a thermoconfined polymerase chain reaction, which enables multiplexed, repeated random access to compartmentalized DNA files. The strategy is based on localizing biotin-functionalized oligonucleotides inside thermoresponsive, semipermeable microcapsules. At low temperatures, microcapsules are permeable to enzymes, primers and amplified products, whereas at high temperatures, membrane collapse prevents molecular crosstalk during amplification. Our data show that the platform outperforms non-compartmentalized DNA storage compared with repeated random access and reduces amplification bias tenfold during multiplex polymerase chain reaction. Using fluorescent sorting, we also demonstrate sample pooling and data retrieval by microcapsule barcoding. Therefore, the thermoresponsive microcapsule technology offers a scalable, sequence-agnostic approach for repeated random access to archival DNA files.

Combinatorial PCR Method for Efficient, Selective Oligo Retrieval from Complex Oligo Pools.

With the rapidly decreasing cost of array-based oligo synthesis, large-scale oligo pools offer significant benefits for advanced applications including gene synthesis, CRISPR-based gene editing, and DNA data storage. The selective retrieval of specific oligos from these complex pools traditionally uses polymerase chain reaction (PCR). Designing a large number of primers to use in PCR presents a serious challenge, particularly for DNA data storage, where the size of an oligo pool is orders of magnitude larger than other applications. Although a nested primer address system was recently developed to increase the number of accessible files for DNA storage, it requires more complicated lab protocols and more expensive reagents to achieve high specificity, as well as more DNA address space. Here, we present a new combinatorial PCR method that has none of those drawbacks and outperforms in retrieval specificity. In experiments, we accessed three files that each comprised 1% of a DNA prototype database that contained 81 different files and enriched them to over 99.9% using our combinatorial primer method. Our method provides a viable path for scaling up DNA data storage systems and has broader utility whenever one must access a specific target oligo and can design their own primer regions.

Synthetic DNA applications in information technology.

Synthetic DNA is a growing alternative to electronic-based technologies in fields such as data storage, product tagging, or signal processing. Its value lies in its characteristic attributes, namely Watson-Crick base pairing, array synthesis, sequencing, toehold displacement and polymerase chain reaction (PCR) capabilities. In this review, we provide an overview of the most prevalent applications of synthetic DNA that could shape the future of information technology. We emphasize the reasons why the biomolecule can be a valuable alternative for conventional electronic-based media, and give insights on where the DNA-analog technology stands with respect to its electronic counterparts.

A nanopore interface for higher bandwidth DNA computing.

DNA has emerged as a powerful substrate for programming information processing machines at the nanoscale. Among the DNA computing primitives used today, DNA strand displacement (DSD) is arguably the most popular, with DSD-based circuit applications ranging from disease diagnostics to molecular artificial neural networks. The outputs of DSD circuits are generally read using fluorescence spectroscopy. However, due to the spectral overlap of typical small-molecule fluorescent reporters, the number of unique outputs that can be detected in parallel is limited, requiring complex optical setups or spatial isolation of reactions to make output bandwidths scalable. Here, we present a multiplexable sequencing-free readout method that enables real-time, kinetic measurement of DSD circuit activity through highly parallel, direct detection of barcoded output strands using nanopore sensor array technology (Oxford Nanopore Technologies' MinION device). These results increase DSD output bandwidth by an order of magnitude over what is currently feasible with fluorescence spectroscopy.

Molecular-level similarity search brings computing to DNA data storage.

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.

A deep learning model for predicting next-generation sequencing depth from DNA sequence.

Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.

Author Correction: Probing the physical limits of reliable DNA data retrieval.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Probing the physical limits of reliable DNA data retrieval.

Synthetic DNA is gaining momentum as a potential storage medium for archival data storage. In this process, digital information is translated into sequences of nucleotides and the resulting synthetic DNA strands are then stored for later retrieval. Here, we demonstrate reliable file recovery with PCR-based random access when as few as ten copies per sequence are stored, on average. This results in density of about 17 exabytes/gram, nearly two orders of magnitude greater than prior work has shown. We successfully retrieve the same data in a complex pool of over 1010 unique sequences per microliter with no evidence that we have begun to approach complexity limits. Finally, we also investigate the effects of file size and sequencing coverage on successful file retrieval and look for systematic DNA strand drop out. These findings substantiate the robustness and high data density of the process examined here.

Quantifying molecular bias in DNA data storage.

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 assembly for nanopore data storage readout.

Synthetic DNA is becoming an attractive substrate for digital data storage due to its density, durability, and relevance in biological research. A major challenge in making DNA data storage a reality is that reading DNA back into data using sequencing by synthesis remains a laborious, slow and expensive process. Here, we demonstrate successful decoding of 1.67 megabytes of information stored in short fragments of synthetic DNA using a portable nanopore sequencing platform. We design and validate an assembly strategy for DNA storage that drastically increases the throughput of nanopore sequencing. Importantly, this assembly strategy is generalizable to any application that requires nanopore sequencing of small DNA amplicons.

Nucleic Acid Strand Displacement with Synthetic mRNA Inputs in Living Mammalian Cells.

Strand displacement reactions are widely used in DNA nanotechnology as a building block for engineering molecular computers and machines. Here, we demonstrate that strand displacement-based probes can be triggered by RNA expressed in mammalian cells, thus taking a step toward adapting the DNA nanotechnology toolbox to a cellular environment. We systematically compare different probe architectures in order to identify a design that works robustly in living cells. Our optimized strand displacement probe combines chemically modified nucleic acids that enhance stability to degradation by cellular nucleases with structural elements that improve probe retention in the cytoplasm. We visualize probe binding to individual mRNA carrying 96 repeats of a target sequence in the 3'UTR. We find that RNA counts based on live cell imaging using a strand displacement probe are comparable to counts from independent measurement based on fluorescence in situ hybridization experiments. We used probes with scrambled toeholds and scrambled binding domains to demonstrate that target recognition indeed occurs through toehold-mediated strand displacement. Our results demonstrate that strand displacement probes can work reliably in mammalian cells and lay the groundwork for future applications of such probes for live-cell imaging and molecular computing.

Random access in large-scale DNA data storage.

Synthetic DNA is durable and can encode digital data with high density, making it an attractive medium for data storage. However, recovering stored data on a large-scale currently requires all the DNA in a pool to be sequenced, even if only a subset of the information needs to be extracted. Here, we encode and store 35 distinct files (over 200 MB of data), in more than 13 million DNA oligonucleotides, and show that we can recover each file individually and with no errors, using a random access approach. We design and validate a large library of primers that enable individual recovery of all files stored within the DNA. We also develop an algorithm that greatly reduces the sequencing read coverage required for error-free decoding by maximizing information from all sequence reads. These advances demonstrate a viable, large-scale system for DNA data storage and retrieval.

Computing in mammalian cells with nucleic acid strand exchange.

DNA strand displacement has been widely used for the design of molecular circuits, motors, and sensors in cell-free settings. Recently, it has been shown that this technology can also operate in biological environments, but capabilities remain limited. Here, we look to adapt strand displacement and exchange reactions to mammalian cells and report DNA circuitry that can directly interact with a native mRNA. We began by optimizing the cellular performance of fluorescent reporters based on four-way strand exchange reactions and identified robust design principles by systematically varying the molecular structure, chemistry and delivery method. Next, we developed and tested AND and OR logic gates based on four-way strand exchange, demonstrating the feasibility of multi-input logic. Finally, we established that functional siRNA could be activated through strand exchange, and used native mRNA as programmable scaffolds for co-localizing gates and visualizing their operation with subcellular resolution.

Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks.

DNA nanotechnology requires large amounts of highly pure DNA as an engineering material. Plasmid DNA could meet this need since it is replicated with high fidelity, is readily amplified through bacterial culture and can be stored indefinitely in the form of bacterial glycerol stocks. However, the double-stranded nature of plasmid DNA has so far hindered its efficient use for construction of DNA nanostructures or devices that typically contain single-stranded or branched domains. In recent work, it was found that nicked double stranded DNA (ndsDNA) strand displacement gates could be sourced from plasmid DNA. The following is a protocol that details how these ndsDNA gates can be efficiently encoded in plasmids and can be derived from the plasmids through a small number of enzymatic processing steps. Also given is a protocol for testing ndsDNA gates using fluorescence kinetics measurements. NdsDNA gates can be used to implement arbitrary chemical reaction networks (CRNs) and thus provide a pathway towards the use of the CRN formalism as a prescriptive molecular programming language. To demonstrate this technology, a multi-step reaction cascade with catalytic kinetics is constructed. Further it is shown that plasmid-derived components perform better than identical components assembled from synthetic DNA.

DNA nanotechnology from the test tube to the cell.

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.

Programmable chemical controllers made from DNA.

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.