A simple DNA gate motif for synthesizing large-scale circuits

SemiSynBio: A new era for neuromorphic computing

Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with the rise of synthetic biology, becoming the driving force for new generation semiconductor synthetic biology (SemiSynBio) technologies. DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks (ANNs), providing the possibility of executing neuromorphic computing at the molecular level. Herein, we briefly outline the principles of neuromorphic computing, describe the advances in DNA computing with a focus on synthetic neuromorphic computing, and summarize the major challenges and prospects for synthetic neuromorphic computing. We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing, which would be of widespread use in biocomputing, DNA storage, information security, and national defense.

Kinetic Proofreading Can Enhance Specificity in a Nonenzymatic DNA Strand Displacement Network

Kinetic proofreading is used throughout natural systems to enhance the specificity of molecular recognition. At its most basic level, kinetic proofreading uses a supply of chemical fuel to drive a recognition interaction out of equilibrium, allowing a single free-energy difference between correct and incorrect targets to be exploited two or more times. Despite its importance in biology, there has been little effort to incorporate kinetic proofreading into synthetic systems in which molecular recognition is important, such as nucleic acid nanotechnology. In this article, we introduce a DNA strand displacement-based kinetic proofreading motif, showing that the consumption of a DNA-based fuel can be used to enhance molecular recognition during a templated dimerization reaction. We then show that kinetic proofreading can enhance the specificity with which a probe discriminates single nucleotide mutations, both in terms of the initial rate with which the probe reacts and the long-time behavior.

Cascadable-Controllable Self-Assembly DNA Tiles for Large-Scale DNA Logic Circuits

In the last few decades, DNA-based self-assembly tiles has become a hot field in research due to its special applications and advantages. The regularity and strong design methods comprise other DNA-based digital circuit design methods. In addition to the obvious advantages of this method, there are challenges in performing computations based on self-assembly tiles, which have hindered the development and construction of large computing circuits with this method. The first challenge is the creation of crystals from DNA molecules in the output, which has led to the impossibility of cascading. The second challenge of this method is the uncontrollability of the reactions of the tiles, which increases the percentage of computing errors. In this article, these two challenges have been solved by changing the structure of leading tiles so that without the activator strand, tiles remain inactive and cannot be connected to other tiles. Also, when the tiles are activated, single-strand DNA will be released after connecting to other tiles, which will be used as the output of the circuit. This output gives the possibility of cascading to self-assembly designed circuits. The method introduced in this article can be a beginning for the re-development of DNA-based circuit design with the self-assembly tile method.

Engineering a cell-free biosensor signal amplification circuit with polymerase strand recycling

Cell-free systems are powerful synthetic biology technologies because of their ability to recapitulate sensing and gene expression without the complications of living cells. Cell-free systems can perform even more advanced functions when genetic circuits are incorporated as information processing components. Here we expand cell-free biosensing by engineering a highly specific isothermal signal amplification circuit called polymerase strand recycling (PSR) that leverages T7 RNA polymerase off-target transcription to recycle nucleic acid inputs within DNA strand displacement circuits. We develop design rules for PSR circuit components and use these rules to construct modular biosensors that can directly sense different RNA targets with limits of detection in the nM range and high specificity. We then use PSR for signal amplification within allosteric transcription factor-based biosensors for small molecule detection. We use a double equilibrium model of transcription factor:DNA and transcription factor:ligand binding interactions to predict biosensor sensitivity enhancement by PSR, and then demonstrate this approach experimentally by achieving 3.6-4.6-fold decreases in biosensor EC50 to sub micromolar ranges. We believe this work expands the current capabilities of cell-free circuits by incorporating PSR, which we anticipate will have a wide range of uses within biotechnology.

DNA Logic Gates Integrated on DNA Substrates in Molecular Computing

Due to nucleic acid's programmability, it is possible to realize DNA structures with computing functions, and thus a new generation of molecular computers is evolving to solve biological and medical problems. Pioneered by Milan Stojanovic, Boolean DNA logic gates created the foundation for the development of DNA computers. Similar to electronic computers, the field is evolving towards integrating DNA logic gates and circuits by positioning them on substrates to increase circuit density and minimize gate distance and undesired crosstalk. In this minireview, we summarize recent developments in the integration of DNA logic gates into circuits localized on DNA substrates. This approach of all-DNA integrated circuits (DNA ICs) offers the advantages of biocompatibility, increased circuit response, increased circuit density, reduced unit concentration, facilitated circuit isolation, and facilitated cell uptake. DNA ICs can face similar challenges as their equivalent circuits operating in bulk solution (bulk circuits), and new physical challenges inherent in spatial localization. We discuss possible avenues to overcome these obstacles.

Amplification of Signal on Cell Surfaces in Molecular Cascades

We can formulate mixtures of oligonucleotide-antibody conjugates to act as molecular cascade-based automata that analyze pairs of cell surface markers (CD markers) on individual cells in a manner consistent with the implementation of Boolean logic-for example, by producing a fluorescent label only if two markers are present. While traditional methods to characterize cells are based on transducing signals from individual cell surface markers, these cascades can be used to combine into a single signal the presence of two or even more CDs. In our original design, oligonucleotide components irreversibly flowed from one antibody to another, driven by increased hybridizations, leading to the magnitude of the final signal on each cell being determined by the surface marker that was the least abundant. This is a significant limitation to the precise labeling of narrow subpopulations, and, in order to overcome it, we changed our design to accomplish signal amplification to a more abundant cell surface marker. We show the AMPLIFY function on two examples: (1) we amplify the fluorescent label from the CD19 marker onto a fivefold more abundant CD45, and (2) we amplify broadly distributed CD45RA to a more constant marker, CD3. We expect this new function to enable the increasingly complex Boolean analysis of cell surfaces.

Directing Uphill Strand Displacement with an Engineered Superhelicase

The ability to finely tune reaction rates and binding energies between components has made DNA strand displacement circuits promising candidates to replicate the complex regulatory functions of biological reaction networks. However, these circuits often lack crucial properties, such as signal turnover and the ability to transiently respond to successive input signals that require the continuous input of chemical energy. Here, we introduce a method for providing such energy to strand displacement networks in a controlled fashion: an engineered DNA helicase, Rep-X, that transiently dehybridizes specific DNA complexes, enabling the strands in the complex to participate in downstream hybridization or strand displacement reactions. We demonstrate how this process can direct the formation of specific metastable structures by design and that this dehybridization process can be controlled by DNA strand displacement reactions that effectively protect and deprotect a double-stranded complex from unwinding by Rep-X. These findings can guide the design of active DNA strand displacement regulatory networks, in which sustained dynamical behavior is fueled by helicase-regulated unwinding.

Three-input logic gate based on DNA strand displacement reaction

In this paper, three kinds of three-input logic gates are designed based on DNA strand displacement reaction, which are three-input OR logic gate, three-input AND logic gate, and three-input MAJORITY logic gate. The logic gates designed in this paper takes different DNA strands as input and fluorescence signals as output. The biochemical experimental results verify my designs. The results show that DNA strand displacement technology has important application value in DNA computing, especially in the construction of DNA molecular logic gates.

Design and Simulation of a Multilayer Chemical Neural Network That Learns via Backpropagation

The design and implementation of adaptive chemical reaction networks, capable of adjusting their behavior over time in response to experience, is a key goal for the fields of molecular computing and DNA nanotechnology. Mainstream machine learning research offers powerful tools for implementing learning behavior that could one day be realized in a wet chemistry system. Here we develop an abstract chemical reaction network model that implements the backpropagation learning algorithm for a feedforward neural network whose nodes employ the nonlinear "leaky rectified linear unit" transfer function. Our network directly implements the mathematics behind this well-studied learning algorithm, and we demonstrate its capabilities by training the system to learn a linearly inseparable decision surface, specifically, the XOR logic function. We show that this simulation quantitatively follows the definition of the underlying algorithm. To implement this system, we also report ProBioSim, a simulator that enables arbitrary training protocols for simulated chemical reaction networks to be straightforwardly defined using constructs from the host programming language. This work thus provides new insight into the capabilities of learning chemical reaction networks and also develops new computational tools to simulate their behavior, which could be applied in the design and implementations of adaptive artificial life.

A survey on molecular-scale learning systems with relevance to DNA computing

DNA computing has emerged as a promising alternative to achieve programmable behaviors in chemistry by repurposing the nucleic acid molecules into chemical hardware upon which synthetic chemical programs can be executed. These chemical programs are capable of simulating diverse behaviors, including boolean logic computation, oscillations, and nanorobotics. Chemical environments such as the cell are marked by uncertainty and are prone to random fluctuations. For this reason, potential DNA-based molecular devices that aim to be deployed into such environments should be capable of adapting to the stochasticity inherent in them. In keeping with this goal, a new subfield has emerged within DNA computing, focusing on developing approaches that embed learning and inference into chemical reaction systems. If realized in biochemical contexts, such molecular machines can engender novel applications in fields such as biotechnology, synthetic biology, and medicine. Therefore, it would be beneficial to review how different ideas were conceived, how the progress has been so far, and what the emerging ideas are in this nascent field of 'molecular-scale learning'.

A multifunctional monolithic interfacial sensor based on gold nanoparticle

The spatial and temporal uneven distribution of complex biochemical reactions creates the diversity of biological systems. And the microenvironment confers fine regulation of these reactions, a stunning example of which is liquid-liquid phase separation (LLPS). LLPS can form a separate compartment without the physical separation formed by conventional membrane structures, and the reactions within the interface have specific reaction dynamics. Inspired by this, we report an interfacial sensor based on gold nanoparticles showing that interfacial factors have similar properties operating in natural biological environments and sensors. It repels molecules outside the interface and adjusts the DNA conformation within the interface to produce unique dynamics. The sensor adopts a modular design, allowing functional modules assembled on a single nanoparticle to avoid complex designs. We demonstrate the functionality of logical operations, using apurinic/apyrimidinic endonuclease 1 and micro RNA as inputs, showing that the sensor has the ability and potential to become a multifunctional platform with clear interface nature.

Programming Non-Nucleic Acid Molecules into Computational Nucleic Acid Systems

Nucleic acid (NA) computation has been widely developed in the past years to solve kinds of logic and mathematic issues in both information technologies and biomedical analysis. However, the difficulty to integrate non-NA molecules limits its power as a universal platform for molecular computation. Here, we report a versatile prototype of hybridized computation integrated with both nucleic acids and non-NA molecules. Employing the conformationally controlled ligand converters, we demonstrate that non-NA molecules, including both small molecules and proteins, can be computed as nucleic acid strands to construct the circuitry with increased complexity and scalability, and can be even programmed to solve arithmetical calculations within the computational nucleic acid system. This study opens a new door for molecular computation in which all-NA circuits can be expanded with integration of various ligands, and meanwhile, ligands can be precisely programmed by the nuclei acid computation.

A Novel Adaptive Linear Neuron Based on DNA Strand Displacement Reaction Network

Analog DNA strand displacement circuits can be used to build artificial neural network due to the continuity of dynamic behavior. In this study, DNA implementations of novel catalysis, novel degradation and adjustment reaction modules are designed and used to build an analog DNA strand displacement reaction network. A novel adaptive linear neuron (ADALINE) is constructed by the ordinary differential equations of an ideal formal chemical reaction network, which is built by reaction modules. When reaction network approaches equilibrium, the weights of the ADALINE are updated without learning algorithm. Simulation results indicate that, ADALINE based on the analog DNA strand displacement circuit has ability to implement the learning function of the ADALINE based on the ideal formal chemical reaction networks, and fit a class of linear function.

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

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.

Chemical Reaction Networks’ Programming for Solving Equations

The computational ability of the chemical reaction networks (CRNs) using DNA as the substrate has been verified previously. To solve more complex computational problems and perform the computational steps as expected, the practical design of the basic modules of calculation and the steps in the reactions have become the basic requirements for biomolecular computing. This paper presents a method for solving nonlinear equations in the CRNs with DNA as the substrate. We used the basic calculation module of the CRNs with a gateless structure to design discrete and analog algorithms and realized the nonlinear equations that could not be solved in the previous work, such as exponential, logarithmic, and simple triangle equations. The solution of the equation uses the transformation method, Taylor expansion, and Newton iteration method, and the simulation verified this through examples. We used and improved the basic calculation module of the CRN++ programming language, optimized the error in the basic module, and analyzed the error’s variation over time.

Programming cell-free biosensors with DNA strand displacement circuits

Cell-free biosensors are powerful platforms for monitoring human and environmental health. Here, we expand their capabilities by interfacing them with toehold-mediated strand displacement circuits, a dynamic DNA nanotechnology that enables molecular computation through programmable interactions between nucleic acid strands. We develop design rules for interfacing a small molecule sensing platform called ROSALIND with toehold-mediated strand displacement to construct hybrid RNA–DNA circuits that allow fine-tuning of reaction kinetics. We use these design rules to build 12 different circuits that implement a range of logic functions (NOT, OR, AND, IMPLY, NOR, NIMPLY, NAND). Finally, we demonstrate a circuit that acts like an analog-to-digital converter to create a series of binary outputs that encode the concentration range of the molecule being detected. We believe this work establishes a pathway to create ‘smart’ diagnostics that use molecular computations to enhance the speed and utility of biosensors.

Equipping ROSALIND, a cell-free biosensing platform, with information processing circuits based on toehold-mediated DNA strand displacement enhances sensor performance and enables logic gate computation.

Renewable DNA Proportional-Integral Controller with Photoresponsive Molecules

A molecular robot is an intelligent molecular system. A typical control problem of molecular robots is to maintain the concentration of a specific DNA strand at the desired level, which is typically attained by a molecular feedback control mechanism. A molecular feedback system can be constructed in a bottom-up method by transforming a nonlinear chemical reaction system into a pseudo-linear system. This method enables the implementation of a molecular proportional-integral (PI) controller on a DNA reaction system. However, a DNA reaction system is driven by fuel DNA strand consumption, and without a sufficient amount of fuel strands, the molecular PI controller cannot perform normal operations as a concentration regulator. In this study, we developed a design method for a molecular PI control system to regenerate fuel strands by introducing photoresponsive reaction control. To this end, we employed a photoresponsive molecule, azobenzene, to guide the reaction direction forward or backward using light irradiation. We validated our renewable design of the PI controller by numerical simulations based on the reaction kinetics. We also confirmed the proof-of-principle of our renewable design by conducting experiments using a basic DNA circuit.

Design Approaches and Computational Tools for DNA Nanostructures

Designing a structure in nanoscale with desired shape and properties has been enabled by structural DNA nanotechnology. Design strategies in this research field have evolved to interpret various aspects of increasingly more complex nanoscale assembly and to realize molecular-level functionality by exploring static to dynamic characteristics of the target structure. Computational tools have naturally been of significant interest as they are essential to achieve a fine control over both shape and physicochemical properties of the structure. Here, we review the basic design principles of structural DNA nanotechnology together with its computational analysis and design tools.

A Cooperative DNA Catalyst

DNA catalysts are fundamental building blocks for diverse molecular information-processing circuits. Allosteric control of DNA catalysts has been developed to activate desired catalytic pathways at desired times. Here we introduce a new type of DNA catalyst that we call a cooperative catalyst: a pair of reversible reactions are employed to drive a catalytic cycle in which two signal species, which can be interpreted as an activator and an input, both exhibit catalytic behavior for output production. We demonstrate the role of a dissociation toehold in controlling the kinetics of the reaction pathway and the significance of a wobble base pair in promoting the robustness of the activator. We show near-complete output production with input and activator concentrations that are 0.1 times the gate concentration. The system involves just a double-stranded gate species and a single-stranded fuel species, as simple as the seesaw DNA catalyst, which has no allosteric control. The simplicity and modularity of the design make the cooperative DNA catalyst an exciting addition to strand-displacement motifs for general-purpose computation and dynamics.

Solving 0-1 Integer Programming Problem Based on DNA Strand Displacement Reaction Network

Chemical reaction networks (CRNs) based on DNA strand displacement (DSD) can be used as an effective programming language for solving various mathematical problems. In this paper, we design three chemical reaction modules by using the DNA strand displacement reaction as the basic principle, with a weighted reaction module, sum reaction module, and threshold reaction module. These modules are used as basic elements to form chemical reaction networks that can be used to solve 0-1 integer programming problems. The problem can be solved through the three steps of weighting, sum, and threshold, and then the results of the operations can be expressed through a single-stranded DNA output with fluorescent molecules. Finally, we use biochemical experiments and Visual DSD simulation software to verify and evaluate the chemical reaction networks. The results have shown that the DSD-based chemical reaction networks constructed in this paper have good feasibility and stability.

Multifunctional Clip Strand for the Regulation of DNA Strand Displacement and Construction of Complex DNA Nanodevices

Strand displacement reactions are important bricks for the construction of various DNA nanodevices, among which the toehold-mediated strand displacement reaction is the most prevalently adopted. However, only a limited number of tools could be used to finely regulate the toehold reaction, thus restricting DNA nanodevices from being more multifunctional and powerful. Herein, we developed a regulation tool, Clip, and achieved multiple regulatory functions, including subtle adjustment of the reaction rates, allosteric strand displacement, selective activation, and resetting of the reaction. Taking advantages of the multiple functions, we constructed Clip-toehold-based DNA walking machines. They showed behaviors of controllable walking, concatenation, and programmable pathways. Furthermore, we built Clip-toehold-based AND and OR logic gates and integrated those logic gates to construct multilayer circuits, which could be reset and reused to process different input signals. We believe that the proposed Clip tool has expanded the functionality of DNA strand displacement-based nanodevices to a much more complex and diverse level and anticipate that the tool will be widely adopted in DNA nanotechnology.

Synthesis Strategy of Reversible Circuits on DNA Computers

DNA computers and quantum computers are gaining attention as alternatives to classical digital computers. DNA is a biological material that can be reprogrammed to perform computing functions. Quantum computing performs reversible computations by nature based on the laws of quantum mechanics. In this paper, DNA computing and reversible computing are combined to propose novel theoretical methods to implement reversible gates and circuits in DNA computers based on strand displacement reactions, since the advantages of reversible logic gates can be exploited to improve the capabilities and functionalities of DNA computers. This paper also proposes a novel universal reversible gate library (URGL) for synthesizing n-bit reversible circuits using DNA to reduce the average length and cost of the constructed circuits when compared with previous methods. Each n-bit URGL contains building blocks to generate all possible permutations of a symmetric group of degree n. Our proposed group (URGL) in the paper is a permutation group. The proposed implementation methods will improve the efficiency of DNA computer computations as the results of DNA implementations are better in terms of quantum cost, DNA cost, and circuit length.

Single-cell nucleic acid profiling in droplets (SNAPD) enables high-throughput analysis of heterogeneous cell populations

Experimental methods that capture the individual properties of single cells are revealing the key role of cell-to-cell variability in countless biological processes. These single-cell methods are becoming increasingly important across the life sciences in fields such as immunology, regenerative medicine and cancer biology. In addition to high-dimensional transcriptomic techniques such as single-cell RNA sequencing, there is a need for fast, simple and high-throughput assays to enumerate cell samples based on RNA biomarkers. In this work, we present single-cell nucleic acid profiling in droplets (SNAPD) to analyze sets of transcriptional markers in tens of thousands of single mammalian cells. Individual cells are encapsulated in aqueous droplets on a microfluidic chip and the RNA markers in each cell are amplified. Molecular logic circuits then integrate these amplicons to categorize cells based on the transcriptional markers and produce a detectable fluorescence output. SNAPD is capable of analyzing over 100,000 cells per hour and can be used to quantify distinct cell types within heterogeneous populations, detect rare cells at frequencies down to 0.1% and enrich specific cell types using microfluidic sorting. SNAPD provides a simple, rapid, low cost and scalable approach to study complex phenotypes in heterogeneous cell populations.

The art of molecular computing: Whence and whither

An astonishingly diverse biomolecular circuitry orchestrates the functioning machinery underlying every living cell. These biomolecules and their circuits have been engineered not only for various industrial applications but also to perform other atypical functions that they were not evolved for-including computation. Various kinds of computational challenges, such as solving NP-complete problems with many variables, logical computation, neural network operations, and cryptography, have all been attempted through this unconventional computing paradigm. In this review, we highlight key experiments across three different ''eras'' of molecular computation, beginning with molecular solutions, transitioning to logic circuits and ultimately, more complex molecular networks. We also discuss a variety of applications of molecular computation, from solving NP-hard problems to self-assembled nanostructures for delivering molecules, and provide a glimpse into the exciting potential that molecular computing holds for the future. Also see the video abstract here: https://youtu.be/9Mw0K0vCSQw.

Biocomputing Based on DNA Strand Displacement Reactions

The high sequence specificity and precise base complementary pairing principle of DNA provides a rich orthogonal molecular library for molecular programming, making it one of the most promising materials for developing bio-compatible intelligence. In recent years, DNA has been extensively studied and applied in the field of biological computing. Among them, the toehold-mediated strand displacement reaction (SDR) with properties including enzyme free, flexible design and precise control, have been extensively used to construct biological computing circuits. This review provides a systemic overview of SDR design principles and the applications. Strategies for designing DNA-only, enzymes-assisted, other molecules-involved and external stimuli-controlled SDRs are described. The recently realized computing functions and the application of DNA computing in other fields are introduced. Finally, the advantages and challenges of SDR-based computing are discussed.

Analytical design of multi-threshold and high fan-in DNA-based logical sensors to profile the pattern of MS microRNAs

Early detection of diseases is very important to increase the life quality and reduce the treatment cost for the patient. MicroRNAs have been introduced in recent years as an efficient class of biomarkers for detecting the risky situation of many diseases such as cancers, Multiple sclerosis (MS), and heart attacks, and other diseases. Now, real-time PCR has been used to profile the microRNA expression, which is expensive, time-consuming, and has low accuracy. Most recently, DNA logic gates are used to detect the MicroRNA expression level that is more accurate and faster than previous methods. In this paper, we improved the design of multi-threshold and multi-input DNA-based logic gates in response to specific microRNA (miRNA) inputs. The proposed design style can simultaneously recognize multiple miRNAs with different rising and falling thresholds. The proposed structure in this paper is used to diagnose Multiple Sclerosis (MS) as a case study. We simulated this system to understand its performance and compare it with other existing methods. The simulation results show the efficiency of the proposed method in terms of accuracy, efficiency, and speed. In this analysis, unwanted reactions, fault positive, and the probability of generating the final output using the formal method are investigated in depth. Finally, the proposed solutions are improved based on the results of these analyses. The analytic approach of this paper helps to design the DNA-based logic gates for real diseases.

Encryption Algorithm Based on DNA Strand Displacement and DNA Sequence Operation

DNA strand displacement is introduced in this study and used to construct an analog DNA strand displacement chaotic system based on six reaction modules in nanoscale size. The DNA strand displacement circuit is employed in encryption as a chaotic generator to produce chaotic sequences. In the encryption algorithm, we convert chaotic sequences to binary ones by comparing the concentration of signal DNA strand. Simulation results show that the encryption scheme is sensitive to the keys, and key space is large enough to resist the brute-force attacks, furthermore algorithm has a high capacity to resist statistic attack. Based on robustness analysis, our proposed encryption scheme is robust to the DNA strand displacement reaction rate control, noise and concentration detection to a certain extent.

Information processing based on DNA toehold-mediated strand displacement (TMSD) reaction

SemiSynBio is an emerging topic toward the construction of platforms for next-generation information processing. Recent research has indicated its promising prospect toward information processing including algorithm design and pattern manipulation with the DNA TMSD reaction, which is one of the cores of the SemiSynBio technology route. The DNA TMSD reaction is the process in which an invader strand displaces the incumbent strand from the gate strand through initiation at the exposed toehold domain. Also, the DNA TMSD reaction generally involves three processes: toehold association, branch migration and strand disassociation. Herein, we review the recent progress on information processing with the DNA TMSD reaction. We highlight the diverse developments on information processing with the logic circuit, analog circuit, combinational circuit and information relay with the DNA origami structure. Additionally, we explore the current challenges and various trends toward the design and application of the DNA TMSD reaction in future information processing.

Cross-Inhibitor: a time-sensitive molecular circuit based on DNA strand displacement

Designing biochemical systems that can be effectively used in diverse fields, including diagnostics, molecular computing and nanomachines, has long been recognized as an important goal of molecular programming and DNA nanotechnology. A key issue in the development of such practical devices on the nanoscale lies in the development of biochemical components with information-processing capacity. In this article, we propose a molecular device that utilizes DNA strand displacement networks and allows interactive inhibition between two input signals; thus, it is termed a cross-inhibitor. More specifically, the device supplies each input signal with a processor such that the processing of one input signal will interdict the signal of the other. Biochemical experiments are conducted to analyze the interdiction performance with regard to effectiveness, stability and controllability. To illustrate its feasibility, a biochemical framework grounded in this mechanism is presented to determine the winner of a tic-tac-toe game. Our results highlight the potential for DNA strand displacement cascades to act as signal controllers and event triggers to endow molecular systems with the capability of controlling and detecting events and signals.

Domain-Specific Programming Languages for Computational Nucleic Acid Systems

The construction of models of system behavior is of great importance throughout science and engineering. In bioengineering and bionanotechnology, these often take the form of dynamic models that specify the evolution of different species over time. To ensure that scientific observations and conclusions are consistent and that systems can be reliably engineered on the basis of model predictions, it is important that models of biomolecular systems can be constructed in a reliable, principled, and efficient manner. This review focuses on efforts to address this need by using domain-specific programming languages as the basis for custom design tools for researchers working on computational nucleic acid devices, where a domain-specific language is simply a programming language tailored to a particular application domain. The underlying thesis of our review is that there is a continuum of practical implementation strategies for computational nucleic acid systems, which can all benefit from appropriate domain-specific languages and software design tools. We emphasize the need for specialized yet flexible tools that can be realized using domain-specific languages that compile to more general-purpose representations.

Demonstration of Arithmetic Calculations by DNA Tile-Based Algorithmic Self-Assembly

Owing to its high information density, energy efficiency, and massive parallelism, DNA computing has undergone several advances and made significant contributions to nanotechnology. Notably, arithmetic calculations implemented by multiple logic gates such as adders and subtractors have received much attention because of their well-established logic algorithms and feasibility of experimental implementation. Although small molecules have been used to implement these computations, a DNA tile-based calculator has been rarely addressed owing to complexity of rule design and experimental challenges for direct verification. Here, we construct a DNA-based calculator with three types of building blocks (propagator, connector, and solution tiles) to perform addition and subtraction operations through algorithmic self-assembly. An atomic force microscope is used to verify the solutions. Our method provides a potential platform for the construction of various types of DNA algorithmic crystals (such as flip-flops, encoders, and multiplexers) by embedding multiple logic gate operations in the DNA base sequences.

Design of hidden thermodynamic driving for non-equilibrium systems via mismatch elimination during DNA strand displacement

Recent years have seen great advances in the development of synthetic self-assembling molecular systems. Designing out-of-equilibrium architectures, however, requires a more subtle control over the thermodynamics and kinetics of reactions. We propose a mechanism for enhancing the thermodynamic drive of DNA strand-displacement reactions whilst barely perturbing forward reaction rates: the introduction of mismatches within the initial duplex. Through a combination of experiment and simulation, we demonstrate that displacement rates are strongly sensitive to mismatch location and can be tuned by rational design. By placing mismatches away from duplex ends, the thermodynamic drive for a strand-displacement reaction can be varied without significantly affecting the forward reaction rate. This hidden thermodynamic driving motif is ideal for the engineering of non-equilibrium systems that rely on catalytic control and must be robust to leak reactions.

Availability-Driven Design of Hairpin Fuels and Small Interfering Strands for Leakage Reduction in Autocatalytic Networks

DNA-based circuits and computational tools offer great potential for advanced biomedical and technological applications. However, leakage, which is the production of an output in the absence of an input, widely exists in DNA network. As a new approach to leakage reduction, this study utilizes availability to reduce leakage in an entropy-driven autocatalytic DNA reaction networks. Here, we report the performance improvements resulting from direct tailoring of fuel strand availability through two novel approaches: (1) the addition of interfering domains to fuel strands, and (2) the introduction of separate small interfering strands. The best performing fuel designs resulted in increased performance ratios of up to 22%. Employing small interfering strands (5-12 nucleotides (nt)) improved the performance ratios by up to 21%. Furthermore, the stability of the network using either leakage reduction method matched well with computed availability and experimental results showing Spearman correlation coefficients of -0.84 for modified fuel strands and -0.92 for small interfering strands.

Implementing digital computing with DNA-based switching circuits

DNA strand displacement reactions (SDRs) provide a set of intelligent toolboxes for developing molecular computation. Whereas SDR-based logic gate circuits have achieved a high level of complexity, the scale-up for practical achievable computational tasks remains a hurdle. Switching circuits that were originally proposed by Shannon in 1938 and nowadays widely used in telecommunication represent an alternative and efficient means to realize fast-speed and high-bandwidth communication. Here we develop SDR-based DNA switching circuits (DSCs) for implementing digital computing. Using a routing strategy on a programmable DNA switch canvas, we show that arbitrary Boolean functions can be represented by DSCs and implemented with molecular switches with high computing speed. We further demonstrate the implementation of full-adder and square-rooting functions using DSCs, which only uses down to 1/4 DNA strands as compared with a dual-rail logic expression-based design. We expect that DSCs provide a design paradigm for digital computation with biomolecules.

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.

Conformational enhancement of fidelity in toehold-sequestered DNA nanodevices

Simple design principles improve conformational stability and decrease strand leakage by two orders of magnitude.

In Vitro Transcriptional Regulation via Nucleic-Acid-Based Transcription Factors

Cells execute complex transcriptional programs by deploying distinct protein regulatory assemblies that interact with cis-regulatory elements throughout the genome. Using concepts from DNA nanotechnology, we synthetically recapitulated this feature in in vitro gene networks actuated by T7 RNA polymerase (RNAP). Our approach involves engineering nucleic acid hybridization interactions between a T7 RNAP site-specifically functionalized with single-stranded DNA (ssDNA), templates displaying cis-regulatory ssDNA domains, and auxiliary nucleic acid assemblies acting as artificial transcription factors (TFs). By relying on nucleic acid hybridization, de novo regulatory assemblies can be computationally designed to emulate features of protein-based TFs, such as cooperativity and combinatorial binding, while offering unique advantages such as programmability, chemical stability, and scalability. We illustrate the use of nucleic acid TFs to implement transcriptional logic, cascading, feedback, and multiplexing. This framework will enable rapid prototyping of increasingly complex in vitro genetic devices for applications such as portable diagnostics, bioanalysis, and the design of adaptive materials.

Repeated Reuse of Deoxyribozyme-Based Logic Gates

Deoxyribozymes (DNAzymes) have demonstrated a significant capacity for biocomputing and hold promise for information processing within advanced biological devices if several key capabilities are developed. One required capability is reuse-having DNAzyme logic gates be cyclically, and controllably, activated and deactivated. We designed an oligonucleotide-based system for DNAzyme reuse that could (1) remove previously bound inputs by addition of complementary oligonucleotides via toe-hold mediated binding and (2) diminish output signal through the addition of quencher-labeled oligonucleotides complementary to the fluorophore-bound substrate. Our system demonstrated, for the first time, the ability for DNAzymes to have their activity toggled, with activity returning to 90-125% of original activity. This toggling could be performed multiple times with control being exerted over when the toggling occurs, with three clear cycles observed before the variability in activity became too great. Our data also demonstrated that fluorescent output of the DNAzyme activity could be actively removed and regenerated. This reuse system can increase the efficiency of DNAzyme-based logic circuits by reducing the number of redundant oligonucleotides and is critical for future development of reusable biodevices controlled by logical operations.

Nucleic Acid Databases and Molecular-Scale Computing

DNA outperforms most conventional storage media in terms of information retention time, physical density, and volumetric coding capacity. Advances in synthesis and sequencing technologies have enabled implementations of large synthetic DNA databases with impressive storage capacity and reliable data recovery. Several robust DNA storage architectures featuring random access, error correction, and content rewritability have been constructed with the potential for scalability and cost reduction. We survey these recent achievements and discuss alternative routes for overcoming the hurdles of engineering practical DNA storage systems. We also review recent exciting work on in vivo DNA memory including intracellular recorders constructed by programmable genome editing tools. Besides information storage, DNA could serve as a versatile molecular computing substrate. We highlight several state-of-the-art DNA computing techniques such as strand displacement, localized hybridization chain reactions, and enzymatic reaction networks. We summarize how these simple primitives have facilitated rational designs and implementations of in vitro DNA reaction networks that emulate digital/analog circuits, artificial neural networks, or nonlinear dynamic systems. We envision these modular primitives could be strategically adapted for sophisticated database operations and massively parallel computations on DNA databases. We also highlight in vivo DNA computing modules such as CRISPR logic gates for building scalable genetic circuits in living cells. To conclude, we discuss various implications and challenges of DNA-based storage and computing, and we particularly encourage innovative work on bridging these two areas of research to further explore molecular parallelism and near-data processing. Such integrated molecular systems could lead to far-reaching applications in biocomputing, security, and medicine.

An emergent understanding of strand displacement in RNA biology

DNA and RNA are generally regarded as central molecules in molecular biology. Recent advancements in the field of DNA/RNA nanotechnology successfully used DNA/RNA as programmable molecules to construct molecular machines and nanostructures with predefined shapes and functions. The key mechanism for dynamic control of the conformations of these DNA/RNA nanodevices is a reaction called strand displacement, in which one strand in a formed duplex is replaced by a third invading strand. While DNA/RNA strand displacement has mainly been used to de novo design molecular devices, we argue in this review that this reaction is also likely to play a key role in multiple cellular events such as gene recombination, CRISPR-based genome editing, and RNA cotranscriptional folding. We introduce the general mechanism of strand displacement reaction, give examples of its use in the construction of molecular machines, and finally review natural processes having characteristic which suggest that strand displacement is occurring.

Autonomous dynamic control of DNA nanostructure self-assembly

Biological cells routinely reconfigure their shape using dynamic signalling and regulatory networks that direct self-assembly processes in time and space, through molecular components that sense, process and transmit information from the environment. A similar strategy could be used to enable life-like behaviours in synthetic materials. Nucleic acid nanotechnology offers a promising route towards this goal through a variety of sensors, logic and dynamic components and self-assembling structures. Here, by harnessing both dynamic and structural DNA nanotechnology, we demonstrate dynamic control of the self-assembly of DNA nanotubes-a well-known class of programmable DNA nanostructures. Nanotube assembly and disassembly is controlled with minimal synthetic gene systems, including an autonomous molecular oscillator. We use a coarse-grained computational model to capture nanotube length distribution dynamics in response to inputs from nucleic acid circuits. We hope that these results may find use for the development of responsive nucleic acid materials, with potential applications in biomaterials science, nanofabrication and drug delivery.

Temporal Pattern Recognition through Analog Molecular Computation

Living cells communicate information about physiological conditions by producing signaling molecules in a specific timed manner. Different conditions can result in the same total amount of a signaling molecule, differing only in the pattern of the molecular concentration over time. Such temporally coded information can be completely invisible to even state-of-the-art molecular sensors with high chemical specificity that respond only to the total amount of the signaling molecule. Here, we demonstrate design principles for circuits with temporal specificity, that is, molecular circuits that respond to specific temporal patterns in a molecular concentration. We consider pulsatile patterns in a molecular concentration characterized by three fundamental temporal features: time period, duty fraction, and number of pulses. We develop circuits that respond to each one of these features while being insensitive to the others. We demonstrate our design principles using general chemical reaction networks and with explicit simulations of DNA strand displacement reactions. In this way, our work develops building blocks for temporal pattern recognition through molecular computation.

Transducing Protease Activity into DNA Output for Developing Smart Bionanosensors

DNA can process information through sequence-based reorganization but cannot typically receive input information from most biological processes and translate that into DNA compatible language. Coupling DNA to a substrate responsive to biological events can address this limitation. A two-component sensor incorporating a chimeric peptide-DNA substrate is evaluated here as a protease-to-DNA signal convertor which transduces protease activity through DNA gates that discriminate between different input proteases. Acceptor dye-labeled peptide-DNAs are assembled onto semiconductor quantum dot (QD) donors as the input gate. Addition of trypsin or chymotrypsin cleaves their cognate peptide sequence altering the efficiency of Förster resonance energy transfer (FRET) with the QD and frees a DNA output which interacts with a tetrahedral output gate. Downstream output gate rearrangement results in FRET sensitization of a new acceptor dye. Following characterization of component assembly and optimization of individual steps, sensor ability to discriminate between the two proteases is confirmed along with effects from joint interactions where potential for cross-talk is highest. Processing multiple bits of information for a sensing outcome provides more confidence than relying on a single change especially for the discrimination between different targets. Coupling other substrates to DNA that respond similarly could help target other types of enzymes.

Principles and Applications of Nucleic Acid Strand Displacement Reactions

Dynamic DNA nanotechnology, a subfield of DNA nanotechnology, is concerned with the study and application of nucleic acid strand-displacement reactions. Strand-displacement reactions generally proceed by three-way or four-way branch migration and initially were investigated for their relevance to genetic recombination. Through the use of toeholds, which are single-stranded segments of DNA to which an invader strand can bind to initiate branch migration, the rate with which strand displacement reactions proceed can be varied by more than 6 orders of magnitude. In addition, the use of toeholds enables the construction of enzyme-free DNA reaction networks exhibiting complex dynamical behavior. A demonstration of this was provided in the year 2000, in which strand displacement reactions were employed to drive a DNA-based nanomachine (Yurke, B.; et al. Nature 2000, 406, 605-608). Since then, toehold-mediated strand displacement reactions have been used with ever increasing sophistication and the field of dynamic DNA nanotechnology has grown exponentially. Besides molecular machines, the field has produced enzyme-free catalytic systems, all DNA chemical oscillators and the most complex molecular computers yet devised. Enzyme-free catalytic systems can function as chemical amplifiers and as such have received considerable attention for sensing and detection applications in chemistry and medical diagnostics. Strand-displacement reactions have been combined with other enzymatically driven processes and have also been employed within living cells (Groves, B.; et al. Nat. Nanotechnol. 2015, 11, 287-294). Strand-displacement principles have also been applied in synthetic biology to enable artificial gene regulation and computation in bacteria. Given the enormous progress of dynamic DNA nanotechnology over the past years, the field now seems poised for practical application.

Automated sequence-level analysis of kinetics and thermodynamics for domain-level DNA strand-displacement systems

As an engineering material, DNA is well suited for the construction of biochemical circuits and systems, because it is simple enough that its interactions can be rationally designed using Watson-Crick base pairing rules, yet the design space is remarkably rich. When designing DNA systems, this simplicity permits using functional sections of each strand, called domains, without considering particular nucleotide sequences. However, the actual sequences used may have interactions not predicted at the domain-level abstraction, and new rigorous analysis techniques are needed to determine the extent to which the chosen sequences conform to the system's domain-level description. We have developed a computational method for verifying sequence-level systems by identifying discrepancies between the domain-level and sequence-level behaviour. This method takes a DNA system, as specified using the domain-level tool Peppercorn, and analyses data from the stochastic sequence-level simulator Multistrand and sequence-level thermodynamic analysis tool NUPACK to estimate important aspects of the system, such as reaction rate constants and secondary structure formation. These techniques, implemented as the Python package KinDA, will allow researchers to predict the kinetic and thermodynamic behaviour of domain-level systems after sequence assignment, as well as to detect violations of the intended behaviour.

Improving the Performance of DNA Strand Displacement Circuits by Shadow Cancellation

DNA strand displacement circuits are powerful tools that can be rationally engineered to implement molecular computing tasks because they are programmable, cheap, robust, and predictable. A key feature of these circuits is the use of catalytic gates to amplify signal. Catalytic gates tend to leak; that is, they generate output signal even in the absence of intended input. Leaks are harmful to the performance and correct operation of DNA strand displacement circuits. Here, we present "shadow cancellation", a general-purpose technique to mitigate leak in catalytic DNA strand displacement circuits. Shadow cancellation involves constructing a parallel shadow circuit that mimics the primary circuit and has the same leak characteristics. It is situated in the same test tube as the primary circuit and produces "anti-background" DNA strands that cancel "background" DNA strands produced by leak. We demonstrate the feasibility and strength of the shadow leak cancellation approach through a challenging test case, a cross-catalytic feedback DNA amplifier circuit that leaks prodigiously. Shadow cancellation dramatically reduced the leak of this circuit and improved the signal-to-background difference by several fold. Unlike existing techniques, it makes no modifications to the underlying amplifier circuit and is agnostic to its leak mechanism. Shadow cancellation also showed good robustness to concentration errors in multiple scenarios. This work introduces a direction in leak reduction techniques for DNA strand displacement amplifier circuits and can potentially be extended to other molecular amplifiers.

8-Bit Adder and Subtractor with Domain Label Based on DNA Strand Displacement

DNA strand displacement, which plays a fundamental role in DNA computing, has been widely applied to many biological computing problems, including biological logic circuits. However, there are many biological cascade logic circuits with domain labels based on DNA strand displacement that have not yet been designed. Thus, in this paper, cascade 8-bit adder/subtractor with a domain label is designed based on DNA strand displacement; domain t and domain f represent signal 1 and signal 0, respectively, instead of domain t and domain f are applied to representing signal 1 and signal 0 respectively instead of high concentration and low concentration high concentration and low concentration. Basic logic gates, an amplification gate, a fan-out gate and a reporter gate are correspondingly reconstructed as domain label gates. The simulation results of Visual DSD show the feasibility and accuracy of the logic calculation model of the adder/subtractor designed in this paper. It is a useful exploration that may expand the application of the molecular logic circuit.