Programmable chemical controllers made from DNA

In vitro molecular machine learning algorithm via symmetric internal loops of DNA

Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules.

Multi-Arm Junctions for Dynamic DNA Nanotechnology

Nonenzymatic catalytic substrates have been engineered using toehold-mediated DNA strand displacement, and their programmable applications range from medical diagnosis to molecular computation. However, the complexity, stability, scalability, and sensitivity of those systems are plagued by network leakage. A novel way to suppress leakage is to increase its energy barrier through four-way branch migration. Presented here, we designed multi-arm junction substrates that simultaneously exploit four-way branch migration, with a high-energy barrier to minimize leakage, and three-way branch migration, with a low-energy barrier to maximize catalysis. Original feed forward, autocatalytic, and cross-catalytic systems have been designed with polynomial and exponential amplification that exhibit the modularity of linear substrates and the stability of hairpin substrates, creating a new phase space for synthetic biologist, biotechnologist, and DNA nanotechnologists to explore. A key insight is that high-performing circuits can be engineered in the absence of intensive purification and/or extensive rounds of design optimization. Without adopting established leakage suppression techniques, the ratio of the catalytic rate constant to the leakage rate constant is more than 2 orders of magnitude greater than state-of-the-art linear and hairpin substrates. Our results demonstrate that multi-arm junctions have great potential to become central building blocks in dynamic DNA nanotechnology.

Microscopic agents programmed by DNA circuits

Information stored in synthetic nucleic acids sequences can be used in vitro to create complex reaction networks with precisely programmed chemical dynamics. Here, we scale up this approach to program networks of microscopic particles (agents) dispersed in an enzymatic solution. Agents may possess multiple stable states, thus maintaining a memory and communicate by emitting various orthogonal chemical signals, while also sensing the behaviour of neighbouring agents. Using this approach, we can produce collective behaviours involving thousands of agents, for example retrieving information over long distances or creating spatial patterns. Our systems recapitulate some fundamental mechanisms of distributed decision making and morphogenesis among living organisms and could find applications in cases where many individual clues need to be combined to reach a decision, for example in molecular diagnostics.

Design of an embedded inverse-feedforward biomolecular tracking controller for enzymatic reaction processes

Feedback control is widely used in chemical engineering to improve the performance and robustness of chemical processes. Feedback controllers require a 'subtractor' that is able to compute the error between the process output and the reference signal. In the case of embedded biomolecular control circuits, subtractors designed using standard chemical reaction network theory can only realise one-sided subtraction, rendering standard controller design approaches inadequate. Here, we show how a biomolecular controller that allows tracking of required changes in the outputs of enzymatic reaction processes can be designed and implemented within the framework of chemical reaction network theory. The controller architecture employs an inversion-based feedforward controller that compensates for the limitations of the one-sided subtractor that generates the error signals for a feedback controller. The proposed approach requires significantly fewer chemical reactions to implement than alternative designs, and should have wide applicability throughout the fields of synthetic biology and biological engineering.

Engineering chemical reaction modules via programming the assembly of DNA hairpins

The architect of enzyme-free chemical reaction modules, working as building blocks in implementing complex computing tasks, was achieved by modulating the assembly of DNA hairpins, including non-catalytic and catalytic systems. The performance of heterogeneous outputted sequences, which were programmed on different hairpins for triggering the downstream reaction, was asymmetric in the non-catalytic system, whereas symmetric in the catalytic system. Furthermore, complicated DNA-only chemical modules possessing controllable species of inputs or outputs were constructed based on our strategy. The kinetic studies revealed that the performance of the chemical modules was toehold length correlated; on the basis of which, a DNA concentration monitor was constructed.

Antibody-controlled actuation of DNA-based molecular circuits

DNA-based molecular circuits allow autonomous signal processing, but their actuation has relied mostly on RNA/DNA-based inputs, limiting their application in synthetic biology, biomedicine and molecular diagnostics. Here we introduce a generic method to translate the presence of an antibody into a unique DNA strand, enabling the use of antibodies as specific inputs for DNA-based molecular computing. Our approach, antibody-templated strand exchange (ATSE), uses the characteristic bivalent architecture of antibodies to promote DNA-strand exchange reactions both thermodynamically and kinetically. Detailed characterization of the ATSE reaction allowed the establishment of a comprehensive model that describes the kinetics and thermodynamics of ATSE as a function of toehold length, antibody-epitope affinity and concentration. ATSE enables the introduction of complex signal processing in antibody-based diagnostics, as demonstrated here by constructing molecular circuits for multiplex antibody detection, integration of multiple antibody inputs using logic gates and actuation of enzymes and DNAzymes for signal amplification.

DNA Strand-Displacement Timer Circuits

Chemical circuits can coordinate elaborate sequences of events in cells and tissues, from the self-assembly of biological complexes to the sequence of embryonic development. However, autonomously directing the timing of events in synthetic systems using chemical signals remains challenging. Here we demonstrate that a simple synthetic DNA strand-displacement circuit can release target sequences of DNA into solution at a constant rate after a tunable delay that can range from hours to days. The rates of DNA release can be tuned to the order of 1-100 nM per day. Multiple timer circuits can release different DNA strands at different rates and times in the same solution. This circuit can thus facilitate precise coordination of chemical events in vitro without external stimulation.

Surface-assisted DNA self-assembly: An enzyme-free strategy towards formation of branched DNA lattice

DNA based self-assembled nanostructures and DNA origami has proven useful for organizing nanomaterials with firm precision. However, for advanced applications like nanoelectronics and photonics, large-scale organization of self-assembled branched DNA (bDNA) into periodic lattices is desired. In this communication for the first time we report a facile method of self-assembly of Y-shaped bDNA nanostructures on the cationic surface of Aluminum (Al) foil to prepare periodic two dimensional (2D) bDNA lattice. Particularly those Y-shaped bDNA structures having smaller overhangs and unable to self-assemble in solution, they are easily assembled on the surface of Al foil in the absence of ligase. Field emission scanning electron microscopy (FESEM) analysis shows homogenous distribution of two-dimensional bDNA lattices across the Al foil. When the assembled bDNA structures were recovered from the Al foil and electrophoresed in nPAGE only higher order polymeric bDNA structures were observed without a trace of monomeric structures which confirms the stability and high yield of the bDNA lattices. Therefore, this enzyme-free economic and efficient strategy for developing bDNA lattices can be utilized in assembling various nanomaterials for functional molecular components towards development of DNA based self-assembled nanodevices.

Chemical Reaction Networks for Computing Polynomials

Chemical reaction networks (CRNs) provide a fundamental model in the study of molecular systems. Widely used as formalism for the analysis of chemical and biochemical systems, CRNs have received renewed attention as a model for molecular computation. This paper demonstrates that, with a new encoding, CRNs can compute any set of polynomial functions subject only to the limitation that these functions must map the unit interval to itself. These polynomials can be expressed as linear combinations of Bernstein basis polynomials with positive coefficients less than or equal to 1. In the proposed encoding approach, each variable is represented using two molecular types: a type-0 and a type-1. The value is the ratio of the concentration of type-1 molecules to the sum of the concentrations of type-0 and type-1 molecules. The proposed encoding naturally exploits the expansion of a power-form polynomial into a Bernstein polynomial. Molecular encoders for converting any input in a standard representation to the fractional representation as well as decoders for converting the computed output from the fractional to a standard representation are presented. The method is illustrated first for generic CRNs; then chemical reactions designed for an example are mapped to DNA strand-displacement reactions.

Stacking modular DNA circuitry in cascading self-assembly of spherical nucleic acids

Integrated circuitries are successfully built through using the cascaded modular strategy with the assistance of stochastic simulations.

Efficient immobilization of enzymes onto magnetic nanoparticles by DNA strand displacement: a stable and high-performance biocatalyst

An efficient enzyme immobilization strategy based on toehold-mediated DNA strand displacement on modified magnetic nanoparticles was developed in this study.

Materials learning from life: concepts for active, adaptive and autonomous molecular systems

A broad overview of functional aspects in biological and synthetic out-of-equilibrium systems.

Stable DNA-based reaction–diffusion patterns

This paper demonstrates the generation of enzyme free DNA reaction–diffusion gradientsin vitrothat remain stable for tens of hours.

Exploiting the dynamic properties of covalent modification cycle for the design of synthetic analog biomolecular circuitry

Background

Cycles of covalent modification are ubiquitous motifs in cellular signalling. Although such signalling cycles are implemented via a highly concise set of chemical reactions, they have been shown to be capable of producing multiple distinct input-output mapping behaviours – ultrasensitive, hyperbolic, signal-transducing and threshold-hyperbolic.

Results

In this paper, we show how the set of chemical reactions underlying covalent modification cycles can be exploited for the design of synthetic analog biomolecular circuitry. We show that biomolecular circuits based on the dynamics of covalent modification cycles allow (a) the computation of nonlinear operators using far fewer chemical reactions than purely abstract designs based on chemical reaction network theory, and (b) the design of nonlinear feedback controllers with strong performance and robustness properties.

Conclusions

Our designs provide a more efficient route for translation of complex circuits and systems from chemical reactions to DNA strand displacement-based chemistry, thus facilitating their experimental implementation in future Synthetic Biology applications.

Stochastic analysis of Chemical Reaction Networks using Linear Noise Approximation

Stochastic evolution of Chemical Reactions Networks (CRNs) over time is usually analyzed through solving the Chemical Master Equation (CME) or performing extensive simulations. Analysing stochasticity is often needed, particularly when some molecules occur in low numbers. Unfortunately, both approaches become infeasible if the system is complex and/or it cannot be ensured that initial populations are small. We develop a probabilistic logic for CRNs that enables stochastic analysis of the evolution of populations of molecular species. We present an approximate model checking algorithm based on the Linear Noise Approximation (LNA) of the CME, whose computational complexity is independent of the population size of each species and polynomial in the number of different species. The algorithm requires the solution of first order polynomial differential equations. We prove that our approach is valid for any CRN close enough to the thermodynamical limit. However, we show on four case studies that it can still provide good approximation even for low molecule counts. Our approach enables rigorous analysis of CRNs that are not analyzable by solving the CME, but are far from the deterministic limit. Moreover, it can be used for a fast approximate stochastic characterization of a CRN.

Design of a Synthetic Integral Feedback Circuit: Dynamic Analysis and DNA Implementation

The design and implementation of regulation motifs ensuring robust perfect adaptation are challenging problems in synthetic biology. Indeed, the design of high-yield robust metabolic pathways producing, for instance, drug precursors and biofuels, could be easily imagined to rely on such a control strategy in order to optimize production levels and reduce production costs, despite the presence of environmental disturbance and model uncertainty. We propose here a motif that ensures tracking and robust perfect adaptation for the controlled reaction network through integral feedback. Its metabolic load on the host is fully tunable and can be made arbitrarily close to the constitutive limit, the universal minimal metabolic load of all possible controllers. A DNA implementation of the controller network is finally provided. Computer simulations using realistic parameters demonstrate the good agreement between the DNA implementation and the ideal controller dynamics.

Supervised Learning in Adaptive DNA Strand Displacement Networks

The development of engineered biochemical circuits that exhibit adaptive behavior is a key goal of synthetic biology and molecular computing. Such circuits could be used for long-term monitoring and control of biochemical systems, for instance, to prevent disease or to enable the development of artificial life. In this article, we present a framework for developing adaptive molecular circuits using buffered DNA strand displacement networks, which extend existing DNA strand displacement circuit architectures to enable straightforward storage and modification of behavioral parameters. As a proof of concept, we use this framework to design and simulate a DNA circuit for supervised learning of a class of linear functions by stochastic gradient descent. This work highlights the potential of buffered DNA strand displacement as a powerful circuit architecture for implementing adaptive molecular systems.

Analog Computation by DNA Strand Displacement Circuits

DNA circuits have been widely used to develop biological computing devices because of their high programmability and versatility. Here, we propose an architecture for the systematic construction of DNA circuits for analog computation based on DNA strand displacement. The elementary gates in our architecture include addition, subtraction, and multiplication gates. The input and output of these gates are analog, which means that they are directly represented by the concentrations of the input and output DNA strands, respectively, without requiring a threshold for converting to Boolean signals. We provide detailed domain designs and kinetic simulations of the gates to demonstrate their expected performance. On the basis of these gates, we describe how DNA circuits to compute polynomial functions of inputs can be built. Using Taylor Series and Newton Iteration methods, functions beyond the scope of polynomials can also be computed by DNA circuits built upon our architecture.

A DNA-Based Archival Storage System

Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up. Using DNA to archive data is an attractive possibility because it is extremely dense, with a raw limit of 1 exabyte/mm 3 (109 GB/mm 3 ), and long-lasting, with observed half-life of over 500 years. This paper presents an architecture for a DNA-based archival storage system. It is structured as a key-value store, and leverages common biochemical techniques to provide random access. We also propose a new encoding scheme that offers controllable redundancy, trading off reliability for density. We demonstrate feasibility, random access, and robustness of the proposed encoding with wet lab experiments involving 151 kB of synthesized DNA and a 42 kB random-access subset, and simulation experiments of larger sets calibrated to the wet lab experiments. Finally, we highlight trends in biotechnology that indicate the impending practicality of DNA storage for much larger datasets.

High-resolution mapping of bifurcations in nonlinear biochemical circuits

Analog molecular circuits can exploit the nonlinear nature of biochemical reaction networks to compute low-precision outputs with fewer resources than digital circuits. This analog computation is similar to that employed by gene-regulation networks. Although digital systems have a tractable link between structure and function, the nonlinear and continuous nature of analog circuits yields an intricate functional landscape, which makes their design counter-intuitive, their characterization laborious and their analysis delicate. Here, using droplet-based microfluidics, we map with high resolution and dimensionality the bifurcation diagrams of two synthetic, out-of-equilibrium and nonlinear programs: a bistable DNA switch and a predator-prey DNA oscillator. The diagrams delineate where function is optimal, dynamics bifurcates and models fail. Inverse problem solving on these large-scale data sets indicates interference from enzymatic coupling. Additionally, data mining exposes the presence of rare, stochastically bursting oscillators near deterministic bifurcations.

A strand graph semantics for DNA-based computation

DNA nanotechnology is a promising approach for engineering computation at the nanoscale, with potential applications in biofabrication and intelligent nanomedicine. DNA strand displacement is a general strategy for implementing a broad range of nanoscale computations, including any computation that can be expressed as a chemical reaction network. Modelling and analysis of DNA strand displacement systems is an important part of the design process, prior to experimental realisation. As experimental techniques improve, it is important for modelling languages to keep pace with the complexity of structures that can be realised experimentally. In this paper we present a process calculus for modelling DNA strand displacement computations involving rich secondary structures, including DNA branches and loops. We prove that our calculus is also sufficiently expressive to model previous work on non-branching structures, and propose a mapping from our calculus to a canonical strand graph representation, in which vertices represent DNA strands, ordered sites represent domains, and edges between sites represent bonds between domains. We define interactions between strands by means of strand graph rewriting, and prove the correspondence between the process calculus and strand graph behaviours. Finally, we propose a mapping from strand graphs to an efficient implementation, which we use to perform modelling and simulation of DNA strand displacement systems with rich secondary structure.

Modular verification of chemical reaction network encodings via serializability analysis

Chemical reaction networks are a powerful means of specifying the intended behaviour of synthetic biochemical systems. A high-level formal specification, expressed as a chemical reaction network, may be compiled into a lower-level encoding, which can be directly implemented in wet chemistry and may itself be expressed as a chemical reaction network. Here we present conditions under which a lower-level encoding correctly emulates the sequential dynamics of a high-level chemical reaction network. We require that encodings are transactional, such that their execution is divided by a "commit reaction" that irreversibly separates the reactant-consuming phase of the encoding from the product-generating phase. We also impose restrictions on the sharing of species between reaction encodings, based on a notion of "extra tolerance", which defines species that may be shared between encodings without enabling unwanted reactions. Our notion of correctness is serializability of interleaved reaction encodings, and if all reaction encodings satisfy our correctness properties then we can infer that the global dynamics of the system are correct. This allows us to infer correctness of any system constructed using verified encodings. As an example, we show how this approach may be used to verify two- and four-domain DNA strand displacement encodings of chemical reaction networks, and we generalize our result to the limit where the populations of helper species are unlimited.

An enzyme-free catalytic DNA circuit for amplified detection of aflatoxin B1 using gold nanoparticles as colorimetric indicators

An enzyme-free biosensor for the amplified detection of aflatoxin B1 has been constructed based on a catalytic DNA circuit. Three biotinylated hairpin DNA probes (H1, H2, and H3) were designed as the assembly components to construct the sensing system (triplex H1-H2-H3 product). Cascaded signal amplification capability was obtained through toehold-mediated strand displacement reactions to open the hairpins and recycle the trigger DNA. By the use of streptavidin-functionalized gold nanoparticles as the signal indicators, the colorimetric readout can be observed by the naked eye. In the presence of a target, the individual nanoparticles (red) aggregate into a cross-linked network of nanoparticles (blue) via biotin-streptavidin coupling. The colorimetric assay is ultrasensitive, enabling the visual detection of trace levels of aflatoxin B1 (AFB1) as low as 10 pM without instrumentation. The calculated limit of detection (LOD) is 2 pM in terms of 3 times standard deviation over the blank response. The sensor is robust and works even when challenged with complex sample matrices such as rice samples. Our sensing platform is simple and convenient in operation, requiring only the mixing of several solutions at room temperature to achieve visible and intuitive results, and holds great promise for the point-of-use monitoring of AFB1 in environmental and food samples.

Implementing Nonlinear Feedback Controllers Using DNA Strand Displacement Reactions

We show how an important class of nonlinear feedback controllers can be designed using idealized abstract chemical reactions and implemented via DNA strand displacement (DSD) reactions. Exploiting chemical reaction networks (CRNs) as a programming language for the design of complex circuits and networks, we show how a set of unimolecular and bimolecular reactions can be used to realize input-output dynamics that produce a nonlinear quasi sliding mode (QSM) feedback controller. The kinetics of the required chemical reactions can then be implemented as enzyme-free, enthalpy/entropy driven DNA reactions using a toehold mediated strand displacement mechanism via Watson-Crick base pairing and branch migration. We demonstrate that the closed loop response of the nonlinear QSM controller outperforms a traditional linear controller by facilitating much faster tracking response dynamics without introducing overshoots in the transient response. The resulting controller is highly modular and is less affected by retroactivity effects than standard linear designs.

Molecular circuits for dynamic noise filtering

The invention of the Kalman filter is a crowning achievement of filtering theory-one that has revolutionized technology in countless ways. By dealing effectively with noise, the Kalman filter has enabled various applications in positioning, navigation, control, and telecommunications. In the emerging field of synthetic biology, noise and context dependency are among the key challenges facing the successful implementation of reliable, complex, and scalable synthetic circuits. Although substantial further advancement in the field may very well rely on effectively addressing these issues, a principled protocol to deal with noise-as provided by the Kalman filter-remains completely missing. Here we develop an optimal filtering theory that is suitable for noisy biochemical networks. We show how the resulting filters can be implemented at the molecular level and provide various simulations related to estimation, system identification, and noise cancellation problems. We demonstrate our approach in vitro using DNA strand displacement cascades as well as in vivo using flow cytometry measurements of a light-inducible circuit in Escherichia coli.

An Engineered Kinetic Amplification Mechanism for Single Nucleotide Variant Discrimination by DNA Hybridization Probes

Even a single-nucleotide difference between the sequences of two otherwise identical biological nucleic acids can have dramatic functional consequences. Here, we use model-guided reaction pathway engineering to quantitatively improve the performance of selective hybridization probes in recognizing single nucleotide variants (SNVs). Specifically, we build a detection system that combines discrimination by competition with DNA strand displacement-based catalytic amplification. We show, both mathematically and experimentally, that the single nucleotide selectivity of such a system in binding to single-stranded DNA and RNA is quadratically better than discrimination due to competitive hybridization alone. As an additional benefit the integrated circuit inherits the property of amplification and provides at least 10-fold better sensitivity than standard hybridization probes. Moreover, we demonstrate how the detection mechanism can be tuned such that the detection reaction is agnostic to the position of the SNV within the target sequence. in contrast, prior strand displacement-based probes designed for kinetic discrimination are highly sensitive to position effects. We apply our system to reliably discriminate between different members of the let-7 microRNA family that differ in only a single base position. Our results demonstrate the power of systematic reaction network design to quantitatively improve biotechnology.

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.

Using RecA protein to enhance kinetic rates of DNA circuits

While DNA circuits are becoming increasingly useful as signal transducers, their utility is inhibited by their slow catalytic rate. Here, we demonstrate how RecA, a recombination enzyme that catalyzes sequence specific strand exchange, can be used to increase circuit rates up to 9-fold. We also show how the introduction of RNA into DNA circuits further controls the specificity of RecA strand exchange, improving signal-to-noise.

DNA based arithmetic function: a half adder based on DNA strand displacement

Biomolecular programming utilizes the reactions and information stored in biological molecules, such as proteins and nucleic acids, for computational purposes. DNA has proven itself an excellent candidate for building logic operating systems due to its highly predictable molecular behavior. In this work we designed and realized an XOR logic gate and an AND logic gate based on DNA strand displacement reactions. These logic gates utilize ssDNA as input and output signals. The XOR gate and the AND gate were used as building blocks for constructing a half adder logic circuit, which is a primary step in constructing a full adder, a basic arithmetic unit in computing. This work provides the field of DNA molecular programming with a potential universal arithmetic tool.

A stochastic DNA walker that traverses a microparticle surface

Molecular machines have previously been designed that are propelled by DNAzymes, protein enzymes and strand displacement. These engineered machines typically move along precisely defined one- and two-dimensional tracks. Here, we report a DNA walker that uses hybridization to drive walking on DNA-coated microparticle surfaces. Through purely DNA:DNA hybridization reactions, the nanoscale movements of the walker can lead to the generation of a single-stranded product and the subsequent immobilization of fluorescent labels on the microparticle surface. This suggests that the system could be of use in analytical and diagnostic applications, similar to how strand exchange reactions in solution have been used for transducing and quantifying signals from isothermal molecular amplification assays. The walking behaviour is robust and the walker can take more than 30 continuous steps. The traversal of an unprogrammed, inhomogeneous surface is also due entirely to autonomous decisions made by the walker, behaviour analogous to amorphous chemical reaction network computations, which have been shown to lead to pattern formation.

Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks

The ability to adapt to stimuli is a defining feature of many biological systems and critical to maintaining homeostasis. While it is well appreciated that negative feedback can be used to achieve homeostasis when networks behave deterministically, the effect of noise on their regulatory function is not understood. Here, we combine probability and control theory to develop a theory of biological regulation that explicitly takes into account the noisy nature of biochemical reactions. We introduce tools for the analysis and design of robust homeostatic circuits and propose a new regulation motif, which we call antithetic integral feedback. This motif exploits stochastic noise, allowing it to achieve precise regulation in scenarios where similar deterministic regulation fails. Specifically, antithetic integral feedback preserves the stability of the overall network, steers the population of any regulated species to a desired set point, and adapts perfectly. We suggest that this motif may be prevalent in endogenous biological circuits and useful when creating synthetic circuits.

DNAzyme-Based Logic Gate-Mediated DNA Self-Assembly

Controlling DNA self-assembly processes using rationally designed logic gates is a major goal of DNA-based nanotechnology and programming. Such controls could facilitate the hierarchical engineering of complex nanopatterns responding to various molecular triggers or inputs. Here, we demonstrate the use of a series of DNAzyme-based logic gates to control DNA tile self-assembly onto a prescribed DNA origami frame. Logic systems such as "YES," "OR," "AND," and "logic switch" are implemented based on DNAzyme-mediated tile recognition with the DNA origami frame. DNAzyme is designed to play two roles: (1) as an intermediate messenger to motivate downstream reactions and (2) as a final trigger to report fluorescent signals, enabling information relay between the DNA origami-framed tile assembly and fluorescent signaling. The results of this study demonstrate the plausibility of DNAzyme-mediated hierarchical self-assembly and provide new tools for generating dynamic and responsive self-assembly systems.

G-Quadruplexes Light up Localized DNA Circuits

DNA circuits tethered to nanoplatforms can perform cascade reactions for signal amplification. One DNA single strand activates a strand-displacement cascade generating numerous outputs, and therefore amplifying the signal. These localized circuits present, however, an important limitation: the spontaneous activation of the cascade reaction. Current methods to stabilize these circuits employ combination of protective DNA strands, which need to be removed to activate the device. This protection-deprotection process generates an important amount of unwanted side reactions. This is indeed an important limitation for the large potential application of these amplification circuits. In the present work, G-quadruplex DNA structures were used to stabilize localized DNA circuits. This new protocol generates nanoplatforms that no longer requires protective-deprotective systems and is therefore completely neutral to the sample. In addition, cations such as Pb(2+) or Ca(2+) can be also employed to activate the device enlarging the potential applications from biosensors devices to metal detector sensors.