Pattern Generation with Nucleic Acid Chemical Reaction Networks

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Pattern Generation with Nucleic Acid Chemical Reaction Networks Siyuan S. Wang and Andrew D. Ellington*

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Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, United States ABSTRACT: Biological and biochemical systems are manifestations of chemical reaction networks (CRNs). The ability to design and engineer such networks may allow the construction of artificial systems that are as complex as those seen in biology, opening the way to translational possibilities including adaptive materials. One venue for progress is the design of autonomous systems capable of pattern generation; however many synthetic CRNs, such as the Belousov−Zhabotinsky reaction, cannot be rewired to encode more complex interactions and thus lack the capacity for more detailed engineering algorithms. In contrast, DNA is an information-rich molecule with predictable and reliable base-pairing interactions and well-studied kinetics, and the use of DNA has greatly enabled the rational design of much more complex synthetic CRNs. Recent advances in the DNA computing field include circuits for pattern transformation, an example of self-organization. An arsenal of tools for designing DNA circuits to implement various CRNs has been developed by DNA nanotechnologists, including software to reliably program strand-displacement nucleic acid circuits. In addition, DNA walkers can be used to create CRNs with controlled diffusivity, while DNA gels similarly represent a new medium for implementing CRNs that may ultimately lead to the development of smart materials. As we will argue, future endeavors in nucleic acid-based pattern generation will be most greatly advanced by harnessing well-known enzymatic processes to serve as generators and amplifiers. Once nucleic acid computing tools are further developed to expedite the design process of pattern generation, we anticipate a transition from proof-of-concept curiosities to application-driven inquiries.

CONTENTS 1. 2. 3. 4.

Introduction Chemical Reaction Networks (CRNs) Chemical Computing Systems Synthetic Pattern Generation 4.1. Hybridization as a Means of Programming Chemical Computation 4.2. Applying Hybridization-Based Computation To Pattern Generation 5. Complex Tasks in Spatially Distributed Environments 5.1. Directed Motion via DNA Walkers 5.2. Nanomachines as Tools To Achieve Microscopic Tasks 5.3. Responsive Materials Controlled by Embedded Nucleic Acid Programs 6. Outlook for Nucleic Acid Pattern Generation 7. Conclusion Author Information Corresponding Author ORCID Notes Biographies Acknowledgments References

1. INTRODUCTION The discovery of reaction−diffusion (RD) nonequilibrium systems, such as the Belousov−Zhabotinsky (BZ) reaction, led to the realization that relatively simple, inorganic chemical systems were capable of sustained, nontrivial dynamics and could form patterns in space and time1,2 (Figure 1). The dynamic behavior of the oscillating BZ reaction can also be controlled in rudimentary ways, and attempts at building chemical reaction network computers using the BZ reaction have been made.3 Similarly in biology, we see examples of how evolution has programmed more complex RD systems. Numerous genetic circuits have evolved to orchestrate

A B C C D E G G I I J J K K K K K K K

Figure 1. The BZ reaction can form dynamic spiral patterns and traveling wave fronts under nonstirred reaction conditions. Adapted with permission from ref 2. Copyright 1984 Nature Publishing Group. Special Issue: Nucleic Acid Nanotechnology Received: October 10, 2018

© XXXX American Chemical Society

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developmental programs in multicellular organisms that organize functional patterns and structures based on encoded information. Alan Turing put forth a model of how a small number of morphogenschemical compounds that induce developmental changescould cause patterns to emerge over time, leading to spotted, striped, or labyrinth-like skin patterning akin to those observed on animal skins.4,5 Subsequently, Lewis Wolpert in 1969 addressed how scale-independent organization could be achieved with chemical gradients, a nonuniformity serving as a source of information.6 Experimental works have confirmed these general chemical mechanisms decades after their suggestion.7−9 Notably, structures such as skin patterning and body segmentation were originally proposed to be governed by purely chemical mechanisms; later evidence has shown that mechanical forces also play a crucial role in body plan formation.10−12 The distinction between small-molecule-based and biological CRNs is primarily the difference between the programmability of their underlying components. While most small-molecule CRNs can be tuned via only a few parameters (concentration, mixing), biological CRNs are much more broadly programmable because of the information-rich interactions and reactions that comprise them, such as small-molecule:protein interactions, protein:protein interactions, protein:nucleic acid interactions, and dynamic processes, such as transcription and translation. In order to make more complex synthetic CRNs, the DNA computation and nanotechnology communities have used the highly programmable set of Watson−Crick interactions in a nucleic acid duplex to create dynamic reactions. We review these attempts from the point of view of pattern generation, first distilling the principles behind CRNs, then seeing how they can be adapted to nucleic acids, and finally presenting an outlook on the roles nucleic acid CRNs can play in practically programming patterns.

(2)

I1 + I2 → J

(3)

Y + J → O̷

(4)

The discrete molecular counts of species X1 and X2 represent the inputs to the function implemented by the system, while the count of species Y represents the output. I1, I2, and J act as intermediates. The left-hand sides of each equation show its reactants, and the right-hand sides show the products. For instance, one molecule of X1 will produce one molecule each of I1 and Y, as indicated in reaction 1. We note that reactions 1 and 2, if isolated from reactions 3 and 4, will increase the molecular count of species Y until it represents the sum of the two inputs. The two inputs are copied to I1 and I2. In reaction 3, the molecular count of J adapts the smaller of the two input values {I1, I2}. Finally, reaction 4 facilitates the subtraction of J (the smaller of the two input values) from Y (the sum of the two input values), causing the molecular count of species Y to equal that of the larger input value. Thus, this CRN effectively computes the max function of two inputs. In the stochastic CRN (SCRN) model, any reaction can fire at any time given there are sufficient counts of reactants for that reaction, so there is no guarantee that any particular reaction occurs before another.14 However, we note that this CRN will still implement the maximum function in the limit of infinite time because no species can replicate itself (i.e., produce more copies of itself in an unbounded manner) or catalyze the production of other molecules (i.e., act as reactants in a reaction without being consumed), so only one output state is possible. In the case of this particular CRN, concentrations can in theory be substituted for molecular counts to produce an experimental implementation of the maximum function. Whether any arbitrary scheme can be expected to perform the same function using concentrations is an open question but is likely dependent upon the CRN itself. In more deeply considering the physical implementation of the CRN model, the stoichiometry of reactions in a CRN do in fact involve discrete quantities while reaction rates and concentrations are continuous and difficult to precisely control.13 It has been proven that a discrete molecular count-based model such as the SCRN model can be scaled up to a concentration-based model such as mass action kinetics.15 The CRN formalism is particularly effective for understanding how chemical reactions can be programmed to perform computation.16 The design of modular and programmable biochemical systems for achieving desirable tasks is a central goal of not only DNA computation but also synthetic biology. CRN representation can facilitate this design by abstracting biochemical systems as mathematical models with computational properties addressable independent of the details of implementation. By representing information as chemical concentrations, we can design chemical algorithms that manipulate information by prescribed reactions. CRNs carrying out sophisticated computational tasks have been proposed; in fact, it has been shown that fast and reliable Turing-universal computation is attainable using molecular counts in the SCRN model.14 The translation of robust CRNs to physical implementation can be realized through rational DNA design. The kinetics and thermodynamics of a hybridization reaction between two DNA strands is easily manipulated through base-pair changes. The increasingly efficient commercial synthesis of DNA sequences17 has powered rapid advances in synthetic and molecular biology

2. CHEMICAL REACTION NETWORKS (CRNS) Chemical systems can exhibit a variety of complex behaviors, as in the case of RD systems. These processes can be considered independently of the chemical substrates in an abstraction that allows the design of arbitrary chemical systems without demanding knowledge of the reactants first. The formalism of chemical reaction networks (CRNs) makes chemical systems, even ones without real-world counterparts, amenable to mathematical analysis and simulation. For example, by applying mass action kinetics, CRNs may be modeled by differential equations. Because chemical systems are represented simply in terms of stoichiometric reactions without attention to the structural bases of the interactions, the CRN abstraction focuses on behaviors that can arise from network topology and stoichiometry alone. This abstraction makes it possible to study chemical behaviors in a generalizable manner because these reactions are not limited by the details of their physical implementations. For example, pattern formation by a system of inorganic reactions can be compared to similar pattern formation by gene regulatory networks, and studying their CRN representations can reveal substrate-independent properties about the system’s behavior. The CRN formalism thus makes it possible to explore the space of possible chemical behaviors not yet observed naturally. An example of a CRN that performs a computation is shown as follows13 X1 → I1 + Y

X2 → I2 + Y

(1) B

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Figure 2. Chemical pattern generation by CRNs is dependent on diffusion rates, rate law (e.g., stoichiometry), and kinetic rates of reactions. There are both experimental methods and mathematical models that enable manipulation of these properties in nucleic acid-based CRNs. For instance, diffusion can be controlled by direct chemical modification of the reactant species or by changing the viscosity of the reaction medium.

much attention in the field has recently shifted toward designing circuit behaviors that unfold over time and/or across space. While chemical computers will not be able to rival the speed of electronic computers, the decentralized nature of their underlying reactions are well suited for embedding computation into materials. The construction of smart materials ultimately requires a direct chemical interface between sensing and actuation. This requirement in turn calls for scalable implementations of chemical computations in the form of CRNs or for interfaces between matter-based computations and electronic computation. To this end, efforts are well underway to understand and control the principles of pattern generation, as discussed in more detail in later sections. In recent works, these have culminated in CRNs that generate patterns in space (such as an image31−33) or dynamic patterns in time (such as an oscillation or traveling wave34−40). Beyond in vitro molecular programming systems with nucleic acids, there have been attempts to engineer pattern generation by programming synthetic genetic circuits in organisms.41−43 While the discussion of these systems is beyond the scope of this review, in vivo manipulations of transcription and translation that lead to changes in biological state may provide functional advantages over in vitro nucleic acid computingin particular, spatial control of gene expression,41,42,44 potentially followed by cell differentiation. On the other hand, genetic circuits may experience significant crosstalk due to the complexity of the systems in which they are embedded. This may prove difficult to debug and, in turn, limit scalability. In contrast, in vitro DNA computing relies on sequence orthogonality to maintain independent channels of control and in principle can produce scalable computation with low crosstalk.45 Comparatively, nonidealities are much easier to identify and consequently model in minimal systems than in cellular environments. Advances in in vitro DNA computation can strike a balance between these trade-offs and then may ultimately find an additional home in organismal genetic circuits via programmable parts such as riboswitches.46

and should likewise expand the capabilities of DNA computation. DNA strand displacement reactionsa class of reactions based on hybridizationare an effective primitive for implementing arbitrary CRNs,18 as we will demonstrate throughout our review.

3. CHEMICAL COMPUTING SYSTEMS Equipped with a mathematical formalism that standardizes the study of complex chemical systems, researchers can now begin to design CRNs capable of chemical computation. We focus here on the design of distributed computations that result in autonomous organization in space. The chemical drivers of morphogenesis are currently understood to be medium diffusivity, rate laws, and kinetic rates19,20 (Figure 2). While natural selection has as usual proved to be an excellent optimizer for tuning biological development in different species, expanding the space of possible morphogeneses requires more rational methods. Previous approaches have demonstrated control of genetic regulation in developmental pathways,21,22 but such modifications apply only to the chemistries available to organisms. In order to more broadly implement synthetic pattern-generating networks we require a readily programmable class of chemical reactions that can also promote selforganization in broader, nonbiological contexts. Nucleic acid-based in vitro circuits relying on hybridization reactions have proven useful in this regard. The field of DNA programming has come to see nucleic acids as a computing medium that encodes its information through concentration and its connections through sequence in a very different manner from its biologically intended purpose. Nucleic acid strand displacement circuits can be designed to store information and perform complex functions, taking inputs in the form of particular sequences and concentrations and generating programmed outputs that are again read as sequences and concentrations but can also be transformed into other (optical, electrochemical, mechanical) outputs. Solution-based circuits constructed with DNA alone are capable of performing scalable, complex computations,23 and circuits that additionally involve enzymatic amplification have been used in diagnostics to detect trace amounts of medically relevant biomolecules.24−26 Advances in our understanding of nucleic acid biophysics27,28 and hybridization kinetics29,30 have greatly propelled the use of DNA and RNA in nanotechnology applications, and the de novo design of many arbitrary chemical systems can now be readily carried out in the lab. Previous experimental implementations of DNA computation have been primarily focused on well-mixed systems; however,

4. SYNTHETIC PATTERN GENERATION A key challenge for bioengineers and material scientists alike is the creation of minimalistic RD systems that can be used to describe or program pattern generation. Previously proposed mechanisms focus on two RD processes that generate patternspattern transformation and pattern formation. Pattern transformation is the conversion of a pre-existing pattern into a more complex one and requires an environment with differential chemical concentrations in space (Figure 3a). C

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well characterized in the 1980s and 1990s.52−58 Most notably, SantaLucia and colleagues proposed a unified model for estimating nucleic acid thermodynamics based on the nearestneighbor model, and this in turn allowed for the de novo prediction of hybridization energetic parameters by sequence.27,28 Experimental studies, primarily relying on hyperchromism, have validated the contribution of individual base pairs to the enthalpies and entropies of hybridization. In addition to sequence identity and composition, various other factors have been found to affect nearest-neighbor parameters, including sequence length,58 oligonucleotide concentration,59 sodium concentration,59,60 and magnesium concentration.61 Studies have quantified the effects of these factors on thermodynamic parameters and presented corrections terms to earlier models, ultimately allowing for exquisite control over the design of hybridization stability. The ability to calculate hybridization strength in turn allows the prediction of nucleic acid secondary structures. Early prediction algorithms coupled nearest-neighbor energy parameters and sequence alignment to compute likely minimum free energy folds for RNA sequences.62 These advances served as the basis for some of the first nucleic acid simulation tools, which primarily focused on prediction of RNA secondary structures. For example, mfold 62 and ViennaRNA63 use these algorithms to calculate minimum free energy (MFE) structures of given sequences. Programs for predicting folding path kinetics are also available.64 Later, prediction of DNA folding became feasible with the availability of DNA energy parameters.65 These earlier pieces of software addressed the challenge of folding, which primarily concerns the prediction of secondary structures adopted by single-stranded polymers. New biotechnology (e.g., PCR) and diagnostic assays (e.g., SNP detection) necessitated methods for predicting multistranded interactions. A first algorithm for the calculation of the partition function of multistranded complexes was demonstrated by Dirks and colleagues in 2007.66 This probabilistic treatment of minimum free energy secondary structures was then applied to a more versatile secondary structure prediction software package, NUPACK,67 which had utility well beyond secondary structure prediction and served as a tool for prototyping DNA circuits. Prediction and analysis of both multistranded structures and multistate systems are within the capabilities of NUPACK, as will be addressed later. While these advances in prediction have proved a boon for designing static structures, such as DNA origami,49 kinetics must also be considered in order to design, test, and analyze dynamic systems. Similar to the equilibrium-associated hybridization free energy, hybridization kinetics are also sequence and length dependent, making the prediction of binding dynamics likewise tractable. Extensive work has been done on the physics of DNA hybridization, resulting in a widely accepted model involving a nucleation event (initial contact by single bases) that, once stabilized, enables zippering (consecutive bonding of remaining unpaired bases) of the full complementary region.68 With a deeper understanding of how pairing and unpairing can sequentially occur, researchers could delve into a particular class of such reactions: nucleic acid strand displacement reactions (Figure 4). The key insight that toehold-mediated strand displacement could be adapted to programmable DNA machines was first demonstrated by Yurke and colleagues through molecular tweezers that were driven to open or close by strand displacement reactions.69 Cascading subsequent toehold strand displacement reactions were soon shown to experimentally approximate logical operations, forming the basis for DNA

For example, the French Flag model proposed by Wolpert serves as a classic example of pattern transformation.6 Pattern

Figure 3. Pattern generation by autonomous chemical systems can be broadly classified into pattern transformation and pattern formation. (a) Pattern transformation is the conversion of an initial pattern into a different pattern by reactions that process the initial image. (b) Pattern formation is the emergence of regular patterns from an initial uniform configuration that is unstable. Stochasticity perturbs the uniform configuration, leading to heterogeneous forms that persist through short-range activation and long-range inhibition.

formation, as described by Turing, is the creation of an inhomogeneous pattern from otherwise uniform starting conditions (Figure 3), a process that involves symmetry breaking.4,47 Studies of animal skin coloration have suggested that underlying molecular mechanisms similar to the reaction− diffusion mechanism proposed by Turing7,8,48 are responsible for some cases of pattern formation observed in biology. These biological examples rely on morphogens that exhibit both longrange inhibition and short-range activation.5 The properties of the pattern formed depend on the rates of diffusion and reaction of the morphogens, and many qualitatively different emergent patterns have been identified.20 Examples of both types of pattern generation occur in nature and are at root nonequilibrium self-organization processes. Synthetic examples of pattern transformation can occur in closed systems, although only transient patterns will be produced, while actual pattern formation will always require an external source of reactant species. For the purposes of this review, we draw a distinction between the self-organization process of pattern generation, which leads to nonequilibrium steady states, and static selfassembly that is observed in many DNA nanotechnology applications (such as DNA origami49 ), which leads to thermodynamic equilibrium.50 In the following subsections, we discuss the application of DNA hybridization reactions to chemical computing, the progress made in designing hybridization-based systems for pattern transformation, and the prospect of such systems in synthetically recreating de novo pattern formation. 4.1. Hybridization as a Means of Programming Chemical Computation

Nucleic acids have the most predictable behavior of all biopolymers thanks to Watson−Crick base pairing.51 The thermodynamic stability of DNA and RNA hybridization was D

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while the predictability of hybridization equilibria and dynamics allowed validation of those designs with computational tools. Some of the most popular packages for designing in vitro nucleic acid circuits are web applications such as NUPACK67 and VisualDSD.76 NUPACK provides a broad range of utilities for both design of reaction pathways and analysis of user-provided sequences, especially for the sequence design of strands which are involved in multiple complexes at different points in the course of a nucleic acid CRN’s program.77 Because all reactants are present in the same test tube, the sequence designs of these strands must necessarily encode an order to all interactions in the system in order to orchestrate the evolution of the circuit. Given a pathway design in the form of desired complexes in one test tube, NUPACK then returns optimized sequence designs found through random walks in the sequence space. As another tool used for the design of nucleic acid circuits, VisualDSD additionally provides both stochastic and deterministic simulations of DNA CRNs for prototyping the dynamics of DNA devices. The VisualDSD language abstracts DNA devices to the domain level, and users can specify all reactions and rates used in the network. Outputs include plots of species concentrations over time and diagrams of expected intermolecular hybridization and displacement reactions. With these tools in hand, the design of CRNs becomes a true possibility both in theory and in experiment.

Figure 4. Nucleic acid strand displacement reactions used to implement CRNs. (a) Toehold-mediated strand displacement describes the substitution of a previously bound strand by a competing strand with greater complementarity. Note that “C” can no longer bind back to strand I as no toeholds are available, so this reaction is irreversible. (b) Toehold exchange describes the replacement of one available toehold with another. Because a new toehold becomes uncovered, the reaction may be reversed or the “gate” complex may interact with yet another strand complementary to the second toehold.

4.2. Applying Hybridization-Based Computation To Pattern Generation

Pattern generation with nucleic acid CRNs requires that the overall behavior of a simple RD system be determined via individual components’ diffusion coefficients, reaction rates, and rate laws. With the theoretical basis and computational tools now available, two of these three properties (reaction rates and rate laws) can be readily tuned in a DNA-based RD system through reaction design and the design of the sequences of individual network components. Further control will require the ability to predict diffusion, a variable not often addressed in prediction tools. Experimentally, diffusivity of components through the medium can be altered, for example, by adding viscous reagents to the medium or by directly changing the component. The diffusion coefficient of DNA has also been shown to scale polynomially with the length of the strand,78 providing one method for differentially slowing down reactants. Indirect changes to a molecule’s travel speed are possible, for example, by including specific hybridization domains such that these domains bind to immobilized strands in the medium, resulting in a decrease in effective diffusivity. Nonetheless, even in the absence of design tools with built-in diffusion, it has proven possible to readily program pattern generation using strand displacement and exchange kinetics.29 A number of works have already begun to explore pattern transformation. Chirieleison and colleagues demonstrated a 2D DNA circuit that uses feedforward circuitry to imprint and further process a UV-induced pattern.31 A key component of this circuitry was a DNA strand attached by a photocleavable linker that is released upon irradiation and diffuses to the surrounding area. This initiates a cascade of toehold strand exchange reactions that ultimately produces fluorescent signal but only at the edge of the imprinted image. In another example, Gines and colleagues developed a DNA-based circuit based on enzyme- (rather than light-) mediated creation and destruction of oligonucleotides. The components of the DNA circuit were localized on microbeads.38 These individual beads or “agents”

nanotechnology and DNA computing to this day.18,70−72 Later works by Qian and Winfree elegantly generalized the use of toehold strand displacement reactions to many computational operations, including boolean logic,23 neural network classification,73 and winner-take-all classification.74 Strand displacement reactions can be initiated by a toehold or a short unbound region usually 4−8 nucleotides in length that enables binding to occur. In a toehold strand displacement reaction, the exposed single-stranded toehold of a hemiduplex initiates interactions with a complementary strand, which can then strand exchange with the original duplex partner strand. Hybridization between the unbound region and a new complementary partner initiates the process, and the new partner displaces the previously bound partner in a mechanism termed branch migration. Finally, the previous partner is fully displaced by the new partner (Figure 4a). Toehold exchange can be thought of as a potentially reversible variation on toehold strand displacement. It involves a three-step hybridization process in which a previously unbound strand associates with the exposed toehold of a partially bound strand (toehold binding), competes with the previously bound complementary strand (branch migration), and finally replaces the previously bound complement when the complement spontaneously dissociates (strand displacement), resulting in the occlusion of the original toehold and the exposure of another toehold, potentially one with a different sequence (Figure 4b).75 Zhang and Winfree quantified how toehold exchange reaction rates can be controlled by relative lengths of the first and second toeholds and have additionally shown, remarkably, that the dependence of toehold exchange and strand displacement rates on toehold length spans across 6 orders of magnitude.29 More recent works have sought to predict DNA hybridization kinetics by sequence.30 Toehold exchange reactions allowed the ready design of increasingly complex DNA- or RNA-based reactions, E

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advances to the creation of discrete patterns, Scalise and Schulman introduced another RD program that could emulate the behavior of a cellular automaton: given a starting state, each cell would evolve over time according to the programmed rules of the system.84 To approximate this behavior, Scalise and Schulman suggested the use of spacers, whichat the cost of blurring boundaries between cellsserved to both discretize the cells themselves and prevent unwanted interference with logical operations in neighboring cells. These theoretical and computational insights into RD programs for pattern transformation at once put the relatively early experimental progress into context while blazing new paths for implementation into the future. That said, system-level tools for programming and analyzing nucleic acid spatial patterning have not yet been developed. This is likely due to the lack of a generally accepted method for carrying out pattern transformations with DNA. In contrast, there are many available resources for prototyping extremely complex, static, self-assembling DNA structures, such as those produced via DNA origami.85−88 However, a more universal framework for programming pattern transformation can be envisionedfor example, the experimental implementation of generalizable blur or sharpen modules for pattern processing. Such modules, along with an improved understanding of how to control RD mechanisms, should lead to the adoption of similar design tools for nucleic acid self-organization. True de novo pattern formation is defined as the emergence of stationary, heterogeneous structures from uniform initial conditions. While pattern formation has been experimentally demonstrated and studied with reaction systems involving inorganic, small-molecule reactants (such as the chlorite− iodide−malonic acid reaction89,90) and efforts have been made toward engineering stochastic Turing pattern formation in synthetic bacterial populations,91 nucleic acid systems for pattern formation have not been concretely proposed or implemented in the lab. However, tuning sophisticated reaction components (such as inorganic compounds or protein morphogens) proves a very complex problem outside of highly programmable nucleic acid-based systems. Thus, programmable synthetic pattern formation will likely necessitate the use of predictable polymer components such as DNA. For a uniform concentration to develop persistent patterns, it must have a mechanism to disrupt the uniform distribution of chemical species and break symmetry. This can occur if the initial homogeneous configuration is unstable, and small spatial perturbations are amplified until the concentrations of the species become globally heterogeneous. Through a nonequilibrium process that involves some reactant species to be constantly fed in, the system ultimately converges to a heterogeneous pattern. Notably, the patterns that emerge in this process are not dependent on the initial spatial variations (as is the case with pattern transformation) but are instead largely governed by properties intrinsic to the system, such as rates of reaction and diffusion. In the context of biology, diffusiondependent instability (also known as Turing instability) is one well-studied form of instability that can lead to stable pattern formation. We focus here on stationary patterns formed through Turing instability rather than oscillatory patterns formed in BZ reactions and other chemical oscillators, which are not formed by the definition of Turing instability argued by some.92 RD systems exhibiting diffusion-dependent instability are stable at the uniform steady state when diffusion is absent (i.e., small spatial perturbations return back to the homogeneous state) but become unstable when diffusion is present (i.e., perturbations

could communicate between each other through enzymatically produced, diffusible sequencesone agent could produce a sequence that would then trigger a switch in a distant agent, potentially changing its ability to create a similar diffusible sequence. Through the collective action of multiple agents, traveling waves were carried across distances that were several orders of magnitude larger than the agents themselves. When the same DNA-based circuit exhibiting oscillating predator−prey relationships was implemented under nonstirred conditions, a remarkable variety of pattern transformations proved possible, including long-lived spiral waves in 2D,35 and even more complex wave fronts in systems with more varied agent geometries.38 In another instantiation, this system was used to create a French Flag pattern.33 Unlike other systems where the output was purely optical, in this latter example beads aggregated in regions where target strand concentrations exceeded a threshold, thus actuating a physical change and setting the stage for smart materials that regulate their own form. A variety of additional methods also exist for initiating and controlling pattern generation. Lithography presents one approach for constraining the distribution of individual components. For example, Tayar and colleagues used chemical photolithography to produce chips with gene-encoding DNA sequences in compartments that were further connected via a network of capillaries.79 These chips could be thought of as artificial cells containing cell-free gene networks and were programmed to exhibit interesting collective oscillatory behaviors.39 Controlling the spread and development of initial patterns can be achieved through (1) altering the composition or chemistry of reactants to change diffusivity, (2) changing the size and speed of diffusion via sequence-dependent hybridization,80,81 and (3) changing the diffusivity of the medium itself (most often by embedding reactions in gels). As an example of how these methods can play out in actual pattern-forming systems, Zadorin and colleagues were able to control the development of a traveling wave in 1D by including a hydrophobic 3′ cholesterol moiety on reactants, thereby introducing hydrodynamic drag in the presence of Triton X100 micelles.34 Beyond creating transient patterns, the development of sustained patterns is a particularly interesting challenge. The emergence of persistent patterns in a dissipative system is a hallmark of self-organization and requires a constant input of reactant species. Thus, one approach to improve pattern stability (up to 100+ hours) is to provide “infinite” fuel molecules in a liquid reservoir surrounded by a gel medium.82 While experimentalists have been successful in demonstrating pattern transformation, theory can provide useful insights into what may yet be achievable. Such theoretical explorations have so far considered DNA-only systems. Dalchau, Seelig, and Phillips presented a computational method for programming spatial organization of patterns at the centimeter scale using DNA strand displacement alone83 and additionally used VisualDSD to simulate spatial behaviors, such as traveling waves and consensus networks in both 1D and 2D. In these systems, starting conditions involved either a single source point or randomly distributed inputs. In contrast to these simple starting conditions, Scalise and Schulman proposed a RD program that employed modular components for sequentially processing a starting image and performing arbitrary pattern transformations in both 1D and 2D.32 The modules carried out processes including COPY, AND, and NOT, turning an initially simple pattern into a far more complex one. Extending these F

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become amplified).4,19,47 Necessary conditions for Turing instability in specific two-species systems have been analytically solved, and system parameters can be used to predict the scale and spatial frequency of periodic patterns that can arise. For several specific RD systems, various assortments of emergent spatial patterns have been identified using numerical methods.20 To achieve de novo pattern formation through Turing instability in a programmable nucleic acid system, several hurdles must be overcome. Because linear stability analysis is irreversible, CRNs exhibiting Turing instability cannot be reverse engineered, thereby emphasizing the importance of known examples. Many such examples involve bimolecular or trimolecular autocatalytic reactions.93,94 This presents an interesting challenge to the rational design of strand displacement reactions but certainly not an impossible one. An example of bimolecular autocatalysis, the predator−prey system, has been thoroughly demonstrated in various experimental settings since its initial implementation as a DNA−enzyme system by Fujii and Rondelez.95 Additionally, mathematical studies have determined that reacting species must have different diffusivities to exhibit Turing instability.19,92 Experimentally, sequencedependent81 or size-dependent31,78 diffusion provides methods by which relative diffusion rates can be controlled. On the basis of current designs and implementations of programmable CRNs, one could envision a path forward in rationally designing pattern formation molecular programs. We emphasize that the current goal for nucleic acid-based pattern formation is the demonstration of symmetry breaking, which is a key step in the emergence of stationary spatial patterns. Once instability of the homogeneous state is achieved in nucleic acid systems, additional mechanisms for further specifying particular patterns become available, namely, pattern transformation. Pattern transformation alone is both powerful and highly applicable; for example, it plays a central role in the development of multicellular organisms. It can be readily seen that a thorough understanding of pattern transformation principles, even without methods for pattern formation, has yielded numerous opportunities for thinking about and making self-organizing materials.31,32,41,42 With de novo pattern formation potentially on the horizon and pattern transformation increasingly illuminated, DNA nanotechnology may one day unfold molecular organization to meso- and macroscales that are largely inaccessible or intractable with designed self-assembly approaches, such as DNA origami.

a DNA complex or a ssDNA that contains complementary regions for both fuel and foothold sequences. As the field of DNA walkers grew, so did the variety of walkers.96 To produce the repetitive, cyclic reactions that propel themselves, walkers typically rely on energetic changes brought about by hybridization,97,98 enzyme-catalyzed reactions,99 changes in their environments,100 and light (azobenzene isomerization that forces melting of DNA duplexes101), all of which lead to programmed conformational changes. Early bipedal walkers that relied on strand displacement reactions required the consecutive addition of specific fuel strands to activate each step97 (Figure 5), sometimes with programmed preferences for a particular path choice,102 while later walkers are more autonomous and rely on fuel strands supplied in excess to operate.103,104 Speed and processivity are obviously desirable

Figure 5. “Stepper” style DNA walkers.97 These walkers require the sequential addition of helper strands to the reaction environment to take the next step on the molecular track.

traits for walker systems, and techniques to improve these properties have been suggested.105,106 The relative autonomy of movement is also constrained in large part by the nature of the tracks themselves. Prescriptive landscapes predetermine the route of the walker and can be constructed from linear DNA,99,107 DNA origami,108,109 or carbon nanotubes.110 In contrast, autonomy can be achieved by allowing the walker to choose its own track. For example, socalled molecular spiders have catalytically active leg strands and can march through a hydrogel matrix containing abundant substrates, stochastically choosing each new footstep, resulting in an overall path that mimicks diffusion (a random walk based on the catalytic activity and number of legs).104 By letting walkers choose their own paths, it has proven possible to move over not only 2D but also randomized or disordered 3D surfaces.103 Some combination of choice and programming can be achieved by generating tracks that present substrates within a restricted area,108 or by including a “key” strand on the walker

5. COMPLEX TASKS IN SPATIALLY DISTRIBUTED ENVIRONMENTS Having shown that pattern generation is possible, what would one do with such patterns? Self-organized nucleic acid landscapes could serve as a platform for nanomachines that perform a variety of functions. Current progress in nucleic acidbased nanodevices and responsive materials may facilitate the realization of pattern transformation and pattern formation principles in materials beyond nucleic acids. 5.1. Directed Motion via DNA Walkers

Inspired by the autonomous active transport of motor proteins, such as kinesin and dynein, researchers have designed and built so-called DNA walkers: DNA-based devices that traverse nanoscale surfaces through hybridization with footholds present on the surface. Many walkers operate autonomously and behave in a stochastic manner. The walkers themselves are usually either G

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Figure 6. Autonomous DNA walker that can sort molecular cargo.113 Cargo molecules (i.e., DNA strands with covalently attached fluorophores) are initially randomly distributed throughout the environment at cargo posts. Walkers randomly traverse the track surface and pick up cargos if empty handed and near a post with cargo. When a walker carrying cargo comes near the appropriate goal for its cargo, it drops off the cargo at that location. Over time the walker organizes cargo molecules by type (inset).

that unlocks only particular substrates, resulting in an encoded choice of direction during walking.102 While the demonstration of walking is in and of itself a remarkable feat, researchers have also begun to apply the use of DNA walkers to address molecular-level challenges. For example, a DNA walker in a nanoscale assembly line facilitated a multistep organic synthesis reaction autonomously, quickly, and efficiently, with all reactants in one solution.111 The reagents, amino acid NHS esters, were conjugated to tether strands consisting of a both ribose and deoxyribose backbones and were assembled along a DNA track. In successive steps, the walker catalyzed the polymerization of the NHS esters and finished at the end of the track with its product. Walkers have also been shown to carry cargoes and transport them in an orderly fashion akin to an assembly line.112 In contrast to this ordered transport, Thubagere and colleagues demonstrated a sorting DNA walker that picks up different molecular cargo (oligonucleotides with attached fluorophores) and randomly searches out defined destinations to drop them off113 (Figure 6). Walker systems have also been used for exploring plasmonics. A gold nanorod-based walker released on a DNA origami track yielded an active plasmonic system that could be visualized with nanometer resolution.114 Molecular walkers may prove useful tools for molecular computation by constructing CRNs in which the walkers interact with a surface, and the positions of the walkers, rather than the positions of the cargoes they carry, are used to store and transform information. Localizing circuit parts to specific locations not only enables parallel computation but also speeds up desirable reactions and prevents spurious ones. Early examples of surface DNA computation described operational cycles in which enzymes were used to mark, unmark, create, and

destroy surface-attached strands; more modern methods employ tethered circuit components that can be released by strand exchange reactions. However, because bimolecular double-stranded gates of necessity release one of the strands upon reaction, surface computations are more amenable to hairpins or multistranded complexes that would allow components to remain bound after interactions.115−117 For instance, Qian and Winfree proposed a theoretical framework that uses four-way branch migration reactions to convert from one double-stranded complex to another. Because these complexes involve strands extending from a DNA origami surface, interactions between two adjacent complexes result in a change of state for those complexes but do not release them into solution. This approach can be used to implement unimolecular and bimolecular reactions on a surface for parallel and scalable CRN computation, which in turn can potentially be used to construct a Turing machine or even produce dynamic spatial patterns.118 Building off of the hairpin architecture presented by Muscat and colleagues, Chatterjee and colleagues experimentally demonstrated a modular “DNA Domino” system in which logic gates and signal transmission wires are localized to a DNA origami scaffold and signals are propagated as growing assemblies of strands. The authors performed complex logic operations with circuits that could take up to six inputs, demonstrating an architecture where the number of unique strands used scaled by the number of inputs or outputs desired.119 Given the state of progress with DNA walkers and the possibility of using them as agents in surface computation, it may now be possible to use them as the components of a distributed CRN that carries out pattern transformation. The computational potential of walkersfor example, their capacity for H

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arbitrary logical operations120 or their ability to mimic a universal Turing machine121has been assessed theoretically, and one could imagine that walking mechanisms would provide a way to either control or guide movement within a circuit or system, similar to the control of diffusion via size78 or hybridization81 described above. Thus, the potential for CRNs based on programmable “diffusion” should also exist, and this might facilitate the process of self-organization or result in the translation of tracks into more permanent patterns or morphological features.

5.3. Responsive Materials Controlled by Embedded Nucleic Acid Programs

Using pattern-generating DNA programs to drive structure formation might allow the production of autonomous, responsive, “smart” materials that could be programmed across multiple length scales, from the nanoscale to the macroscopic level. A variety of DNA-based materials have been engineered for different functions ranging from manipulation of nanoparticles to cell-free protein production.137 As one example, the coupling of DNA hybridization and folding reactions to hydrogel structure presages how soft robotics may eventually be programmed with materials such as DNA.138 The Luo lab has made remarkable progress in the development of all-DNA hydrogels, assembled either through covalent chemical crosslinks or via hydrogen bonding and van der Waals forces.139 Multistranded DNA structural motifs were connected into higher order structures using DNA ligase140 and have proved to be surprisingly efficient templates for protein expression.141 Lee and colleagues used rolling circle amplification and multiprimed chain amplification to form a hybridization-based material with interesting physical traits.142 When gold nanoparticles were included, the DNA gel was used as a switch for electrical conductivity. Another remarkable example of DNA-functionalized materials is the DNA “cytoskeleton” developed by Kurokawa and colleagues, in which spherical 40 μm diameter nanostructures structurally supported the membranes of artificial cells, with potential applications in drug delivery.143 The programmability of nucleic acids combined with the possibility of their incorporation into materials allows for the production of soft materials with a variety of interesting functions. Embedded nucleic acid-based regulation can potentially be used to switch between different physical states in programmable materials. In particular, control of phase changes and shape shifting in nucleic acid-functionalized acrylamide copolymers has been demonstrated. The Willner group used DNA duplexes, triplexes, quadruplexes, or other motifs to drive acrylamide cross-linking, and the resultant gels are capable of switching between an amorphous quasi-liquid state and a structured solid state that can be permanently encoded into the material (shape memory). Environmental triggers such as the addition of ions144 or pH changes145 can lead the material to recall its original shape from the quasi-liquid state by causing conformational changes in the cross-linked strands, and two shape memories can even be encoded at a time.146 Notably, DNA strand displacement can be used to trigger these conformational (and consequently phase) changes,147 which encourages further progress combining strand displacementbased DNA computation and soft material actuation. Taking a step toward self-regulating soft materials, Heinen and colleagues presented a pH-dependent DNA hydrogel system that exhibited delayed response in phase change as a consequence of its programming.148 Two antagonistic enzymesurease and esterase−autonomously altered the pH of the hydrogel environment, and over the course of a staggered pH cycle, the i-motif-based hydrogel switches from solution to gel and back to solution. In contrast to the previous examples that show memory of preprogrammed structures following state change, the Schulman lab provided a seminal example of a nonmolded hydrogel that grows by 2 orders of magnitude based on the hybridization chain reaction149 and input strand concentrations.150 Adding terminating hairpins that lack hybridization domains leads the

5.2. Nanomachines as Tools To Achieve Microscopic Tasks

In the context of DNA nanomachines, DNA walkers are just one representative sample, albeit a well-studied example with particularly high potential for applications in pattern generation. Beyond devices to facilitate molecular transport, an assortment of DNA nanomachines has the capability to operate via programmed CRNs. Common properties of DNA machines include a requirement for “fuel” molecules that drive the mechanical action, the consequent production of “waste” molecules upon consuming fuel, and the use of fuel-recycling molecules and strategies to cycle through the steps of operation.122 Early machines functioned as various molecular robots resembling tweezers, gears, and cranes among other things.69,123−125 Many mechanisms have been developed to drive the activities of these machines, varying from DNA strand displacement97,98 to pH,126,127 light,128 and ligands in the case of aptamer-based devices.129 Additionally, Kopperger and colleagues built a DNA origami-based nanoscale robot arm that relies on electricity rather than chemical interactions or optical stimuli to direct its motion. This arm can be precisely manipulated through computer-controlled, externally applied electrical fields to point in arbitrary directions.125 Such a device might eventually be used to drive organization of nanoscale particles through electricity. More generally, the functionality of DNA nanomachines hinges on their input and output. Aside from the mechanisms mentioned above, some interesting uses of non-nucleic acid inputs have been demonstrated recently. Engelen and colleagues have shown that antibodies can serve as inputs for DNA-based molecular computing by promoting strand displacement reactions.130 Song and colleagues used electric fields to induce hybridization for bit operation via electrode addressable arrays.131 Light-sensitive modifications to oligonucleotide strands such as azobenzene and photolabile groups can lead to conditional hybridization132 and cleavage of one strand into two,31 respectively. Promising examples of functional outputs have been reported as well. For instance, DNAzyme therapeutics use functional nucleic acids to catalyze reactions in the presence of specific nucleic acid targets;133 similarly, DNAzymes can be incorporated into controllable nanodevices whose operation depends on target strands as inputs.134 Given the current state of electrochemical and optical detection techniques, nucleic acid outputs can be converted to electronic or photonic signals. Similarly, highly specific and highly sensitive electrochemical sensors for nucleic acid detection (and thus for circuit outputs) have been developed.135,136 Optical detection by fluorescence or chemiluminescence is also widely used in DNA detection.136 Finally, as we will address in the next section, DNA computations can be used to actuate mechanical processes.33,125 Coupling strand exchange reactions to changes in molecular connectivities within materials may one day allow the programming of soft material properties at the macroscale. I

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DNA circuit to distinguish between viral and bacterial respiratory infections or early cancer biomarkers. In the context of synthetic biology, yet another practical direction for pattern classification, researchers have successfully coupled strand displacement-based circuits to the regulation of gene expression in living organisms.46 Green and colleagues have shown gene expression systems governed by in vivo complex logic computation of up to six inputs, demonstrating programmed responses to patterns in input RNA concentrations.154 As previous systems have been built to interpret patterns in strand concentrations, the development of circuits and materials that respond to patterns in space will be an interesting and exciting goal for the near future. Beyond this horizon, the next opportunities will likely come from CRNs and materials that can generate new, informationrich strands as part of their operation. The overall space of programmable self-organizing systems would be drastically expanded by taking advantage of enzyme-mediated transcription and/or replication. An analogy is that nucleic acid-based pattern-generating programs are the information-encoding software in molecular devices, while enzymes or conjugated materials are the hardware that produce physical outputs. Coupling the two would allow opportunities to achieve functional applications downstream, possibly through in vitro protein expression. For instance, the heterogeneities formed by a circuit can lead to differential transcription and, consequently, differential translation of target proteins across space, similar to artificial cells with minimal gene expression systems.79 Transcription in such generative programs could be programmed using the same rules of hybridization as those currently used in nucleic acid circuits. For example, programmable “promoters” that use nucleic acid inputs to enable or prohibit transcription of different nucleic acid outputs have been demonstrated and have been used to make bistable switches,155 oscillators,156 and logical cascades157 and in principle could be used to make even more complex winner-take-all computations.158 Ultimately, the use of generative CRNs and materials should allow sustained pattern generation, just as in living systems. As a system evolves over time, it must be continually fueled to maintain structured, far-from-equilibrium states. While fuel strands can be embedded in gels and diffusion-released or periodically spiked into liquid reservoirs,82 the use of mononucleotides as fuels, rather than complex, pre-synthesized strands, is the more reliable solution. The DNA-based predator−prey systems by Rondelez and co-workers are beautiful examples of this concept in action and have already been shown to work robustly in many situations.33,38,95 Continued explorations of sustained nucleic acid-based pattern generation, especially with functionally modified strands,159 will almost inevitably result in experiments, theory, and algorithms that can bridge the gaps between top-down and bottom-up material engineering strategies.

gel to cease growing, and tuning the ratio between growth and terminating hairpins gives precise control over swelling. By layering gel materials that could be triggered by orthogonal DNAs, Cangialosi and colleagues were then able to selectively trigger shape change in different parts of a hydrogel structure, ultimately leading to a “crab” that could wave its claws, feet, or eyestalks differentially. Recent work from the Schulman lab has further demonstrated that this assembly-based swelling mechanism can be triggered by even small concentrations of catalyst strands.151 Gels could swell in response to catalyst strands at a concentration of 100 nM, which is on the order of most components used in DNA strand displacement circuits, making these hydrogels directly applicable to strand displacement cascades, with CRN interfacing becoming a real possibility. Soft materials like hydrogels are not the only materials that may be physically actuated by DNA hybridization. Shim and colleagues have shown that thin films created through the deposition of DNA-grafted gold nanoparticles are capable of various mechanochemical states driven by DNA strand exchange.152 In the case of these materials, the different morphologies (sizes and shapes) attainable by the bending film and hydrogel depend on the quantitative concentration of reactive strands. Because the available states are not preprogrammed into the materials with molds, a continuum of states (rather than a finite number of discrete states) is available. The opportunities for programming are manifest not only from Schulman’s work but also from demonstrations that (i) strand exchange reactions can induce phase transitions in gels,147 (ii) gelation can impact electrical conductivity,142 and (iii) protein expression within gels is possible.141 Ultimately, having CRNs that can both build off of imprinted patterns (similar to how walkers traverse surfaces) and create material states (similar to the soft hydrogel robots) could foment entirely new types of pattern generation programs, driving materials to take on specific shapes autonomously. Further, it may be possible to design materials that exhibit feedback between chemical and physical properties. Together, these efforts hint at a potential class of smart, restorative materials, of which self-healing materials are but one possibility. Connecting these materials with pattern-generating CRNs could open up possibilities for self-driven structural changes that prove as broad in engineered materials as in their biological counterparts.

6. OUTLOOK FOR NUCLEIC ACID PATTERN GENERATION The advances already described greatly expand the set of complicated tasks achievable at the molecular scale. DNA walkers may provide new insights into how to construct spatially distributed CRNs, materials with embedded nucleic acids could provide a link between a chemical patterned output and a physical state, and DNA nanomachines could greatly diversify both the functionalities of resulting patterns and the types of input signals that can communicate with a nucleic acid program. These endeavors may ultimately allow chemical concentrations to smoothly interface with optical, physical, or electrical signals, leading to programmable patterns for practical applications.3 DNA diagnostics is one practical direction that could benefit from molecular pattern classification or the categorization of combinatorial inputs of multiple target biochemical species. Recent results from Lopez and colleagues have shown promising progress in classifying multigene expression for disease diagnostics153 by weighing RNA inputs using an in silico trained

7. CONCLUSION Examples of pattern generation in natural phenomena have inspired the design of artificial systems capable of autonomous organization in space. DNA rational design is a particularly wellsuited solution to the challenge of programming pattern generation, if not the only tractable current solution. Progress in DNA nanotechnology has demonstrated the utility of nucleic acids as a programmable building material, and prototyping software has expedited the application of thermodynamic and kinetic models of nucleic acid hybridization to rationally J

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(5) Kondo, S.; Miura, T. Reaction-diffusion model as a framework for understanding biological pattern formation. Science 2010, 329, 1616− 1620. (6) Wolpert, L. Positional information and the spatial pattern of cellular differentiation. J. Theor. Biol. 1969, 25, 1−47. (7) Kondo, S.; Asai, R. A reaction-diffusion wave on the skin of the marine angelfish Pomacanthus. Nature 1995, 376, 765. (8) Asai, R.; Taguchi, E.; Kume, Y.; Saito, M.; Kondo, S. Zebrafish leopard gene as a component of the putative reaction-diffusion system. Mech. Dev. 1999, 89, 87−92. (9) Nüsslein-Volhard, C.; Wieschaus, E. Mutations affecting segment number and polarity in Drosophila. Nature 1980, 287, 795. (10) Mercker, M.; Brinkmann, F.; Marciniak-Czochra, A.; Richter, T. Beyond Turing: mechanochemical pattern formation in biological tissues. Biol. Direct 2016, 11, 22. (11) Howard, J.; Grill, S. W.; Bois, J. S. Turing’s next steps: the mechanochemical basis of morphogenesis. Nat. Rev. Mol. Cell Biol. 2011, 12, 392. (12) Mayer, M.; Depken, M.; Bois, J. S.; Jülicher, F.; Grill, S. W. Anisotropies in cortical tension reveal the physical basis of polarizing cortical flows. Nature 2010, 467, 617. (13) Chen, H. L.; Doty, D.; Soloveichik, D. Deterministic function computation with chemical reaction networks. Nat. Comput. 2014, 13, 517−534. (14) Soloveichik, D.; Cook, M.; Winfree, E.; Bruck, J. Computation with finite stochastic chemical reaction networks. Nat. Comput. 2008, 7, 615−633. (15) Kurtz, T. G. The relationship between stochastic and deterministic models for chemical reactions. J. Chem. Phys. 1972, 57, 2976−2978. (16) Arkin, A.; Ross, J. Computational functions in biochemical reaction networks. Biophys. J. 1994, 67, 560−578. (17) Carlson, R. The changing economics of DNA synthesis. Nat. Biotechnol. 2009, 27, 1091. (18) Soloveichik, D.; Seelig, G.; Winfree, E. DNA as a universal substrate for chemical kinetics. Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 5393−5398. (19) Murray, J. D. Mathematical biology. II Spatial models and biomedical applications {Interdisciplinary Applied Mathematics V. 18}, 3rd ed.; Springer-Verlag New York Incorporated: New York, 2001. (20) Pearson, J. E. Complex patterns in a simple system. Science 1993, 261, 189−192. (21) Li, P.; Markson, J. S.; Wang, S.; Chen, S.; Vachharajani, V.; Elowitz, M. B. Morphogen gradient reconstitution reveals Hedgehog pathway design principles. Science 2018, 360, No. 543. (22) Sekine, R.; Shibata, T.; Ebisuya, M. Synthetic mammalian pattern formation driven by differential diffusivity of Nodal and Lefty. Nat. Commun. 2018, 9, 5456. (23) Qian, L.; Winfree, E. Scaling up digital circuit computation with DNA strand displacement cascades. Science 2011, 332, 1196−1201. (24) Wu, L. R.; Chen, S. X.; Wu, Y.; Patel, A. A.; Zhang, D. Y. Multiplexed enrichment of rare DNA variants via sequence-selective and temperature-robust amplification. Nat. Biomed. Eng. 2017, 1, 714. (25) Bhadra, S.; Jiang, Y. S.; Kumar, M. R.; Johnson, R. F.; Hensley, L. E.; Ellington, A. D. Real-time sequence-validated loop-mediated isothermal amplification assays for detection of Middle East respiratory syndrome coronavirus (MERS-CoV). PLoS One 2015, 10, No. e0123126. (26) Bhadra, S.; Riedel, T. E.; Saldaña, M. A.; Hegde, S.; Pederson, N.; Hughes, G. L.; Ellington, A. D. Direct nucleic acid analysis of mosquitoes for high fidelity species identification and detection of Wolbachia using a cellphone. PLoS Neglected Trop. Dis. 2018, 12, e0006671. (27) SantaLucia, J. A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proc. Natl. Acad. Sci. U. S. A. 1998, 95, 1460−1465. (28) Xia, T.; SantaLucia, J., Jr; Burkard, M. E.; Kierzek, R.; Schroeder, S. J.; Jiao, X.; Cox, C.; Turner, D. H. Thermodynamic parameters for an

designed systems, enabling systematic construction of DNAbased circuitry. Recent works have presented DNA circuits capable of pattern transformation, and efforts in associated intellectual directions have suggested new mechanisms for linking self-organizing DNA circuits to physical actuation or optical output. This progress toward precise control of selforganizing systems could lead to new mechanisms for detection in diagnostic systems or environmental responses in smart materials. We expect that, to attain these futuristic goals, the immediate next steps in the field will involve the incorporation of enzymes familiar to DNA computing (i.e., polymerases) into the pattern generation programming repertoire. Concurrent to these developments, we anticipate the rise of dedicated computational tools for facilitating programming of in vitro pattern generation at higher throughput.

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. Phone: (512) 471-6445. Fax: (512) 471-7014. ORCID

Siyuan S. Wang: 0000-0001-8249-6553 Andrew D. Ellington: 0000-0001-6246-5338 Notes

The authors declare no competing financial interest. Biographies Siyuan S. Wang received her B.S. degree in Bioengineering from the California Institute of Technology. She is currently a graduate researcher at the University of Texas, Austin, and a recipient of the National Science Foundation Graduate Research Fellowship. Her research interests include dynamic nucleic acid circuits for selforganization and nucleic acid biophysics. Andrew D. Ellington received his B.S. degree in Biochemistry from Michigan State University and Ph.D. degree in Biochemistry from Harvard University. He is currently a Wilson M. and Kathryn Fraser Research Professor in Biochemistry at the University of Texas, Austin. His laboratory is focused on molecular programming and directed evolution.

ACKNOWLEDGMENTS This publication was made possible through the support of a grant from the John Templeton Foundation (54466). The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation. This material was additionally supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1610403 as well as the Welch Foundation (F-1654). REFERENCES (1) Belousov, B. P. A periodic reaction and its mechanism. Sb. Ref. Radiais Med. Moscow 1959, 1958, 145−147. (2) Agladze, K. I.; Krinsky, V. I.; Pertsov, A. M. Chaos in the nonstirred Belousov-Zhabotinsky reaction is induced by interaction of waves and stationary dissipative structures. Nature 1984, 308, 834. (3) Fang, Y.; Yashin, V. V.; Levitan, S. P.; Balazs, A. C. Pattern recognition with “materials that compute. Sci. Adv. 2016, 2, e1601114. (4) Turing, A. M. The chemical basis of morphogenesis. Philos. Trans. R. Soc. London B 1952, 237, 37−72. K

DOI: 10.1021/acs.chemrev.8b00625 Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(51) Watson, J. D.; Crick, F. H. Molecular structure of nucleic acids. Nature 1953, 171, 737−738. (52) Gotoh, O.; Tagashira, Y. Stabilities of nearest-neighbor doublets in double-helical DNA determined by fitting calculated melting profiles to observed profiles. Biopolymers 1981, 20, 1033−1042. (53) Vologodskii, A. V.; Amirikyan, B. R.; Lyubchenko, Y. L.; FrankKamenetskii, M. D. Allowance for heterogeneous stacking in the DNA helix-coil transition theory. J. Biomol. Struct. Dyn. 1984, 2, 131−148. (54) Breslauer, K. J.; Frank, R.; Blöcker, H.; Marky, L. A. Predicting DNA duplex stability from the base sequence. Proc. Natl. Acad. Sci. U. S. A. 1986, 83, 3746−3750. (55) Delcourt, S. G.; Blake, R. D. Stacking energies in DNA. J. Biol. Chem. 1991, 266, 15160−15169. (56) Doktycz, M. J.; Goldstein, R. F.; Paner, T. M.; Gallo, F. J.; Benight, A. S. Studies of DNA dumbbells. I. Melting curves of 17 DNA dumbbells with different duplex stem sequences linked by T4 endloops: Evaluation of the nearest-neighbor stacking interactions in DNA. Biopolymers 1992, 32, 849−864. (57) Sugimoto, N.; Nakano, S. I.; Yoneyama, M.; Honda, K. I. Improved thermodynamic parameters and helix initiation factor to predict stability of DNA duplexes. Nucleic Acids Res. 1996, 24, 4501− 4505. (58) SantaLucia, J.; Allawi, H. T.; Seneviratne, P. A. Improved nearestneighbor parameters for predicting DNA duplex stability. Biochemistry 1996, 35, 3555−3562. (59) Owczarzy, R.; Vallone, P. M.; Gallo, F. J.; Paner, T. M.; Lane, M. J.; Benight, A. S. Predicting sequence-dependent melting stability of short duplex DNA oligomers. Biopolymers 1997, 44, 217−239. (60) Owczarzy, R.; You, Y.; Moreira, B. G.; Manthey, J. A.; Huang, L.; Behlke, M. A.; Walder, J. A. Effects of sodium ions on DNA duplex oligomers: improved predictions of melting temperatures. Biochemistry 2004, 43, 3537−3554. (61) Owczarzy, R.; Moreira, B. G.; You, Y.; Behlke, M. A.; Walder, J. A. Predicting stability of DNA duplexes in solutions containing magnesium and monovalent cations. Biochemistry 2008, 47, 5336− 5353. (62) Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003, 31, 3406−3415. (63) Hofacker, I. L. Vienna RNA secondary structure server. Nucleic Acids Res. 2003, 31, 3429−3431. (64) Xayaphoummine, A.; Bucher, T.; Isambert, H. Kinefold web server for RNA/DNA folding path and structure prediction including pseudoknots and knots. Nucleic Acids Res. 2005, 33, W605−W610. (65) SantaLucia, J., Jr; Hicks, D. The thermodynamics of DNA structural motifs. Annu. Rev. Biophys. Biomol. Struct. 2004, 33, 415−440. (66) Dirks, R. M.; Bois, J. S.; Schaeffer, J. M.; Winfree, E.; Pierce, N. A. Thermodynamic analysis of interacting nucleic acid strands. SIAM Rev. 2007, 49, 65−88. (67) Zadeh, J. N.; Steenberg, C. D.; Bois, J. S.; Wolfe, B. R.; Pierce, M. B.; Khan, A. R.; Dirks, R. M.; Pierce, N. A. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 2011, 32, 170−173. (68) Ouldridge, T. E.; Š ulc, P.; Romano, F.; Doye, J. P.; Louis, A. A. DNA hybridization kinetics: zippering, internal displacement and sequence dependence. Nucleic Acids Res. 2013, 41, 8886−8895. (69) Yurke, B.; Turberfield, A. J.; Mills, A. P., Jr; Simmel, F. C.; Neumann, J. L. A DNA-fuelled molecular machine made of DNA. Nature 2000, 406, 605. (70) Takahashi, K.; Yaegashi, S.; Kameda, M. A.; Hagiya, M. Chain reaction systems based on loop dissociation of DNA. DNA Computing; Lecture Notes in Computer Science; Springer Berlin Heidelberg: Berlin, Heidelberg, 2006; Vol. 3892, pp 347−358. (71) Seelig, G.; Soloveichik, D.; Zhang, E.; Winfree, E. Winfree Enzyme-free nucleic acid logic circuits. Science 2006, 314, 1585−1588. (72) Zhang, D. Y.; Seelig, G. Dynamic DNA nanotechnology using strand-displacement reactions. Nat. Chem. 2011, 3, 103. (73) Qian, L.; Winfree, E.; Bruck, J. Neural network computation with DNA strand displacement cascades. Nature 2011, 475, 368.

expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 1998, 37, 14719−14735. (29) Zhang, D. Y.; Winfree, E. Control of DNA strand displacement kinetics using toehold exchange. J. Am. Chem. Soc. 2009, 131, 17303− 17314. (30) Zhang, J. X.; Fang, J. Z.; Duan, W.; Wu, L. R.; Zhang, A. W.; Dalchau, N.; Yordanov, B.; Petersen, R.; Phillips, A.; Zhang, D. Y. Predicting DNA hybridization kinetics from sequence. Nat. Chem. 2017, 10, 91. (31) Chirieleison, S. M.; Allen, P. B.; Simpson, Z. B.; Ellington, A. D.; Chen, X. Pattern transformation with DNA circuits. Nat. Chem. 2013, 5, 1000. (32) Scalise, D.; Schulman, R. Designing modular reaction-diffusion programs for complex pattern formation. Technology 2014, 2, 55−66. (33) Zadorin, A. S.; Rondelez, Y.; Gines, G.; Dilhas, V.; Urtel, G.; Zambrano, A.; Galas, J.; Estévez-Torres, A. Synthesis and materialization of a reaction-diffusion French flag pattern. Nat. Chem. 2017, 9, 990. (34) Zadorin, A. S.; Rondelez, Y.; Galas, J. C.; Estévez-Torres, A. Synthesis of programmable reaction-diffusion fronts using DNA catalyzers. Phys. Rev. Lett. 2015, 114, 068301. (35) Padirac, A.; Fujii, T.; Estévez-Torres, A.; Rondelez, Y. Spatial waves in synthetic biochemical networks. J. Am. Chem. Soc. 2013, 135, 14586−14592. (36) Hasatani, K.; Leocmach, M.; Genot, A. J.; Estévez-Torres, A.; Fujii, T.; Rondelez, Y. High-throughput and long-term observation of compartmentalized biochemical oscillators. Chem. Commun. 2013, 49, 8090−8092. (37) Zambrano, A.; Zadorin, A. S.; Rondelez, Y.; Estévez-Torres, A.; Galas, J. C. Pursuit-and-evasion reaction-diffusion waves in microreactors with tailored geometry. J. Phys. Chem. B 2015, 119, 5349− 5355. (38) Gines, G.; Zadorin, A. S.; Galas, J. C.; Fujii, T.; Estévez-Torres, A.; Rondelez, Y. Microscopic agents programmed by DNA circuits. Nat. Nanotechnol. 2017, 12, 351. (39) Tayar, A. M.; Karzbrun, E.; Noireaux, V.; Bar-Ziv, R. H. Synchrony and pattern formation of coupled genetic oscillators on a chip of artificial cells. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 11609− 11614. (40) Dupin, A.; Simmel, F. C. Signalling and differentiation in emulsion-based multi-compartmentalized in vitro gene circuits. Nat. Chem. 2019, 11, 32. (41) Basu, S.; Gerchman, Y.; Collins, C. H.; Arnold, F. H.; Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature 2005, 434, 1130. (42) Tabor, J. J.; Salis, H. M.; Simpson, Z. B.; Chevalier, A. A.; Levskaya, A.; Marcotte, E. M.; Voigt, C. A.; Ellington, A. D. A synthetic genetic edge detection program. Cell 2009, 137, 1272−1281. (43) Cao, Y.; Ryser, M. D.; Payne, S.; Li, B.; Rao, C. V.; You, L. Collective space-sensing coordinates pattern scaling in engineered bacteria. Cell 2016, 165, 620−630. (44) Levskaya, A.; Chevalier, A. A.; Tabor, J. J.; Simpson, Z. B.; Lavery, L. A.; Levy, M.; Davidson, E. A.; Scouras, A.; Ellington, A. D.; Marcotte, E. M.; et al. Synthetic biology: engineering Escherichia coli to see light. Nature 2005, 438, 441. (45) Chen, X.; Ellington, A. D. Shaping up nucleic acid computation. Curr. Opin. Biotechnol. 2010, 21, 392−400. (46) Kim, J.; Yin, P.; Green, A. A. Ribocomputing: Cellular Logic Computation Using RNA Devices. Biochemistry 2018, 57, 883−885. (47) Satnoianu, R. A.; van den Driessche, P. Some remarks on matrix stability with application to Turing instability. Linear Algebra Its Appl. 2005, 398, 69−74. (48) Manukyan, L.; Montandon, S. A.; Fofonjka, A.; Smirnov, S.; Milinkovitch, M. C. A living mesoscopic cellular automaton made of skin scales. Nature 2017, 544, 173. (49) Rothemund, P. W. Folding DNA to create nanoscale shapes and patterns. Nature 2006, 440, 297. (50) Halley, J. D.; Winkler, D. A. Consistent concepts of selforganization and self-assembly. Complexity 2008, 14, 10−17. L

DOI: 10.1021/acs.chemrev.8b00625 Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(74) Cherry, K. M.; Qian, L. Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature 2018, 559, 370. (75) Srinivas, N.; Ouldridge, T. E.; Š ulc, P.; Schaeffer, J. M.; Yurke, B.; Louis, A. A.; Doye, J. P.; Winfree, E. On the biophysics and kinetics of toehold-mediated DNA strand displacement. Nucleic Acids Res. 2013, 41, 10641−10658. (76) Lakin, M. R.; Youssef, S.; Polo, F.; Emmott, S.; Phillips, A. Visual DSD: a design and analysis tool for DNA strand displacement systems. Bioinformatics 2011, 27, 3211−3213. (77) Wolfe, B. R.; Porubsky, N. J.; Zadeh, J. N.; Dirks, R. M.; Pierce, N. A. Constrained multistate sequence design for nucleic acid reaction pathway engineering. J. Am. Chem. Soc. 2017, 139, 3134−3144. (78) Smith, D. E.; Perkins, T. T.; Chu, S. Dynamical scaling of DNA diffusion coefficients. Macromolecules 1996, 29, 1372−1373. (79) Karzbrun, E.; Tayar, A. M.; Noireaux, V.; Bar-Ziv, R. H. Programmable on-chip DNA compartments as artificial cells. Science 2014, 345, 829−832. (80) Allen, P. B.; Chen, X.; Simpson, Z. B.; Ellington, A. D. Modeling scalable pattern generation in DNA reaction networks. Nat. Comput. 2014, 13, 583−595. (81) Allen, P. B.; Chen, X.; Ellington, A. D. Spatial control of DNA reaction networks by DNA sequence. Molecules 2012, 17, 13390− 13402. (82) Zenk, J.; Scalise, D.; Wang, K.; Dorsey, P.; Fern, J.; Cruz, A.; Schulman, R. Stable DNA-based reaction-diffusion patterns. RSC Adv. 2017, 7, 18032−18040. (83) Dalchau, N.; Seelig, G.; Phillips, A. Computational design of reaction-diffusion patterns using DNA-based chemical reaction networks. International Workshop on DNA-Based Computers 2014, 8727, 84−99. (84) Scalise, D.; Schulman, R. Emulating cellular automata in chemical reaction-diffusion networks. Nat. Comput. 2016, 15, 197−214. (85) Douglas, S. M.; Marblestone, A. H.; Teerapittayanon, S.; Vazquez, A.; Church, G. M.; Shih, W. M. Rapid prototyping of 3D DNA-origami shapes with caDNAno. Nucleic Acids Res. 2009, 37, 5001−5006. (86) Kim, D. N.; Kilchherr, F.; Dietz, H.; Bathe, M. Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures. Nucleic Acids Res. 2012, 40, 2862−2868. (87) Castro, C. E.; Kilchherr, F.; Kim, D. N.; Shiao, E. L.; Wauer, T.; Wortmann, P.; Bathe, M.; Dietz, H. A primer to scaffolded DNA origami. Nat. Methods 2011, 8, 221. (88) Veneziano, R.; Ratanalert, S.; Zhang, K.; Zhang, F.; Yan, H.; Chiu, W.; Bathe, M. Designer nanoscale DNA assemblies programmed from the top down. Science 2016, 352, 1534−1534. (89) Lengyel, I.; Rabai, G.; Epstein, I. R. Experimental and modeling study of oscillations in the chlorine dioxide-iodine-malonic acid reaction. J. Am. Chem. Soc. 1990, 112, 9104−9110. (90) Ouyang, Q.; Swinney, H. L. Transition from a uniform state to hexagonal and striped Turing patterns. Nature 1991, 352, 610−612. (91) Karig, D.; Martini, K. M.; Lu, T.; DeLateur, N. A.; Goldenfeld, N.; Weiss, R. Stochastic Turing patterns in a synthetic bacterial population. Proc. Natl. Acad. Sci. U. S. A. 2018, 115, 6572−6577. (92) Leppänen, T. Computational studies of pattern formation in Turing systems. Ph.D. thesis, Helsinki University of Technology, 2004. (93) Barrio, R. A.; Varea, C.; Aragón, J. L.; Maini, P. K. A twodimensional numerical study of spatial pattern formation in interacting Turing systems. Bull. Math. Biol. 1999, 61, 483−505. (94) Ermentrout, B. Stripes or spots? Nonlinear effects in bifurcation of reaction-diffusion equations on the square. Proc. R. Soc. London, Ser. A 1991, 434, 413−417. (95) Fujii, T.; Rondelez, Y. Predator-prey molecular ecosystems. ACS Nano 2013, 7, 27−34. (96) Pan, J.; Li, F.; Cha, T. G.; Chen, H.; Choi, J. H. Recent progress on DNA based walkers. Curr. Opin. Biotechnol. 2015, 34, 56−64. (97) Shin, J. S.; Pierce, N. A. A synthetic DNA walker for molecular transport. J. Am. Chem. Soc. 2004, 126, 10834−10835.

(98) Sherman, W. B.; Seeman, N. C. A precisely controlled DNA biped walking device. Nano Lett. 2004, 4, 1203−1207. (99) Yin, P.; Yan, H.; Daniell, X. G.; Turberfield, A. J.; Reif, J. H. A unidirectional DNA walker that moves autonomously along a track. Angew. Chem. 2004, 116, 5014−5019. (100) Wang, Z. G.; Elbaz, J.; Willner, I. DNA machines: bipedal walker and stepper. Nano Lett. 2011, 11, 304−309. (101) You, M.; Huang, F.; Chen, Z.; Wang, R. W.; Tan, W. Building a nanostructure with reversible motions using photonic energy. ACS Nano 2012, 6, 7935−7941. (102) Wickham, S. F.; Bath, J.; Katsuda, Y.; Endo, M.; Hidaka, K.; Sugiyama, H.; Turberfield, A. J. A DNA-based molecular motor that can navigate a network of tracks. Nat. Nanotechnol. 2012, 7, 169. (103) Jung, C.; Allen, P. B.; Ellington, A. D. A stochastic DNA walker that traverses a microparticle surface. Nat. Nanotechnol. 2016, 11, 157. (104) Pei, R.; Taylor, S. K.; Stefanovic, D.; Rudchenko, S.; Mitchell, T. E.; Stojanovic, M. N. Behavior of polycatalytic assemblies in a substratedisplaying matrix. J. Am. Chem. Soc. 2006, 128, 12693−12699. (105) Jung, C.; Allen, P. B.; Ellington, A. D. A simple, cleated DNA walker that hangs on to surfaces. ACS Nano 2017, 11, 8047−8054. (106) Yehl, K.; Mugler, A.; Vivek, S.; Liu, Y.; Zhang, Y.; Fan, M.; Weeks, E.; Salaita, K. High-speed DNA-based rolling motors powered by RNase H. Nat. Nanotechnol. 2016, 11, 184. (107) Yin, P.; Yan, H.; Daniell, X. G.; Turberfield, A. J.; Reif, J. H. A unidirectional DNA walker that moves autonomously along a track. Angew. Chem. 2004, 116, 5014−5019. (108) Lund, K.; Manzo, A. J.; Dabby, N.; Michelotti, N.; JohnsonBuck, A.; Nangreave, J.; Taylor, S.; Pei, R.; Stojanovic, M.; Walter, N.; et al. Molecular robots guided by prescriptive landscapes. Nature 2010, 465, 206. (109) Liber, M.; Tomov, T. E.; Tsukanov, R.; Berger, Y.; Nir, E. A bipedal DNA motor that travels back and forth between two DNA origami tiles. Small 2015, 11, 568−575. (110) Cha, T. G.; Pan, J.; Chen, H.; Salgado, J.; Li, X.; Mao, C.; Choi, J. H. A synthetic DNA motor that transports nanoparticles along carbon nanotubes. Nat. Nanotechnol. 2014, 9, 39. (111) He, Y.; Liu, D. R. Autonomous multistep organic synthesis in a single isothermal solution mediated by a DNA walker. Nat. Nanotechnol. 2010, 5, 778. (112) Gu, H.; Chao, J.; Xiao, S. J.; Seeman, N. C. A proximity-based programmable DNA nanoscale assembly line. Nature 2010, 465, 202. (113) Thubagere, A. J.; Li, W.; Johnson, R. F.; Chen, Z.; Doroudi, S.; Lee, Y. L.; Izatt, G.; Wittman, S.; Srinivas, N.; Woods, D.; et al. A cargosorting DNA robot. Science 2017, 357, No. eaan6558. (114) Zhou, C.; Duan, X.; Liu, N. A plasmonic nanorod that walks on DNA origami. Nat. Commun. 2015, 6, 8102. (115) Chandran, H.; Gopalkrishnan, N.; Phillips, A.; Reif, J. Localized hybridization circuits. DNA Computing and Molecular Programming; Lecture Notes in Computer Science; Springer Berlin Heidelberg: Berlin, Heidelberg, 2011; Vol. 6937, pp 64−83. (116) Muscat, R. A.; Strauss, K.; Ceze, L.; Seelig, G. DNA-based molecular architecture with spatially localized components. SIGARCH Comput. Archit. News 2013, 41, 177−188. (117) Lakin, M. R.; Petersen, R.; Gray, K. E.; Phillips, A. Abstract modelling of tethered DNA circuits. DNA Computing and Molecular Programming; Lecture Notes in Computer Science; Springer International Publishing: Cham, 2014; Vol. 8727, pp 132−147. (118) Qian, L.; Winfree, E. Parallel and scalable computation and spatial dynamics with DNA-based chemical reaction networks on a surface. DNA Computing and Molecular Programming; Lecture Notes in Computer Science; Springer International Publishing: Cham, 2014; Vol. 8727, pp 114−131. (119) Chatterjee, G.; Dalchau, N.; Muscat, R. A.; Phillips, A.; Seelig, G. A spatially localized architecture for fast and modular DNA computing. Nat. Nanotechnol. 2017, 12, 920. (120) Dannenberg, F.; Kwiatkowska, M.; Thachuk, C.; Turberfield, A. J. DNA walker circuits: Computational potential, design, and verification. DNA Computing and Molecular Programming; Lecture M

DOI: 10.1021/acs.chemrev.8b00625 Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Notes in Computer Science; Springer International Publishing: Cham, 2013; Vol. 8141, pp 31−45. (121) Yin, P.; Turberfield, A. J.; Sahu, S.; Reif, J. H. Design of an autonomous DNA nanomechanical device capable of universal computation and universal translational motion. DNA Computing; Lecture Notes in Computer Science; Springer Berlin Heidelberg: Berlin, Heidelberg, 2005; Vol. 3384, pp 426−444. (122) Wang, F.; Willner, B.; Willner, I. DNA-based machines. Molecular Machines and Motors; Springer, 2014; pp 279−338. (123) Tian, Y.; Mao, C. Molecular gears: a pair of DNA circles continuously rolls against each other. J. Am. Chem. Soc. 2004, 126, 11410−11411. (124) Pelossof, G.; Tel-Vered, R.; Liu, X.; Willner, I. Switchable mechanical DNA “arms” operating on nucleic acid scaffolds associated with electrodes or semiconductor quantum dots. Nanoscale 2013, 5, 8977−8981. (125) Kopperger, E.; List, J.; Madhira, S.; Rothfischer, F.; Lamb, D. C.; Simmel, F. C. A self-assembled nanoscale robotic arm controlled by electric fields. Science 2018, 359, 296−301. (126) Elbaz, J.; Wang, Z. G.; Orbach, R.; Willner, I. pH-stimulated concurrent mechanical activation of two DNA “tweezers”. A “SETRESET” Logic Gate System. Nano Lett. 2009, 9, 4510−4514. (127) Shimron, S.; Magen, N.; Elbaz, J.; Willner, I. pH-programmable DNAzyme nanostructures. Chem. Commun. 2011, 47, 8787−8789. (128) Liang, X.; Nishioka, H.; Takenaka, N.; Asanuma, H. A DNA nanomachine powered by light irradiation. ChemBioChem 2008, 9, 702−705. (129) Elbaz, J.; Moshe, M.; Willner, I. Coherent activation of DNA tweezers: a “SET-RESET” logic system. Angew. Chem., Int. Ed. 2009, 48, 3834−3837. (130) Engelen, W.; Meijer, L. H. H.; Somers, B.; De Greef, T. F. A.; Merkx, M. Antibody-controlled actuation of DNA-based molecular circuits. Nat. Commun. 2017, 8, 14473. (131) Song, Y.; Kim, S.; Heller, M. J.; Huang, X. DNA multi-bit nonvolatile memory and bit-shifting operations using addressable electrode arrays and electric field-induced hybridization. Nat. Commun. 2018, 9, 281. (132) Kamiya, Y.; Asanuma, H. Light-driven DNA nanomachine with a photoresponsive molecular engine. Acc. Chem. Res. 2014, 47, 1663− 1672. (133) Gong, L.; Zhao, Z.; Lv, Y.; Huan, S.; Fu, T.; Zhang, X.; Shen, G.; Yu, R. DNAzyme-based biosensors and nanodevices. Chem. Commun. 2015, 51, 979−995. (134) Peng, H.; Li, X.; Zhang, H.; Le, X. C. A microRNA-initiated DNAzyme motor operating in living cells. Nat. Commun. 2017, 8, 14378. (135) Wei, F.; Lillehoj, P. B.; Ho, C. M. DNA diagnostics: Nanotechnology-Enhanced Electrochemical Detection of Nucleic Acids. Pediatr. Res. 2010, 67, 458. (136) Gabig-Ciminska, M. Developing nucleic acid-based electrical detection systems. Microb. Cell Fact. 2006, 5, 9. (137) Roh, Y. H.; Ruiz, R. C. H.; Peng, S.; Lee, J. B.; Luo, D. Engineering DNA-based functional materials. Chem. Soc. Rev. 2011, 40, 5730−5744. (138) Liu, J. Oligonucleotide-functionalized hydrogels as stimuli responsive materials and biosensors. Soft Matter 2011, 7, 6757−6767. (139) Yang, D.; Hartman, M. R.; Derrien, T. L.; Hamada, S.; An, D.; Yancey, K. G.; Cheng, R.; Ma, M.; Luo, D. DNA materials: bridging nanotechnology and biotechnology. Acc. Chem. Res. 2014, 47, 1902− 1911. (140) Um, S. H.; Lee, J. B.; Park, N.; Kwon, S. Y.; Umbach, C. C.; Luo, D. Enzyme-catalysed assembly of DNA hydrogel. Nat. Mater. 2006, 5, 797. (141) Park, N.; Um, S. H.; Funabashi, H.; Xu, J.; Luo, D. A cell-free protein-producing gel. Nat. Mater. 2009, 8, 432. (142) Lee, J. B.; Peng, S.; Yang, D.; Roh, Y. H.; Funabashi, H.; Park, N.; Rice, E.; Chen, L.; Long, R.; Wu, M.; et al. A mechanical metamaterial made from a DNA hydrogel. Nat. Nanotechnol. 2012, 7, 816.

(143) Kurokawa, C.; Fujiwara, K.; Morita, M.; Kawamata, I.; Kawagishi, Y.; Sakai, A.; Murayama, Y.; Nomura, S.-i. M.; Murata, S.; Takinoue, M.; Yanagisawa, M.; et al. DNA cytoskeleton for stabilizing artificial cells. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 7228−7233. (144) Yu, X.; Hu, Y.; Kahn, J. S.; Cecconello, A.; Willner, I. Orthogonal dual-triggered shape-memory DNA-based hydrogels. Chem. - Eur. J. 2016, 22, 14504−14507. (145) Guo, W.; Lu, C. H.; Orbach, R.; Wang, F.; Qi, X. J.; Cecconello, A.; Seliktar, D.; Willner, I. pH-stimulated DNA hydrogels exhibiting shape-memory properties. Adv. Mater. 2015, 27, 73−78. (146) Hu, Y.; Guo, W.; Kahn, J. S.; Aleman-Garcia, M. A.; Willner, I. A shape-memory DNA-based hydrogel exhibiting two internal memories. Angew. Chem. 2016, 128, 4282−4286. (147) Lu, C. H.; Guo, W.; Hu, Y.; Qi, X. J.; Willner, I. Multitriggered shape-memory acrylamide-DNA hydrogels. J. Am. Chem. Soc. 2015, 137, 15723−15731. (148) Heinen, L.; Heuser, T.; Steinschulte, A.; Walther, A. Antagonistic enzymes in a biocatalytic pH feedback system program autonomous DNA hydrogel life cycles. Nano Lett. 2017, 17, 4989− 4995. (149) Choi, H. M.; Beck, V. A.; Pierce, N. A. Next-generation in situ hybridization chain reaction: higher gain, lower cost, greater durability. ACS Nano 2014, 8, 4284−4294. (150) Cangialosi, A.; Yoon, C.; Liu, J.; Huang, Q.; Guo, J.; Nguyen, T. D.; Gracias, D.; Schulman, R. DNA sequence-directed shape change of photopatterned hydrogels via high-degree swelling. Science 2017, 357, 1126−1130. (151) Fern, J.; Schulman, R. Modular DNA strand-displacement controllers for directing material expansion. Nat. Commun. 2018, 9, 3766. (152) Shim, T. S.; Estephan, Z. G.; Qian, Z.; Prosser, J. H.; Lee, S. Y.; Chenoweth, D. M.; Lee, D.; Park, S.; Crocker, J. C. Shape changing thin films powered by DNA hybridization. Nat. Nanotechnol. 2016, 12, 41. (153) Lopez, R.; Wang, R.; Seelig, G. A molecular multi-gene classifier for disease diagnostics. Nat. Chem. 2018, 10, 746−754. (154) Green, A. A.; Kim, J.; Ma, D.; Silver, P. A.; Collins, J. J.; Yin, P. Complex cellular logic computation using ribocomputing devices. Nature 2017, 548, 117. (155) Kim, J.; White, K. S.; Winfree, E. Construction of an in vitro bistable circuit from synthetic transcriptional switches. Mol. Syst. Biol. 2006, 2, 68. (156) Kim, J.; Winfree, E. Synthetic in vitro transcriptional oscillators. Mol. Syst. Biol. 2011, 7, 465. (157) Kar, S.; Ellington, A. D. In vitro transcription networks based on hairpin promoter switches. ACS Synth. Biol. 2018, 7, 1937−1945. (158) Kim, J.; Hopfield, J.; Winfree, E. Neural network computation by in vitro transcriptional circuits. Advances in neural information processing systems; Neural Information Processing Systems Foundation, Inc., 2005; pp 681−688. (159) Yang, H. W.; Lee, A. W.; Huang, C. H.; Chen, J. K. Characterization of poly(N-isopropylacrylamide)-nucleobase supramolecular complexes featuring bio-multiple hydrogen bonds. Soft Matter 2014, 10, 8330−8340.

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DOI: 10.1021/acs.chemrev.8b00625 Chem. Rev. XXXX, XXX, XXX−XXX