A spatially localized architecture for fast and modular DNA computing

Spatially Localized Entropy-Driven Evolution of Nucleic Acid-Based Constitutional Dynamic Networks for Intracellular Imaging and Spatiotemporal Programmable Gene Therapy

The primer-guided entropy-driven high-throughput evolution of the DNA-based constitutional dynamic network, CDN, is introduced. The entropy gain associated with the process provides a catalytic principle for the amplified emergence of the CDN. The concept is applied to develop a programmable, spatially localized DNA circuit for effective in vitro and in vivo theranostic, gene-regulated treatment of cancer cells. The localized circuit consists of a DNA tetrahedron core modified at its corners with four tethers that include encoded base sequences exhibiting the capacity to emerge and assemble into a [2 × 2] CDN. Two of the tethers are caged by a pair of siRNA subunits, blocking the circuit into a mute, dynamically inactive configuration. In the presence of miRNA-21 as primer, the siRNA subunits are displaced, resulting in amplified release of the siRNAs silencing the HIF-1α mRNA and fast dynamic reconfiguration of the tethers into a CDN. The resulting CDN is, however, engineered to be dynamically reconfigured by miRNA-155 into an equilibrated mixture enriched with a DNAzyme component, catalyzing the cleavage of EGR-1 mRNA. The DNA tetrahedron nanostructure stimulates enhanced permeation into cancer cells. The miRNA-triggered entropy-driven reconfiguration of the spatially localized circuit leads to the programmable, cooperative bis-gene-silencing of HIF-1α and EGR-1 mRNAs, resulting in the effective and selective apoptosis of breast cancer cells and effective inhibition of tumors in tumor bearing mice.

Understanding the relationship between sequences and kinetics of DNA strand displacements

Precisely modulating the kinetics of toehold-mediated DNA strand displacements (TMSD) is essential for its application in DNA nanotechnology. The sequence in the toehold region significantly influences the kinetics of TMSD. However, due to the large sample space resulting from various arrangements of base sequences and the resulted complex secondary structures, such a correlation is not intuitive. Herein, machine learning was employed to reveal the relationship between the kinetics of TMSD and the toehold sequence as well as the correlated secondary structure of invader strands. Key factors that influence the rate constant of TMSD were identified, such as the number of free hydrogen bonding sites in the invader, the number of free bases in the toehold, and the number of hydrogen bonds in intermediates. Moreover, a predictive model was constructed, which successfully achieved semi-quantitative prediction of rate constants of TMSD even with subtle distinctions in toehold sequence.

A Dual-Recognition Fluorescence Enzyme-Linked Immunosorbent Assay for Specific Detection of Intact Lipid Nanoparticles via a Localized Scaffolding Autocatalytic DNA Circuit Amplifier

Lipid nanoparticles (LNPs) are emerging as one of the most promising drug delivery systems. The long-circulating effect of intact LNPs (i-LNPs) is the key to efficacy and toxicity in vivo. However, the significant challenge is specific and sensitive detection of i-LNPs. Herein, a dual-recognition fluorescence enzyme-linked immunosorbent assay (DR-FELISA) was developed to directly isolate and detect i-LNPs by combining dual-recognition separation with a one-step signal amplification strategy. The microplates captured and enriched i-LNPs through antibody-antigen reaction. Dual-chol probes were spontaneously introduced into the lipid bilayer of captured i-LNPs, converting the detection of i-LNPs into the detection of double-cholesterol probes. Finally, the end of the dual-chol probes initiated the localized scaffolding autocatalytic DNA circuits (SADC) system for further signal amplification. The SADC system provides a sensitive and efficient amplifier through localized network structures and self-assembled triggers. Simultaneous recognition of i-LNPs surface PEG-lipid and lipid bilayer structures significantly eliminates interference from biological samples. i-LNPs were detected with high selectivity, ranging from 0.2 to 1.25 mg/mL with a limit of detection of 0.1 mg/mL. Moreover, this method allows the isolation and quantitative analysis of different formulations of i-LNPs in serum samples with a satisfactory recovery rate ranging from 94.8 to 116.3%. Thus, the DR-FELISA method provides an advanced platform for the exclusive and sensitive detection of i-LNPs, providing new insights for the study of the quality and intracorporal process of complex formulations.

Encoding signal propagation on topology-programmed DNA origami

Biological systems often rely on topological transformation to reconfigure connectivity between nodes to guide the flux of molecular information. Here we develop a topology-programmed DNA origami system that encodes signal propagation at the nanoscale, analogous to topologically efficient information processing in cellular systems. We present a systematic molecular implementation of topological operations involving 'glue-cut' processes that can prompt global conformational change of DNA origami structures, with demonstrated major topological properties including genus, number of boundary components and orientability. By spatially arranging reactive DNA hairpins, we demonstrate signal propagation across transmission paths of varying lengths and orientations, and curvatures on the curved surfaces of three-dimensional origamis. These DNA origamis can also form dynamic scaffolds for regulating the spatial and temporal signal propagations whereby topological transformations spontaneously alter the location of nodes and boundary of signal propagation network. We anticipate that our strategy for topological operations will provide a general route to manufacture dynamic DNA origami nanostructures capable of performing global structural transformations under programmable control.

DNA Molecular Computation Using the CRISPR-Mediated Reaction and Surface Growth of Gold Nanoparticles

DNA has emerged as a promising tool to build logic gates for biocomputing. However, prevailing methodologies predominantly rely on hybridization reactions or structural alterations to construct DNA logic gates, which are limited in simplicity and diversity. Herein, we developed simple and smart DNA-based logic gates for biocomputing through the DNA-mediated growth of gold nanomaterials without precise structure design and probe modification. Capitalizing on their excellent plasmonic properties, the surface growth of gold nanomaterials enables distinct wavelength shifts and unique shapes, which are modulated by the composition, length, and concentration of the DNA sequences. Combined with a CRISPR-mediated reaction, we constructed DNA circuits to achieve complicated biocomputing to modulate the surface growth of gold nanomaterials. By implementing logic functions controlled by input-mediated growth of gold nanomaterials, we established YES/NOT, AND/NAND, OR/NOR, XOR, and INHIBIT gates and further constructed cascade logic circuits, parity checker for natural numbers, and gray code encoder, which are promising for DNA biocomputing.

Scaling up of a Self-Confined Catalytic Hybridization Circuit for Robust microRNA Imaging

The precise regulation of cellular behaviors within a confined, crowded intracellular environment is highly amenable in diagnostics and therapeutics. While synthetic circuitry system through a concatenated chemical reaction network has rarely been reported to mimic dynamic self-assembly system. Herein, a catalytic self-defined circuit (CSC) for the hierarchically concatenated assembly of DNA domino nanostructures is engineered. By incorporating pre-sealed symmetrical fragments into the preying hairpin reactants, the CSC system allows the hierarchical DNA self-assembly via a microRNA (miRNA)-powered self-sorting catalytic hybridization reaction. With minimal strand complexity, this self-sustainable CSC system streamlined the circuit component and achieved localization-intensified cascaded signal amplification. Profiting from the self-adaptively concatenated hybridization reaction, a reliable and robust method has been achieved for discriminating carcinoma tissues from the corresponding para-carcinoma tissues. The CSC-sustained self-assembly strategy provides a comprehensive and smart toolbox for organizing various hierarchical DNA nanostructures, which may facilitate more insights for clinical diagnosis and therapeutic assessment.

Construction of a streptavidin-based dual-localized DNAzyme walker for disease biomarker detection

A dual-localized DNAzyme walker (dlDW) was constructed by utilizing multiple split DNAzymes with probes, and their substrates are separately localized on streptavidin and AuNPs, serving as walking pedals and tracks, respectively. Based on dlDW, biosensing platform was successfully constructed and showed great potential application in clinical disease diagnosis.

A spatially localized DNA linear classifier for cancer diagnosis

Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more efficient computing scheme, higher modularity with scaled-up circuity and stronger tolerance of corrupted inputs in a complex environment. Towards these goals, we construct a spatially localized, DNA integrated circuits-based classifier (DNA IC-CLA) that can perform neuromorphic architecture-based computation at a molecular level for medical diagnosis. The DNA-based classifier employs a two-dimensional DNA origami as the framework and localized processing modules as the in-frame computing core to execute arithmetic operations (e.g. multiplication, addition, subtraction) for efficient linear classification of complex patterns of miRNA inputs. We demonstrate that the DNA IC-CLA enables accurate cancer diagnosis in a faster (about 3 h) and more effective manner in synthetic and clinical samples compared to those of the traditional freely diffusible DNA circuits. We believe that this all-in-one DNA-based classifier can exhibit more applications in biocomputing in cells and medical diagnostics.

Intelligent Cell Profiling and Precision Release: Multimolecular Marker-Activated Transmembrane DNA Computing Nanosystem

Accurate classification of tumor cells is of importance for cancer diagnosis and further therapy. In this study, we develop multimolecular marker-activated transmembrane DNA computing systems (MTD). Employing the cell membrane as a native gate, the MTD system enables direct signal output following simple spatial events of "transmembrane" and "in-cell target encounter", bypassing the need of multistep signal conversion. The MTD system comprises two intelligent nanorobots capable of independently sensing three molecular markers (MUC1, EpCAM, and miR-21), resulting in comprehensive analysis. Our AND-AND logic-gated system (MTDAND-AND) demonstrates exceptional specificity, allowing targeted release of drug-DNA specifically in MCF-7 cells. Furthermore, the transformed OR-AND logic-gated system (MTDOR-AND) exhibits broader adaptability, facilitating the release of drug-DNA in three positive cancer cell lines (MCF-7, HeLa, and HepG2). Importantly, MTDAND-AND and MTDOR-AND, while possessing distinct personalized therapeutic potential, share the ability of outputting three imaging signals without any intermediate conversion steps. This feature ensures precise classification cross diverse cells (MCF-7, HeLa, HepG2, and MCF-10A), even in mixed populations. This study provides a straightforward yet effective solution to augment the versatility and precision of DNA computing systems, advancing their potential applications in biomedical diagnostic and therapeutic research.

Sequence-Guided Localization of DNA Hybridization Enables Highly Selective and Robust Genotyping

Genetic variations are always related to human diseases or susceptibility to therapies. Nucleic acid probes that precisely distinguish closely related sequences become an indispensable requisite both in research and clinical applications. Here, a Sequence-guided DNA LOCalization for leaKless DNA detection (SeqLOCK) is introduced as a technique for DNA hybridization, where the intended targets carrying distinct "guiding sequences" act selectively on the probes. In silicon modeling, experimental results reveal considerable agreement (R2 = 0.9228) that SeqLOCK is capable of preserving high discrimination capacity at an extraordinarily wide range of target concentrations. Furthermore, SeqLOCK reveals high robustness to various solution conditions and can be directly adapted to nucleic acid amplification techniques (e.g., polymerase chain reaction) without the need for laborious pre-treatments. Benefiting from the low hybridization leakage of SeqLOCK, three distinct variations with a clinically relevant mutation frequency under the background of genomic DNA can be discriminated simultaneously. This work establishes a reliable nucleic acid hybridization strategy that offers great potential for constructing robust and programmable systems for molecular sensing and computing.

FEN1-assisted DNA logic amplifier circuit for fast and compact DNA computing

This work developed DNA amplifier logic gates (AND-OR, OR-AND, FAN-IN, FAN-OUT, and 4-bit square-root circuits) using a flap endonuclease 1 (FEN1)-catalyzed signal amplification reaction, for the fastest and compact DNA computing. Moreover, the logic circuit can use input strands with concentrations of less than 1 nM, which is more than 100 times lower than the input concentration of other DNA logic circuits, providing a promising methodology for constructing fast and compact DNA computations.

DNA Logic Gates Integrated on DNA Substrates in Molecular Computing

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

Singleton {NOT} and Doubleton {YES; NOT} Gates Act as Functionally Complete Sets in DNA-Integrated Computational Circuits

A functionally complete Boolean operator is sufficient for computational circuits of arbitrary complexity. We connected YES (buffer) with NOT (inverter) and two NOT four-way junction (4J) DNA gates to obtain IMPLY and NAND Boolean functions, respectively, each of which represents a functionally complete gate. The results show a technological path towards creating a DNA computational circuit of arbitrary complexity based on singleton NOT or a combination of NOT and YES gates, which is not possible in electronic computers. We, therefore, concluded that DNA-based circuits and molecular computation may offer opportunities unforeseen in electronics.

Collective Molecular Machines: Multidimensionality and Reconfigurability

Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work. During the last 60 years, designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research. Effective progress has been made, attributed to advances in various fields such as supramolecular chemistry, biology and nanotechnology, and informatics. However, individual molecular machines are only capable of producing nanometer work and generally have only a single functionality. In order to address these problems, collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm. In this review, we comprehensively discuss recent developments in the collective behaviors of molecular machines. In particular, collective behavior is divided into two paradigms. One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials. The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations. We discuss design strategies for both modes and focus on the modulation of features and properties. Subsequently, in order to address existing challenges, the idea of transferring experience gained in the field of micro/nano robotics is presented, offering prospects for future developments in the collective behavior of molecular machines.

DNA as a universal chemical substrate for computing and data storage

DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.

Designing 3D multicomponent self-assembling systems with signal-passing building blocks

We introduce an allostery-mimetic building block model for the self-assembly of 3D structures. We represent the building blocks as patchy particles, where each binding site (patch) can be irreversibly activated or deactivated by binding of the particle's other controlling patches to another particle. We show that these allostery-mimetic systems can be designed to increase yields of target structures by disallowing misassembled states and can further decrease the smallest number of distinct species needed to assemble a target structure. Next, we show applications to design a programmable nanoparticle swarm for multifarious assembly: a system of particles that stores multiple possible target structures and a particular structure is recalled by presenting an external trigger signal. Finally, we outline a possible pathway for realization of such structures at nanoscale using DNA nanotechnology devices.

Spacer-Programmed Two-Dimensional DNA Origami Assembly

Two-dimensional (2D) DNA origami assembly represents a powerful approach to the programmable design and construction of advanced 2D materials. Within the context of hybridization-mediated 2D DNA origami assembly, DNA spacers play a pivotal role as essential connectors between sticky-end regions and DNA origami units. Here, we demonstrated that programming the spacer length, which determines the binding radius of DNA origami units, could effectively tune sticky-end hybridization reactions to produce distinct 2D DNA origami arrays. Using DNA-PAINT super-resolution imaging, we unveiled the significant impact of spacer length on the hybridization efficiency of sticky ends for assembling square DNA origami (SDO) units. We also found that the assembly efficiency and pattern diversity of 2D DNA origami assemblies were critically dependent on the spacer length. Remarkably, we realized a near-unity yield of ∼98% for the assembly of SDO trimers and tetramers via this spacer-programmed strategy. At last, we revealed that spacer lengths and thermodynamic fluctuations of SDO are positively correlated, using molecular dynamics simulations. Our study thus paves the way for the precision assembly of DNA nanostructures toward higher complexity.

Programming and monitoring surface-confined DNA computing

DNA-based molecular computing has evolved to encompass a diverse range of functions, demonstrating substantial promise for both highly parallel computing and various biomedical applications. Recent advances in DNA computing systems based on surface reactions have demonstrated improved levels of specificity and computational speed compared to their solution-based counterparts that depend on three-dimensional molecular collisions. Herein, computational biomolecular interactions confined by various surfaces such as DNA origamis, nanoparticles, lipid membranes and chips are systematically reviewed, along with their manipulation methodologies. Monitoring techniques and applications for these surface-based computing systems are also described. The advantages and challenges of surface-confined DNA computing are discussed.

Simulation-Assisted DNA Nanodevice Serve as a General Optical Platform for Multiplexed Analysis of Micrornas

Small frame nucleic acids (FNAs) serve as excellent carrier materials for various functional nucleic acid molecules, showcasing extensive potential applications in biomedicine development. The carrier module and function module combination is crucial for probe design, where an improper combination can significantly impede the functionality of sensing platforms. This study explores the effect of various combinations on the sensing performance of nanodevices through simulations and experimental approaches. Variances in response velocities, sensitivities, and cell uptake efficiencies across different structures are observed. Factors such as the number of functional molecules loaded, loading positions, and intermodular distances affect the rigidity and stability of the nanostructure. The findings reveal that the structures with full loads and moderate distances between modules have the lowest potential energy. Based on these insights, a multisignal detection platform that offers optimal sensitivity and response speed is developed. This research offers valuable insights for designing FNAs-based probes and presents a streamlined method for the conceptualization and optimization of DNA nanodevices.

A genetic circuit on a single DNA molecule as an autonomous dissipative nanodevice

Realizing genetic circuits on single DNA molecules as self-encoded dissipative nanodevices is a major step toward miniaturization of autonomous biological systems. A circuit operating on a single DNA implies that genetically encoded proteins localize during coupled transcription-translation to DNA, but a single-molecule measurement demonstrating this has remained a challenge. Here, we use a genetically encoded fluorescent reporter system with improved temporal resolution and observe the synthesis of individual proteins tethered to a DNA molecule by transient complexes of RNA polymerase, messenger RNA, and ribosome. Against expectations in dilute cell-free conditions where equilibrium considerations favor dispersion, these nascent proteins linger long enough to regulate cascaded reactions on the same DNA. We rationally design a pulsatile genetic circuit by encoding an activator and repressor in feedback on the same DNA molecule. Driven by the local synthesis of only several proteins per hour and gene, the circuit dynamics exhibit enhanced variability between individual DNA molecules, and fluctuations with a broad power spectrum. Our results demonstrate that co-expressional localization, as a nonequilibrium process, facilitates single-DNA genetic circuits as dissipative nanodevices, with implications for nanobiotechnology applications and artificial cell design.

Engineering of a Self-Regulatory Bidirectional DNA Assembly Circuit for Amplified MicroRNA Imaging

The engineering of catalytic hybridization DNA circuits represents versatile ways to orchestrate a complex flux of molecular information at the nanoscale, with potential applications in DNA-encoded biosensing, drug discovery, and therapeutics. However, the diffusive escape of intermediates and unintentional binding interactions remain an unsolved challenge. Herein, we developed a compact, yet efficient, self-regulatory assembly circuit (SAC) for achieving robust microRNA (miRNA) imaging in live cells through DNA-templated guaranteed catalytic hybridization. By integrating the toehold strand with a preblocked palindromic fragment in the stem domain, the proposed miniature SAC system allows the reactant-to-template-controlled proximal hybridization, thus facilitating the bidirectional-sustained assembly and the localization-intensified signal amplification without undesired crosstalk. With condensed components and low reactant complexity, the SAC amplifier realized high-contrast intracellular miRNA imaging. We anticipate that this simple and template-controlled design can enrich the clinical diagnosis and prognosis toolbox.

Toward three-dimensional DNA industrial nanorobots

Nanoscale industrial robots have potential as manufacturing platforms and are capable of automatically performing repetitive tasks to handle and produce nanomaterials with consistent precision and accuracy. We demonstrate a DNA industrial nanorobot that fabricates a three-dimensional (3D), optically active chiral structure from optically inactive parts. By making use of externally controlled temperature and ultraviolet (UV) light, our programmable robot, ~100 nanometers in size, grabs different parts, positions and aligns them so that they can be welded, releases the construct, and returns to its original configuration ready for its next operation. Our robot can also self-replicate its 3D structure and functions, surpassing single-step templating (restricted to two dimensions) by using folding to access the third dimension and more degrees of freedom. Our introduction of multiple-axis precise folding and positioning as a tool/technology for nanomanufacturing will open the door to more complex and useful nano- and microdevices.

Ratiometric Fluorescence Biosensing of Tandem Biemissive Ag Clusters Boosted by Confined Catalytic DNA Assembly

The reaction kinetics and yield of traditional DNA assembly with a low local concentration in homogeneous solution remain challenging. Exploring confined catalytic DNA assembly (CCDA) is intriguing to boost the reaction rate and efficacy for creating rapid and sensitive biosensing platforms. A rolling circle amplification (RCA) product containing multiple tandem repeats is a natural scaffold capable of guiding the periodic assembly of customized functional probes at precise sites. Here, we present a RCA-confined CCDA strategy to speed up amplifiable conversion for ratiometric fluorescent sensing of a sequence-specific inducer (I*) by using string green-/red-Ag clusters (sgAgCs and srAgCs) as two counterbalance emitters. Upon recognition of I*, CCDA events are operated by two toehold-mediated strand displacements and localized in repetitive units, thereby releasing I* for recycled signal amplification in the as-grown RCA concatemer. The local concentration of reactive species is increased to facilitate rapider dsDNA complex assembly and more efficient input-output conversion, on which the clustering template sequences of sgAgCs and srAgCs are blocked and opened, enabling srAgCs synthesis but opposite to sgAgCs. Thus, the fluorescence emission of srAgCs goes up, while sgAgCs go down. With the resultant ratio featuring inherent built-in correction, rapid, sensitive, and accurate quantification of I* at the picomolar level is achieved. Benefiting from efficient RCA confinement to enhance reaction kinetics and conversion yield, this CCDA-based strategy provides a new paradigm for developing simple and diverse biosensing methodologies.

Microfluidic Enrichment of Intact SARS-CoV-2 Viral Particles by Stoichiometric Balanced DNA Computation

Health diagnostic tools for community safety and environmental monitoring require selective and quantitatively accurate active viral load assessment. Herein, we report a microfluidic enrichment strategy to separate intact SARS-CoV-2 particles by AND logic gate with inputs of cholesterol oligonucleotides for the envelope and aptamers for the spike viral proteins. Considering the unequal quantity of endogenous spikes and lipid membranes on SARS-CoV-2, a dual-domain binding strategy, with two aptamers targeting different spike domains, was applied to balance the spike-envelope stoichiometric ratio. By balancing the stoichiometric with DNA computation and promoting microscale mass transfer of the herringbone chip, the developed strategy enabled high sensitivity detection of pseudotyped SARS-CoV-2 with a limit of detection as low as 37 active virions/μL while distinguishing it from inactive counterparts, other nontarget viruses, and free spike protein. Moreover, the captured viral particles can be released through DNase I treatment with up to 90% efficiency, which is fully compatible with virus culture and sequencing. Overall, the developed strategy not only identified SARS-CoV-2-infected patients (n = 14) with 100% identification from healthy donors (n = 8) but also provided a fresh perspective on the regulation of stoichiometric ratio to achieve a more biologically relevant DNA computation.

Coarse-grained model simulation-guided localized DNA signal amplification probe for miRNA detection

DNA-based enzyme-free signal amplification strategies are widely employed to detect biomarkers in low abundance. To enhance signal amplification, localized DNA reaction units which increases molecular collision probability is commonly utilized. However, the current understanding of the structure-function relationships in localized DNA signal amplification probes is limited, leading to unsatisfied performance. In this study, we introduced a coarse-grained molecular model to simulate the dynamic behavior of two DNA reaction units within a DNA enzyme-free signal amplification circuit called Localized Catalytic Hairpin Assembly (LCHA). We investigated the impact of localized distance and flexibility on reaction performance. The most efficient LCHA probe guided by simulation exhibits sensitivity 28 times greater that of free CHA, with a detection limit of miR-21 reaching 16 pM, while the least effective LCHA probe demonstrated a modest improvement of only 7 times. We successfully employed the optimized probe to differentiate cancer cells from normal cells based on their miR-21 expression levels, showcasing its quantification ability. By elucidating the mechanistic insights and structure-function relationship in our work, we aim to contribute valuable information that can save users' time and reduce costs when designing localized DNA probes. With a comprehensive understanding of how the localization affects probe performance, researchers can now make more informed and efficient decisions during the design process. This work would find broad applications of DNA nanotechnology in biosensing, biocomputing, and bionic robots.

A geometric framework for reaction enumeration in computational nucleic acid devices

Cascades of DNA strand displacement reactions enable the design of potentially large circuits with complex behaviour. Computational modelling of such systems is desirable to enable rapid design and analysis. In previous work, the expressive power of graph theory was used to enumerate reactions implementing strand displacement across a wide range of complex structures. However, coping with the rich variety of possible graph-based structures required enumeration rules with complicated side-conditions. This paper presents an alternative approach to tackle the problem of enumerating reactions at domain level involving complex structures by integrating with a geometric constraint solving algorithm. The rule sets from previous work are simplified by replacing side-conditions with a general check on the geometric plausibility of structures generated by the enumeration algorithm. This produces a highly general geometric framework for reaction enumeration. Here, we instantiate this framework to solve geometric constraints by a structure sampling approach in which we randomly generate sets of coordinates and check whether they satisfy all the constraints. We demonstrate this system by applying it to examples from the literature where molecular geometry plays an important role, including DNA hairpin and remote toehold reactions. This work therefore enables integration of reaction enumeration and structural modelling.

Enhanced DNA Entropy-Driven Circuit by Locked Nucleic Acids and Simulation-Guided Localization

Signal amplification methods based on DNA molecular interactions are promising tools for detecting various biomarkers in low abundance. The entropy-driven circuit (EDC), as an enzyme-free signal amplification method, has been used in detecting and imaging a variety of biomarkers. The localization strategy can effectively increase the local concentration of the DNA reaction modules to improve the signal amplification effect. However, the localization strategy may also amplify the leak reaction of the EDC, and effective signal amplification can be limited by the unclear structure-function relationship. Herein, we utilized locked nucleic acid (LNA) modification to enhance the stability of the localized entropy-driven circuit (LEDC), which suppressed a 94.6% leak signal. The coarse-grained model molecular simulation was used to guide the structure design of the LEDC, and the influence of critical factors such as the localized distance and spacer length was analyzed at the molecular level to obtain the best reaction performance. The sensitivities of miR-21 and miR-141 detected by a simulation-guided optimal LEDC probe were 17.45 and 65 pM, 1345 and 521 times higher than free-EDC, respectively. The LEDC was further employed for the fluorescence imaging of miRNA in cancer cells, showing excellent specificity and sensitivity. This work utilizes LNA and molecular simulations to comprehensively improve the performance of a localized DNA signal amplification circuit, providing an advanced DNA probe design strategy for biosensing and imaging as well as valuable information for the designers of DNA-based probes.

DNA droplets for intelligent and dynamical artificial cells: from the viewpoint of computation and non-equilibrium systems

Living systems are molecular assemblies whose dynamics are maintained by non-equilibrium chemical reactions. To date, artificial cells have been studied from such physical and chemical viewpoints. This review briefly gives a perspective on using DNA droplets in constructing artificial cells. A DNA droplet is a coacervate composed of DNA nanostructures, a novel category of synthetic DNA self-assembled systems. The DNA droplets have programmability in physical properties based on DNA base sequence design. The aspect of DNA as an information molecule allows physical and chemical control of nanostructure formation, molecular assembly and molecular reactions through the design of DNA base pairing. As a result, the construction of artificial cells equipped with non-equilibrium behaviours such as dynamical motions, phase separations, molecular sensing and computation using chemical energy is becoming possible. This review mainly focuses on such dynamical DNA droplets for artificial cell research in terms of computation and non-equilibrium chemical reactions.

Supervised Learning in a Multilayer, Nonlinear Chemical Neural Network

The development of programmable or trainable molecular circuits is an important goal in the field of molecular programming. Multilayer, nonlinear, artificial neural networks are a powerful framework for implementing such functionality in a molecular system, as they are provably universal function approximators. Here, we present a design for multilayer chemical neural networks with a nonlinear hyperbolic tangent transfer function. We use a weight perturbation algorithm to train the neural network which uses a simple construction to directly approximate the loss derivatives required for training. We demonstrate the training of this system to learn all 16 two-input binary functions from a common starting point. This work thus introduces new capabilities in the field of adaptive and trainable chemical reaction network (CRN) design. It also opens the door to potential future experimental implementations, including DNA strand displacement reactions.

DNA-based programmable gate arrays for general-purpose DNA computing

The past decades have witnessed the evolution of electronic and photonic integrated circuits, from application specific to programmable1,2. Although liquid-phase DNA circuitry holds the potential for massive parallelism in the encoding and execution of algorithms3,4, the development of general-purpose DNA integrated circuits (DICs) has yet to be explored. Here we demonstrate a DIC system by integration of multilayer DNA-based programmable gate arrays (DPGAs). We find that the use of generic single-stranded oligonucleotides as a uniform transmission signal can reliably integrate large-scale DICs with minimal leakage and high fidelity for general-purpose computing. Reconfiguration of a single DPGA with 24 addressable dual-rail gates can be programmed with wiring instructions to implement over 100 billion distinct circuits. Furthermore, to control the intrinsically random collision of molecules, we designed DNA origami registers to provide the directionality for asynchronous execution of cascaded DPGAs. We exemplify this by a quadratic equation-solving DIC assembled with three layers of cascade DPGAs comprising 30 logic gates with around 500 DNA strands. We further show that integration of a DPGA with an analog-to-digital converter can classify disease-related microRNAs. The ability to integrate large-scale DPGA networks without apparent signal attenuation marks a key step towards general-purpose DNA computing.

Temporal logic circuits implementation using a dual cross-inhibition mechanism based on DNA strand displacement

Molecular circuits crafted from DNA molecules harness the inherent programmability and biocompatibility of DNA to intelligently steer molecular machines in the execution of microscopic tasks. In comparison to combinational circuits, DNA-based temporal circuits boast supplementary capabilities, allowing them to proficiently handle the omnipresent temporal information within biochemical systems and life sciences. However, the lack of temporal mechanisms and components proficient in comprehending and processing temporal information presents challenges in advancing DNA circuits that excel in complex tasks requiring temporal control and time perception. In this study, we engineered temporal logic circuits through the design and implementation of a dual cross-inhibition mechanism, which enables the acceptance and processing of temporal information, serving as a fundamental building block for constructing temporal circuits. By incorporating the dual cross-inhibition mechanism, the temporal logic gates are endowed with cascading capabilities, significantly enhancing the inhibitory effect compared to a cross-inhibitor. Furthermore, we have introduced the annihilation mechanism into the circuit to further augment the inhibition effect. As a result, the circuit demonstrates sensitive time response characteristics, leading to a fundamental improvement in circuit performance. This architecture provides a means to efficiently process temporal signals in DNA strand displacement circuits. We anticipate that our findings will contribute to the design of complex temporal logic circuits and the advancement of molecular programming.

Accelerated Hybridization Chain Reaction Kinetics Using Poly DNA Tetrahedrons and Its Application in Detection of Aflatoxin B1

Traditional hybridization chain reaction (HCR) as a popular isothermal amplification technique shows some inevitable disadvantages in bioanalysis due to its relatively slow kinetics, which could be markedly promoted when the HCR initiator occurs under tension. Herein, a poly DNA tetrahedrons (pTDNs)-mediated HCR was successfully constructed to make its initiator in a stretched state by long-range electrostatic forces owing to the superimposed electrostatic interactions derived from the synthesized pTDNs, and it was hypothesized that it could remarkably enhance HCR performance, which was testified by theoretical simulations and experimental studies. Consequently, pTDNs-mediated HCR was applied to develop a novel immunoassay for rapid and sensitive detection of aflatoxin B1 as a proof-of-concept, and its signal amplification was attributed to the increased G4 DNAzyme that loaded on the second antibody. Our work paves a promising way using simple DNA frameworks alone to heighten HCR kinetics for reaction speed improvement and signal amplification in bioanalysis.

Accelerating DNA computing via freeze-thaw cycling

DNA computing harnesses the immense potential of DNA molecules to enable sophisticated and transformative computational processes but is hindered by low computing speed. Here, we propose freeze-thaw cycling as a simple yet powerful method for high-speed DNA computing without complex procedures. Through iterative cycles, we achieve a substantial 20-fold speed enhancement in basic strand displacement reactions. This acceleration arises from the utilization of eutectic ice phase as a medium, temporarily increasing the effective local concentration of molecules during each cycle. In addition, the acceleration effect follows the Hofmeister series, where kosmotropic anions such as sulfate (SO42-) reduce eutectic phase volume, leading to a more notable enhancement in strand displacement reaction rates. Leveraging this phenomenon, freeze-thaw cycling demonstrates its generalizability for high-speed DNA computing across various circuit sizes, achieving up to a remarkable 120-fold enhancement in reaction rates. We envision its potential to revolutionize molecular computing and expand computational applications in diverse fields.

The rate-limiting procedure of 3D DNA walkers and their applications in tandem technology

DNA walkers, artificial dynamic DNA nanomachines, can mimic actin to move rapidly along a predefined nucleic acid track. They can generally be classified as one- (1D), two- (2D), and three-dimensional (3D) DNA walkers. In particular, 3D DNA walkers demonstrate amazing sustainable walking ability, strong enrichment ability, and fantastic signal amplification ability. In light of these, 3D DNA walkers have been widely used in fields such as biosensors, bioanalysis and cell imaging. Most notably, the strong compatibility of 3D DNA walkers allows their integration with a range of amplification strategies, effectively enhancing signal transduction and amplifying biosensor sensing signals. Herein, we first systematically expound the walking principle of the 3D walkers in this review. Then, by presenting representative examples, the research direction of 3D walkers in recent years is discussed. Furthermore, we also categorize and evaluate diverse tandem signal amplification strategies in 3D walkers. Finally, the challenges and development trends of 3D DNA walkers in the emerging field of analysis are carefully discussed. It is believed that this work can provide new ideas for researchers to quickly understand 3D DNA walkers and their applications in diverse biosensors.

DNA Origami Tessellations

Molecular tessellation research aims to elucidate the underlying principles that govern intricate patterns in nature and to leverage these principles to create precise and ordered structures across multiple scales, thereby facilitating the emergence of novel functionalities. DNA origami nanostructures are excellent building blocks for constructing tessellation patterns. However, the size and complexity of DNA origami tessellation systems are currently limited by several unexplored factors relevant to the accuracy of essential design parameters, the applicability of design strategies, and the compatibility between different tiles. Here, we present a general method for creating DNA origami tiles that grow into tessellation patterns with micrometer-scale order and nanometer-scale precision. Interhelical distance (D) was identified as a critical design parameter determining tile conformation and tessellation outcome. Finely tuned D facilitated the accurate geometric design of monomer tiles with minimized curvature and improved tessellation capability, enabling the formation of single-crystalline lattices ranging from tens to hundreds of square micrometers. The general applicability of the design method was demonstrated by 9 tile geometries, 15 unique tile designs, and 12 tessellation patterns covering Platonic, Laves, and Archimedean tilings. Particularly, we took two strategies to increase the complexity of DNA origami tessellation, including reducing the symmetry of monomer tiles and coassembling tiles of different geometries. Both yielded various tiling patterns that rivaled Platonic tilings in size and quality, indicating the robustness of the optimized tessellation system. This study will promote DNA-templated, programmable molecular and material patterning and open up new opportunities for applications in metamaterial engineering, nanoelectronics, and nanolithography.

A molecular assessment of the practical potential of DNA-based computation

The immense information density of DNA and its potential for massively parallelized computations, paired with rapidly expanding data production and storage needs, have fueled a renewed interest in DNA-based computation. Since the construction of the first DNA computing systems in the 1990s, the field has grown to encompass a diverse array of configurations. Simple enzymatic and hybridization reactions to solve small combinatorial problems transitioned to synthetic circuits mimicking gene regulatory networks and DNA-only logic circuits based on strand displacement cascades. These have formed the foundations of neural networks and diagnostic tools that aim to bring molecular computation to practical scales and applications. Considering these great leaps in system complexity as well as in the tools and technologies enabling them, a reassessment of the potential of such DNA computing systems is warranted.

Cascaded, Feedback-Driven, and Spatially Localized Emergence of Constitutional Dynamic Networks Driven by Enzyme-Free Catalytic DNA Circuits

The enzyme-free catalytic hairpin assembly (CHA) process is introduced as a functional reaction module for guided, high-throughput, emergence, and evolution of constitutional dynamic networks, CDNs, from a set of nucleic acids. The process is applied to assemble networks of variable complexities, functionalities, and spatial confinement, and the systems provide possible mechanistic pathways for the evolution of dynamic networks under prebiotic conditions. Subjecting a set of four or six structurally engineered hairpins to a promoter P₁ leads to the CHA-guided emergence of a [2 × 2] CDN or the evolution of a [3 × 3] CDN, respectively. Reacting of a set of branched three-arm DNA-hairpin-functionalized junctions to the promoter strand activates the CHA-induced emergence of a three-dimensional (3D) CDN framework emulating native gene regulatory networks. In addition, activation of a two-layer CHA cascade circuit or a cross-catalytic CHA circuit and cascaded driving feedback-driven evolution of CDNs are demonstrated. Also, subjecting a four-hairpin-modified DNA tetrahedron nanostructure to an auxiliary promoter strand simulates the evolution of a dynamically equilibrated DNA tetrahedron-based CDN that undergoes secondary fueled dynamic reconfiguration. Finally, the effective permeation of DNA tetrahedron structures into cells is utilized to integrate the four-hairpin-functionalized tetrahedron reaction module into cells. The spatially localized miRNA-triggered CHA evolution and reconfiguration of CDNs allowed the logic-gated imaging of intracellular RNAs. Beyond the bioanalytical applications of the systems, the study introduces possible mechanistic pathways for the evolution of functional networks under prebiotic conditions.

Modular reconfiguration of DNA origami assemblies using tile displacement

The power of natural evolution lies in the adaptability of biological organisms but is constrained by the time scale of genetics and reproduction. Engineeringartificial molecular machines should not only include adaptability as a core feature but also apply it within a larger design space and at a faster time scale. A lesson from engineering electromechanical robots is that modular robots can perform diverse functions through self-reconfiguration, a large-scale form of adaptation. Molecular machines made of modular, reconfigurable components may form the basis for dynamic self-reprogramming in future synthetic cells. To achieve modular reconfiguration in DNA origami assemblies, we previously developed a tile displacement mechanism in which an invader tile replaces another tile in an array with controlled kinetics. Here, we establish design principles for simultaneous reconfigurations in tile assemblies using complex invaders with distinct shapes. We present toehold and branch migration domain configurations that expand the design space of tile displacement reactions by two orders of magnitude. We demonstrate the construction of multitile invaders with fixed and variable sizes and controlled size distributions. We investigate the growth of three-dimensional (3D) barrel structures with variable cross sections and introduce a mechanism for reconfiguring them into 2D structures. Last, we show an example of a sword-shaped assembly transforming into a snake-shaped assembly, illustrating two independent tile displacement reactions occurring concurrently with minimum cross-talk. This work serves as a proof of concept that tile displacement could be a fundamental mechanism for modular reconfiguration robust to temperature and tile concentration.

Encoding, Decoding, and Rendering Information in DNA Nanoswitch Libraries

DNA-based construction allows the creation of molecular devices that are useful in information storage and processing. Here, we combine the programmability of DNA nanoswitches and stimuli-responsive conformational changes to demonstrate information encoding and graphical readout using gel electrophoresis. We encoded information as 5-bit binary codes for alphanumeric characters using a combination of DNA and RNA inputs that can be decoded using molecular stimuli such as a ribonuclease. We also show that a similar strategy can be used for graphical visual readout of alphabets on an agarose gel, information that is encoded by nucleic acids and decoded by a ribonuclease. Our method of information encoding and processing could be combined with DNA actuation for molecular computation and diagnostics that require a nonarbitrary visual readout.

A Theoretical Framework for Implementable Nucleic Acids Feedback Systems

Chemical reaction networks can be utilised as basic components for nucleic acid feedback control systems' design for Synthetic Biology application. DNA hybridisation and programmed strand-displacement reactions are effective primitives for implementation. However, the experimental validation and scale-up of nucleic acid control systems are still considerably falling behind their theoretical designs. To aid with the progress heading into experimental implementations, we provide here chemical reaction networks that represent two fundamental classes of linear controllers: integral and static negative state feedback. We reduced the complexity of the networks by finding designs with fewer reactions and chemical species, to take account of the limits of current experimental capabilities and mitigate issues pertaining to crosstalk and leakage, along with toehold sequence design. The supplied control circuits are quintessential candidates for the first experimental validations of nucleic acid controllers, since they have a number of parameters, species, and reactions small enough for viable experimentation with current technical capabilities, but still represent challenging feedback control systems. They are also well suited to further theoretical analysis to verify results on the stability, performance, and robustness of this important new class of control systems.

Point-and-shoot Strategy based on Enzyme-assisted DNA "Paper-Cutting" to Construct Arbitrary Planar DNA Nanostructures

DNA self-assembly provides a "bottom-up" route to fabricating complex shapes on the nanometer scale. However, each structure needs to be designed separately and carried out by professionally trained technicians, which seriously restricts its development and application. Herein, a point-and-shoot strategy based on enzyme-assisted DNA "paper-cutting" to construct planar DNA nanostructures using the same DNA origami as the template is reported. Precisely modeling the shapes with high precision in the strategy based on each staple strand of the desired shape structure hybridizes with its nearest neighbor fragments from the long scaffold strand. As a result, some planar DNA nanostructures by one-pot annealing the long scaffold strand and selected staple strands is constructed. The point-and-shoot strategy of avoiding DNA origami staple strands' re-designing based on different shapes breaks through the shape complexity limitation of the planar DNA nanostructures and enhances the simplicity of design and operation. Overall, the strategy's simple operability and great generality enable it to act as a candidate tool for manufacturing DNA nanostructures.

Prime factorization via localized tile assembly in a DNA origami framework

Modern cybersecurity built on public-key cryptosystems like Rivest-Shamir-Adleman is compromised upon finding solutions to the prime factorization. Nevertheless, solving the prime factorization problem, given a large N, remains computationally challenging. Here, we design DNA origami frameworks (DOFs) to direct localized assembly of double-crossover (DX) tiles for solving prime factorization with a model consisting of the computing, decision-making, and reporting motifs. The model implementation is based on the sequential assembly of different DX tiles in the DOF cavity that carries overhangs encoding the prime and composite integers. The primes are multiplied and then verified with the composite, and the result is visualized under atomic force microscopy via the presence (success) or absence (failure) of biotin-streptavidin labels on the reporting DX tile. The factorization of semiprimes 6 and 15 is realized with this DOF-based demonstration. Given the potential of massively parallel processing ability of DNA, this strategy opens an avenue to solve complex mathematical puzzles like prime factoring with molecular computing.

High-fidelity imaging of intracellular microRNA via a bioorthogonal nanoprobe

The spatiotemporal visualization of intracellular microRNA (miRNA) plays a critical role in the diagnosis and treatment of malignant disease. Although DNAzyme-based biosensing has been regarded as the most promising candidate, inefficient analytical resolution is frequently encountered. Here, we propose a bioorthogonal approach toward high-fidelity imaging of intracellular miRNA by designing a multifunctional nanoprobe that integrates MnO₂ nanosheet-mediated intracellular delivery and activation by a fat mass and obesity-associated protein (FTO)-switched positive feedback. MnO₂ nanosheets facilitate nanoprobe delivery and intracellular DNAzyme cofactors are released upon glutathione-triggered reduction. Meanwhile, an m6A-caged DNAzyme probe could be bioorthogonally activated by intracellular FTO to eliminate potential off-target activation. Therefore, the activated DNAzyme probe and substrate probe could recognize miRNA to perform cascade signal amplification in the initiation of the release of Mn²⁺ from MnO₂ nanosheets. This strategy realized high-fidelity imaging of intracellular aberrant miRNA within tumor cells with a satisfactory detection limit of 9.7 pM, paving the way to facilitate clinical tumor diagnosis and prognosis monitoring.

Mechanical DNA Origami to Investigate Biological Systems

The ability to self-assemble DNA nanodevices with programmed structural dynamics that can sense and respond to the local environment can enable transformative applications in fields including mechanobiology and nanomedicine. The responsive function of biomolecules is often driven by alterations in conformational distributions mediated by highly sensitive interactions with the local environment. In this review, the current state-of-the-art in constructing complex DNA geometries with dynamic and mechanical properties to enable a molecular scale force measurement is first summarized. Next, an overview of engineering modular DNA devices that interact with cell surfaces is highlighted detailing examples of mechanosensitive proteins and the force-induced dynamic molecular interaction on the downstream biochemical signaling. Finally, the challenges and an outlook on this promising class of DNA devices acting as nanomachines to operate at a low piconewton range suitable for a majority of biological effects or as hybrid materials to achieve higher tension exertion required for other biological investigations, are discussed.

Design and Simulation of an Autonomous Molecular Mechanism Using Spatially Localized DNA Computation

As a well-established technique, DNA synthesis offers interesting possibilities for designing multifunctional nanodevices. The micro-processing system of modern semiconductor circuits is dependent on strategies organized on silicon chips to achieve the speedy transmission of substances or information. Similarly, spatially localized structures allow for fixed DNA molecules in close proximity to each other during the synthesis of molecular circuits, thus providing a different strategy that of opening up a remarkable new area of inquiry for researchers. Herein, the Visual DSD (DNA strand displacement) modeling language was used to design and analyze the spatially organized DNA reaction network. The execution rules depend on the hybridization reaction caused by directional complementary nucleotide sequences. A series of DNA strand displacement calculations were organized on the locally coded travel track, and autonomous movement and addressing operations are gradually realized. The DNA nanodevice operates in this manner follows the embedded "molecular program", which improves the reusability and scalability of the same sequence domain in different contexts. Through the communication between various building blocks, the DNA device-carrying the target molecule moves in a controlled manner along the programmed track. In this way, a variety of molecular functional group transport and specific partition storage can be realized. The simulation results of the visual DSD tool provide qualitative and quantitative proof for the operation of the system.

Molecular Computation for Molecular Classification

DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.

Kinetics-accelerated one-step detection of MicroRNA through spatially localized reactions based on DNA tile self-assembly

The localization of isothermal amplification systems has elicited extensive attention due to the enhanced reaction kinetics when detecting ultra-trace small-molecule nucleic acids. Therefore, the seek for an appropriate localization cargo of spatially confined reactions is urgent. Herein, we have developed a novel approach to localize the catalytic hairpin assembly (CHA) system into the DNA tile self-assembly nanostructure. Thanks to the precise programming and robust probe loading capacity, this strategy achieved a 2.3 × 10⁵-fold higher local reaction concentration than a classical CHA system with enhanced reaction kinetics in theory. From the experimental results, this strategy could reach the reaction plateau faster and get access to a magnified effect of 1.57-6.99 times higher in the linear range of microRNA (miRNA) than the simple CHA system. Meanwhile, this strategy satisfied the demand for the one-step detection of miRNA in cell lysates at room temperature with good sensitivity and specificity. These features indicated its excellent potential for ultra-trace molecule detection in clinical diagnosis and provided new insights into the field of bioassays based on DNA tile self-assembly nanotechnology.

DNA Origami: Recent Progress and Applications

This chapter explores the basic concept of DNA origami and its various types. By showing the progress made in structural DNA nanotechnology during the last 15 years, the chapter draws attention to the capability of DNA origami to construct complex structures in both 2D and 3D level. As well as looking at a few examples of dynamic DNA nanostructures, the chapter also explores the possible applications of DNA origami in different fields, such as biological computing, nanorobotics, and DNA walkers.

Lanthanide Encoded Logically Gated Micromachine for Simultaneous Detection of Nucleic Acids and Proteins by Elemental Mass Spectrometry

DNA-based logic computing potentially for analysis of biomarker inputs and generation of oligonucleotide signal outputs is of great interest to scientists in diverse areas. However, its practical use for sensing of multiple biomarkers is limited by the universality and robustness. Based on a proximity assay, a lanthanide encoded logically gated micromachine (LGM-Ln) was constructed in this work, which is capable of responding to multiplex inputs in biological matrices. Under the logic function controls triggered by inputs and a Boolean "AND" algorithm, it is followed by an amplified "ON" signal to indicate the analytes (inputs). In this logically gated sensing system, the whole computational process does not involve strand displacement in an intermolecular reaction, and a threshold-free design is employed to generate the 0 and 1 computation via intraparticle cleavage, which facilitates the computation units and makes the "computed values" more reliable. By simply altering the affinity ligands for inputs' biorecognition, LGM-Ln can also be extended to multi-inputs mode and produce the robust lanthanide encoded outputs in the whole human serum for sensing nucleic acids (with the detection limit of 10 pM) and proteins (with the detection limit of 20 pM). Compared with a logically gated micromachine encoded with fluorophores, the LGM-Ln has higher resolution and no spectral overlaps for multiple inputs, thus holding great promise in multiplex analyses and clinical diagnosis.