Adjusting Linking Strands to Form Size-Controllable DNA Origami Rings

Instruction-responsive programmable assemblies with DNA origami block pieces

DNA nanotechnology has created a wide variety of nanostructures that provide a reliable platform for nanofabrication and DNA computing. However, constructing programmable finite arrays that allow for easy pre-functionalization remains challenge. We aim to create more standardized and controllable DNA origami components, which could be assembled into finite-scale and more diverse superstructures driven by instruction sets. In this work, we designed and implemented DNA origami building block pieces (DOBPs) with eight mutually independent programmable edges and formulated DNA instructions that tailored such components. This system enables DOBPs to be assembled into one or more specific 2D arrays according to the instruction sets. Theoretically, a two-unit system can generate up to 48 distinct DNA arrays. Importantly, experiments results demonstrated that DOBPs are capable of both deterministic and nondeterministic assemblies. Moreover, after examining the effects of different connection strategies and instruction implementations on the yield of the target structures, we assembled more complex 2D arrays, including limited self-assembly arrays such as 'square frames', 'windmills' and 'multiples of 3' long strips. We also demonstrated examples of Boolean logic gates 'AND' and 'XOR' computations based on these assembly arrays. The assembly system provides a model nano-structure for the research on controllable finite self-assembly and offers a more integrated approach for the storage and processing of molecular information.

DNA Origami Nanostructure Detection and Yield Estimation Using Deep Learning

DNA origami is a milestone in DNA nanotechnology. It is robust and efficient in constructing arbitrary two- and three-dimensional nanostructures. The shape and size of origami structures vary. To characterize them, an atomic force microscope, a transmission electron microscope, and other microscopes are utilized. However, the identification of various origami nanostructures heavily depends on the experience of researchers. In this study, we used the deep learning method (improved Yolox) to detect multiple DNA origami structures and estimate their yield. We designed a feature enhancement fusion network with the attention mechanism, and related parameters were researched. Experiments conducted to verify the proposed method showed that the detection accuracy was higher than that of other methods. This method can detect and estimate the DNA origami yield in complex environments, and the detection speed is in the millisecond range.

DNA computing for gastric cancer analysis and functional classification

Early identification of key biomarkers of malignant cancer is vital for patients' prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, we analyzed the expression data by using a weighted gene co-expression network (WGCNA) and found four biomarkers for two clinic features, respectively. Last, we constructed a DSD-based DNA computing system for cancer analysis. The inputs of the system are these identified biomarkers; the outputs are the fluorescent signals that represent their corresponding traits. The experiment and simulation results demonstrated the reliability of the DNA computing system. This DSD simulation system is lab-free but clinically meaningful. We expect this innovative method to be useful for rapid and accurate cancer diagnosis.

Advances in Applications of Molecular Logic Gates

Logic gates are devices that can perform Boolean logic operations and are the basic components of integrated circuits for information processing and storage. In recent years, molecular logic gates are gradually replacing traditional silicon-based electronic computers with their significant advantages and are used in research in water quality monitoring, heavy metal ion detection, disease diagnosis and treatment, food safety detection, and biological sensors. Logic gates at the molecular level have broad development prospects and huge development potential. In this review, the development and application of logic gates in various fields are used as the entry point to discuss the research progress of logic gates and logic circuits. At the same time, the application of logic gates in quite a few emerging fields is briefly summarized and predicted.

Optical and Topological Characterization of Hexagonal DNA Origami Nanotags

DNA origami can be applied as a "ruler" for nanoscale calibration or super-resolution fluorescence microscopy with an ideal structure for defining fluorophore arrangement, allowing the distance between fluorophores to be precisely controlled at the nanometer scale. DNA origami can also be used as a nanotag with arbitrary programmable shapes for topological identification. In this paper, we formed a hexagonal origami structure embedded with three different fluorescent dyes on the surface. The distance between each fluorescent block was ~120 nm, which is below the diffraction limit of light, allowing for its application as a nano-ruler for super-resolution fluorescence microscopy. The outside edge of the hexagonal structure was redesigned to form three different substructures as topological labels. Atomic and scanning force microscopy demonstrated consistency of the nanoscale distance between morphological and fluorescent labels. Therefore, this fluorophore-embedded hexagonal origami platform can be used as a dual nano-ruler for both optical and topological calibration.

DNA strand displacement reactions to accomplish a two-degree-of-freedom PID controller and its application in subtraction gate

Synthesis control circuits can be used to effectively control biochemical molecule processes. In the controller design based on chemical reaction networks (CRNs), generally only the tracking set-point is considered. However, the influence of disturbances, which are frequently encountered in biochemical systems, is often neglected, thus weakening the control effect of the system. In this article, tracking set-point input and suppressing disturbance input are considered in the control effect. Firstly, CRNs are adopted to construct a two-degree-of-freedom PID controller by combining a one-degree-of-freedom PID controller with a feedforward controller for the first time. Then, CRN expressions of the two input functions (step function and ramp function) used as input signals are defined. Furthermore, the two-degree-of-freedom PID controller is founded by DNA strand displacement (DSD) reaction networks, because DNA is an ideal engineering material to constitute molecular devices based on CRNs. The overshoot of the two-degree-of-freedom PID control system is significantly reduced compared to the one-degree-of-freedom PID control system. Finally, a leak reaction is treated as an extraneous disturbance input to a subtraction gate. The influence of external disturbance is solved by the two-degree-of-freedom PID controller. It is worth noting that the two-degree-of-freedom subtraction gate control system better restrains the impact of a disturbance input (leak reaction).