Current molecular logic gates are predominantly focused on the qualitative assessment of target presence, which has certain limitations in scenarios requiring quantitative assessment, such as chemical contaminant monitoring. To bridge this gap, we have developed a novel DNA logic gate featuring a tunable threshold, specifically tailored to the limits of contaminants. At the core of this logic gate is a DNA-gold nanoparticle (AuNP) hybrid film that incorporates aptamer sequences to selectively bind to acetamiprid (ACE) and atrazine (ATR). Upon interaction with these contaminants, the film degrades, releasing AuNPs that, in the presence of Hg2+, catalyze the oxidation of TMB, resulting in a visible blue coloration on test paper. This aptamer-enabled process effectively establishes an OR logic gate, with ACE and ATR as inputs and the appearance of blue color as the output. A key innovation of our system is its tunable input threshold. By adjusting the concentration of Hg2+, we can fine-tune the color mutation points to match the input threshold to predefined limits, such as Maximum Residue Limits (MRLs). This alignment allows semiquantitative assessment of contaminant levels, providing intuitive visual feedback of contaminant exceedance. Validation experiments with spiked samples confirm its accuracy and reliability by closely matching HPLC results. Therefore, our colorimetric DNA logic gate is emerging as a promising tool for easy and semiquantitative monitoring of chemical contaminants across diverse applications.

Due to the complexity of regulatory networks of disease-related biomarkers, developing simple, sensitive, and accurate methods has remained challenging for precise diagnosis. Herein, an \"AND\" logic gates DNA molecular machine (LGDM) was constructed, which was powered by the catalytic hairpin assembly (CHA). It was coupled with dual-emission CdTe quantum dots (QDs)-based cation exchange reaction (CER) for label-free, sensitive, and ratiometric fluorescence detection of APE1 and miRNA biomarkers. Benefiting from synergistic signal amplification strategies and a ratiometric fluorometric output mode, this LGDM enables accurate logic computing with robust and significant output signals from weak inputs. It offers improved sensitivity and selectivity even in cell extracts. Using dual-emission spectra CdTe QDs, with a ratiometric signal output mode, ensured good stability and effectively prevented false-positive signals from intrinsic biological interferences compared to the approach relying on a single signal output mode, which enabled the LGDM to achieve rapid, efficient, and accurate natural drug screening against APE1 inhibitors in vitro and cells. The developed method provides impetus to streamline research related to miRNA and APE1, offering significant promise for widespread application in drug development and clinical analysis.

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.

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.

DNA is commonly employed as a substrate for the building of artificial logic networks due to its excellent biocompatibility and programmability. Till now, DNA logic circuits are rapidly evolving to accomplish advanced operations. Nonetheless, nowadays, most DNA circuits remain to be disposable and lack of field programmability and thereby limits their practicability. Herein, inspired by the Configurable Logic Block (CLB), the CLB-based erasable field-programmable DNA circuit that uses clip strands as its operation-controlling signals is presented. It enables users to realize diverse functions with limited hardware. CLB-based basic logic gates (OR and AND) are first constructed and demonstrated their erasability and field programmability. Furthermore, by adding the appropriate operation-controlling strands, multiple rounds of programming are achieved among five different logic operations on a two-layer circuit. Subsequently, a circuit is successfully built to implement two fundamental binary calculators: half-adder and half-subtractor, proving that the design can imitate silicon-based binary circuits. Finally, a comprehensive CLB-based circuit is built that enables multiple rounds of switch among seven different logic operations including half-adding and half-subtracting. Overall, the CLB-based erasable field-programmable circuit immensely enhances their practicability. It is believed that design can be widely used in DNA logic networks due to its efficiency and convenience.

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.

DNA circuits, as a type of biochemical system, have the capability to synchronize the perception of molecular information with a chemical reaction response and directly process the molecular characteristic information in biological activities, making them a crucial area in molecular digital computing and smart bioanalytical applications. Instead of cascading logic gates, the traditional research approach achieves multiple logic operations which limits the scalability of DNA circuits and increases the development costs. Based on the interface reaction mechanism of Lambda exonuclease, the molecular perceptron proposed in this study, with the need for only adjusting weight and bias parameters to alter the corresponding logic expressions, enhances the versatility of the molecular circuits. We also establish a mathematical model and an improved heuristic algorithm for solving weights and bias parameters for arbitrary logic operations. The simulation and FRET experiment results of a series of logic operations demonstrate the universality of molecular perceptron. We hope the proposed molecular perceptron can introduce a new design paradigm for molecular circuits, fostering innovation and development in biomedical research related to biosensing, targeted therapy, and nanomachines.

The DNA-based logic circuit, constructed to mimic biochemical reaction networks, is highly significant in detecting biomarkers at the molecular level. The differences in the expression levels of microRNAs (miRNAs) within different types of cells provide hope for distinguishing cell subtypes. However, reliance on a single miRNA often leads to unreliable results. Herein, we constructed an enzyme-triggered cascade logic circuit based on the AND gate, which is capable of generating corresponding fluorescence signals in the presence of target miRNAs. The introduction of apurinic/apyrimidinic (AP) sites effectively reduces the likelihood of false signal generation. Amplification of the fluorescence signal relies on the catalytic hairpin assembly and the repetitive reuse of the multicomponent nucleic acid enzyme (MNAzyme). We demonstrated that the logic circuit can not only distinguish cancer cells from normal cells but also identify different types of cancer cells. The programmability of the logic circuits and the simplicity of the assay system allow us to modify the functional sequences to recognize different types of biomarkers, thus providing a reference for the identification of various cell subtypes.

With the advent of the post-Moore\'s Law era, the development of traditional silicon-based computers has reached its limit, and there is an urgent need to develop new computing technologies to meet the needs of science, technology, and daily life. Due to its super-strong parallel computing capability and outstanding data storage capacity, DNA computing has become an important branch and hot research topic of new computer technology. DNA enzyme-free hybridization reaction technology is widely used in DNA computing, showing excellent performance in computing power and information processing. Studies have shown that DNA molecules not only have the computing function of electronic devices, but also exhibit certain human brain-like functions. In the field of artificial intelligence, activation functions play an important role as they enable artificial intelligence systems to fit and predict non-linear and complex variable relationships. Due to the difficulty of implementing activation functions in DNA computing, DNA circuits cannot easily achieve all the functions of artificial intelligence. DNA circuits need to rely on electronic computers to complete the training and learning process. Based on the parallel computing characteristics of DNA computing and the kinetic features of DNA molecule displacement reactions, this paper proposes a new activation function. This activation function can not only be easily implemented by DNA enzyme-free hybridization reaction reactions, but also has good nesting properties in DNA circuits, and can be cascaded with other DNA reactions to form a complete DNA circuit. This paper not only provides the mathematical analysis of the proposed activation function, but also provides a detailed analysis of its kinetic features. The activation function is then nested into a nonlinear neural network for DNA computing. This system is capable of fitting and predicting a certain nonlinear function.

DNA nanomaterials have a wide application prospect in biomedical field, among which DNA computers and biosensors based on Seesaw-based DNA circuit is considered to have the most development potential. However, the serious leakage of Seesaw-based DNA circuit prevented its further development and application. Moreover, the existing methods to suppress leakage can\'t achieve the ideal effect. Interestingly, we found a new source of leakage in Seesaw-based DNA circuit, which we think is the main reason why the previous methods to suppress leakage are not satisfactory. Therefore, based on this discovery, we use DNA triplex to design a new method to suppress the leakage of Seesaw-based DNA circuit. Its ingenious design makes it possible to perfectly suppress the leakage of all sources in Seesaw-based DNA circuit and ensure the normal output of the circuit. Based on this technology, we have constructed basic Seesaw module, AND gate, OR gate, secondary complex circuits and DNA detector. Experimental results show that we can increase the working range of the secondary Seesaw-based DNA circuit by five folds and keep its normal output signal above 90%, and we can improve the LOD of the Seesaw-based DNA detector to 1/11 of the traditional one(1.8pM). More importantly, we successfully developed a detector with adjustable detection range, which can theoretically achieve accurate detection in any concentration range. We believe the established triplex blocking strategy will greatly facilitate the most powerful Seesaw based DNA computers and biosensors, and further promote its application in biological systems.