A supramolecular aggregation-based constitutional dynamic network for information processing

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.

Leveraging DNA-Based Nanostructures for Advanced Error Detection and Correction in Data Communication

This study demonstrates the implementation of the Hamming code using DNA-based nanostructures for error detection and correction in communication systems. The designed DNA nanostructures conduct logical operations to compute check codes and identify and correct erroneous data based on fluorescence signals. The execution of intricate DNA logic operations requires individuals with specialized training. By interpretation of the fluorescence signals generated by the DNA nanostructures, binary language can be extracted, effectively protecting data security. The findings highlight the potential of DNA as a versatile platform for reliable data transmission.

The Construction of DNA Logic Gates Restricted to Certain Live Cells Based on the Structure Programmability and Aptamer-Cell Affinity of G-Quadruplexes

DNA computation is considered a fascinating alternative to silicon-based computers; it has evoked substantial attention and made rapid advances. Besides realizing versatile functions, implementing spatiotemporal control of logic operations, especially at the cellular level, is also of great significance to the development of DNA computation. However, developing simple and efficient methods to restrict DNA logic gates performing in live cells is still a challenge. In this work, a series of DNA logic gates was designed by taking full advantage of the diversity and programmability of the G-quadruplex (G4) structure. More importantly, by further using the high affinity and specific endocytosis of cells to aptamer G4, an INHIBIT logic gate has been realized whose operational site is precisely restricted to specific live cells. The design strategy might have great potential in the field of molecular computation and smart bio-applications.

Information processing using an integrated DNA reaction network

Living organisms use interconnected chemical reaction networks (CRNs) to exchange information with the surrounding environment and respond to diverse external stimuli. Inspired by nature, numerous artificial CRNs with a complex information processing function have been recently introduced, with DNA as one of the most attractive engineering materials. Although much progress has been made in DNA-based CRNs in terms of controllable reaction dynamics and molecular computation, the effective integration of signal translation with information processing in a single CRN remains to be difficult. In this work, we introduced a stimuli-responsive DNA reaction network capable of integrated information translation and processing in a stepwise manner. This network is designed to integrate sensing, translation, and decision-making operations by independent modules, in which various logic units capable of performing different functions were realized, including information identification (YES and OR gates), integration (AND and AND-AND gates), integration-filtration (AND-AND-NOT gate), comparison (Comparator), and map-to-map analysis (Feynman gate). Benefitting from the modular and programmable design, continuous and parallel processing operations are also possible. With the innovative functions, we show that the DNA network is a highly useful addition to the current DNA-based CRNs by offering a bottom-up strategy to design devices capable of cascaded information processing with high efficiency.

Leveraging autocatalytic reactions for chemical domain image classification

Autocatalysis is fundamental to many biological processes, and kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use an autocatalytic reaction, the copper-catalyzed azide-alkyne cycloaddition, to perform digital image recognition tasks. Images are encoded in the concentration of a catalyst across an array of liquid samples, and the classification is performed with a sequence of automated fluid transfers. The outputs of the operations are monitored using UV-vis spectroscopy. The growing interest in molecular information storage suggests that methods for computing in chemistry will become increasingly important for querying and manipulating molecular memory.