Artificial molecular communication network based on DNA nanostructures recognition

Abstract

Artificial simulated communication networks inspired by molecular communication in organisms use biological and chemical molecules as information carriers to realize information transmission. However, the design of programmable, multiplexed and general simulation models remains challenging. Here, we develop a DNA nanostructure recognition-based artificial molecular communication network (DR-AMCN), in which rectangular DNA origami nanostructures serve as nodes and their recognition as edges. After the implementation of DR-AMCN with various communication mechanisms including serial, parallel, orthogonal, and multiplexing, it is applied to construct various communication network topologies with bus, ring, star, tree, and hybrid structures. By the establishment of a node partition algorithm for path traversal based on DR-AMCN, the computational complexity of the seven-node Hamiltonian path problem is reduced with the final solution directly obtained through the rate-zonal centrifugation method, and scalability of this approach is also demonstrated. The developed DR-AMCN enhances our understanding of signal transduction mechanisms, dynamic processes, and regulatory networks in organisms, contributing to the solution of informatics and computational problems, as well as having potential in computer science, biomedical engineering, information technology and other related fields.

Fig. 1. Establishment of DR-AMCN.

a The generic communication network model. Design of node, edge, and communication of DR-AMCN. b Diagrams and corresponding AFM images of results with varying numbers of connector pairs, including one, two, three, four, and six pairs. c Encoding rule of molecular identifier and implementation with DNA nanostructure. d Diagrams and corresponding AFM images of nodes 0–6. scale bars: 50 nm.

Fig. 2. Programmable communication mechanisms of DR-AMCN.

a Diagram of basic communication mechanisms, including one-to-one, one-to-many, and many-to-many communication. b–f Schematic, representative AFM images and product statistics of the communication, including directional communication between nodes N 0 and N 1. Sample size is 112, with 104 for P 01. b orthogonal communication between nodes N 0, N 4 and nodes N 1, N 2 generating path 0 → 1 (blue) and 4 → 2 (red). Sample size is 111, with 52 for P 01 and 51 for P 42. c, d parallel communication between node N 0 with N 1 or N 2 generating path 0 → 1 (blue) and 0 → 2 (red) without bias. Sample size is 224, with 58 for P 01 and 57 for P 02. e as well as multiplexed communication for path 0 → 1 (blue), 0 → 2 (red), 4 → 1 (golden), and 4 → 2 (purple) through a common connector. Sample size is 136, with 31 for P 01, 30 for P 02, 32 for P 41 and 34 for P 42. f The x in the product statistics represents deserted reactants and other side products. The yield of each path was calculated by counting the number of target DNA nanostructures over all nanostructures. Three AFM images were counted for each DNA nanostructure, and each image was from an independent experiment. Source data are provided as a Source Data file. Scale bars, 50 nm.

Fig. 3. The programmability and scalability of DR-AMCN for complex network topologies.

a–c, Diagrams of network topology, DNA nanostructure design, and communication paths with corresponding cropped AFM images for the bus (a), star (b), and tree (c) topology, respectively. Scale bars, 50 nm. d–f, Large range AFM image of the bus (d), star (e), and tree (f) topology, respectively. Scale bars, 200 nm. The distinct paths were indicated by boxes with distinct colors. In the bus topology, a DNA nanostructure pentamer represents P 012345. In the star topology, four types of DNA nanostructure dimers encode P 01 (blue), P 02 (red), P 03 (purple), and P 04 (golden), respectively. In the tree topology, four types of DNA nanostructure trimers encode P 013 (blue), P 014 (red), P 025 (purple), and P 026 (golden), respectively.

Fig. 4. Node partition algorithm for path traversal.

a The representation, adjacency matrix, and definition of nodes of the seven-node Hamilton graph G. The number of nonzero elements in the corresponding row is the outdegree (OD), and the number of nonzero elements in the corresponding column is the indegree (ID). b The node analysis and node partition of N 1. The red cross indicates the blocking the outgoing path. c Table of the node partition result for G. d The flow chart of node partition algorithm for graph traversal. e The path traversal tree for G.

Fig. 5. DR-AMCN-driven path traversal on a Hamiltonian graph.

a Experimental protocol and implementation of the path traversal. b Schematic diagram and representative AFM image for five paths in the Hamiltonian graph. Scale bars, 50 nm. c Example of a multiplexed AFM image chosen to highlight parallel DNA computing of DR-AMCN. The left side of the display showed a large range AFM image, while the cropped images highlight specific regions corresponding to the numbers in large range AFM image with recognizable paths marked.

Fig. 6. Solving the Hamiltonian path problem.

a Schematic illustration of rate-zonal centrifugation selection for separation of heptamer DNA nanostructure from mixed DNA nanostructures. b Experimental protocol of rate-zonal centrifugation. The mixed sample was loaded onto glycerin with a concentration gradient and centrifuged to collect 23 DNA fractions from top to bottom, which were subsequently analyzed by gel electrophoresis. c Gel analysis of products after centrifugation. Band 1–23 were DNA fractions, the band C represented mixed sample before centrifugation. d Large range and zoom-in AFM image of the band 15 from gel analysis. Four typical heptamer structures were marked with orange boxed and extracted to show the Hamiltonian path 0 → 1 → 2 → 3 → 4 → 5 → 6. Scale bars, 200 nm. Source data are provided as a Source Data file.