Wang Junke, Xie Mo, Ouyang Lilin, Li Jinggang, Wang Lianhui, Fan Chunhai, Chao Jie
Key Laboratory for Organic Electronics and Information Displays (KLOEID), Nanjing University of Posts and Telecommunications, Nanjing, China.
Jiangsu Key Laboratory for Biosensors, Nanjing University of Posts and Telecommunications, Nanjing, China.
Nat Commun. 2025 Jan 2;16(1):244. doi: 10.1038/s41467-024-55527-w.
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.
受生物体内分子通信启发的人工模拟通信网络利用生物和化学分子作为信息载体来实现信息传输。然而,可编程、多路复用和通用模拟模型的设计仍然具有挑战性。在此,我们开发了一种基于DNA纳米结构识别的人工分子通信网络(DR-AMCN),其中矩形DNA折纸纳米结构用作节点,它们的识别用作边。在实现了包括串行、并行、正交和多路复用等各种通信机制的DR-AMCN之后,它被应用于构建具有总线、环、星、树和混合结构的各种通信网络拓扑。通过建立基于DR-AMCN的用于路径遍历的节点划分算法,七节点哈密顿路径问题的计算复杂度得以降低,最终解决方案通过速率区带离心法直接获得,并且该方法的可扩展性也得到了证明。所开发的DR-AMCN增强了我们对生物体内信号转导机制、动态过程和调控网络的理解,有助于解决信息学和计算问题,并且在计算机科学、生物医学工程、信息技术及其他相关领域具有潜力。