IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-024-55527-w.html
   My bibliography  Save this article

Artificial molecular communication network based on DNA nanostructures recognition

Author

Listed:
  • Junke Wang

    (Nanjing University of Posts and Telecommunications
    Nanjing University of Posts and Telecommunications)

  • Mo Xie

    (Nanjing University of Posts and Telecommunications
    Nanjing University of Posts and Telecommunications)

  • Lilin Ouyang

    (Nanjing University of Posts and Telecommunications
    Nanjing University of Posts and Telecommunications)

  • Jinggang Li

    (Nanjing University of Posts and Telecommunications
    Nanjing University of Posts and Telecommunications)

  • Lianhui Wang

    (Nanjing University of Posts and Telecommunications
    Nanjing University of Posts and Telecommunications)

  • Chunhai Fan

    (Shanghai Jiao Tong University
    New Cornerstone Science Laboratory
    Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Jie Chao

    (Nanjing University of Posts and Telecommunications
    Nanjing University of Posts and Telecommunications)

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.

Suggested Citation

  • Junke Wang & Mo Xie & Lilin Ouyang & Jinggang Li & Lianhui Wang & Chunhai Fan & Jie Chao, 2025. "Artificial molecular communication network based on DNA nanostructures recognition," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55527-w
    DOI: 10.1038/s41467-024-55527-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-55527-w
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-55527-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55527-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.