arrow
Return

A Programmed DNA Dynamic Assembly-Guided Molecular Amplifier for Authentic Information Decryption

delete2025-05-19
delete0
delete
OA
AI
N
Ning Yang
周杰 (Jie Zhou)
F
Fengying Yuan
刘亚娟 (Yajuan Liu)
J
Jia Zhang
卓颖 (Ying Zhuo)
欧阳煜 cover
欧阳煜 (Yu Ouyang)
柴雅琴 cover
柴雅琴 (Yaqin Chai)
张璞 cover
张璞 (Pu Zhang)
袁若 cover
袁若 (Ruo Yuan)
DOI:10.1002/advs.202409586delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Nucleic acid-based cryptographic approaches are an innovative emerging field for information process. However, the poor reproducibility and interference from bioenvironment of the existing decryption led to different binary translation according to the fixed threshold defined by Sender, which seriously affects the authenticity during message communication. Here, a programmed DNA constitutional dynamic network (CDN)-derived adaptive threshold is shown, which is defined by the difference value of the two groups of the output patterns from CDN. Under external stimuli, the threshold is adaptive to the generated dynamic output patterns, which avoids the contrary binary translation from a slight difference on the output under fixed threshold. Importantly, there are two self-calibrating patterns in each output group and the total concentration of constituents from the CDN system are constant, which greatly eliminates the data error. The CDN system is accompanied by computational simulation, which can predict the output patterns of the system at different states. The CDN is used to control the orthogonal and cascaded nanoparticle-based molecular amplifiers to expand the volume of the transmitting message, as well as allow the accurate and specific sensing of DNA. Various state-of-the-art representation is demonstrated by coding and decoding different types of messages.
Keywords:
DNA self-assembly
DNA decryption
DNA sensing
DNA nanotechnology
nanomachine

Journal

Advanced Science cover
Advanced Science
IF:
14.1
Papers:
1.8W
Citations:
11.5W

Organization

S
Southwest Univ
Scholars:
3.0K
Papers: 1.0K
Citations: 340