Mapping Temporally Ordered Inputs to Binary Message Outputs with a DNA Temporal Logic Circuit

• Published in: Nanomaterials (Basel, Switzerland) Vol. 13; no. 5; p. 903
• Main Authors: Zhao, Shuai, Liu, Yuan, Zhang, Xiaokang, Qin, Rui, Wang, Bin, Zhang, Qiang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.02.2023; MDPI
• Subjects: Analysis; Biological activity; chemical reaction networks; Circuits; Communications circuits; Data processing; Deoxyribonucleic acid; DNA; DNA nanotechnology; DNA strand displacement; DNA temporal logic circuits; Encryption; Enzymes; Genetic testing; Information processing; Logic circuits; Mapping; Messages; Methods; molecular encryption; Neural networks; Signal processing; Simulation methods; Substrates; Temporal logic; China; United States > US; DNA nanotechnology; molecular encryption; DNA strand displacement; chemical reaction networks; DNA temporal logic circuits
• ISSN: 2079-4991; 2079-4991
• DOI: 10.3390/nano13050903

Molecular circuits and devices with temporal signal processing capability are of great significance for the analysis of complex biological processes. Mapping temporal inputs to binary messages is a process of history-dependent signal responses, which can help understand the signal-processing behavior of organisms. Here, we propose a DNA temporal logic circuit based on DNA strand displacement reactions, which can map temporally ordered inputs to corresponding binary message outputs. The presence or absence of the output signal is determined by the type of substrate reaction with the input so that different orders of inputs correspond to different binary outputs. We demonstrate that a circuit can be generalized to more complex temporal logic circuits by increasing or decreasing the number of substrates or inputs. We also show that our circuit had excellent responsiveness to temporally ordered inputs, flexibility, and expansibility in the case of symmetrically encrypted communications. We envision that our scheme can provide some new ideas for future molecular encryption, information processing, and neural networks.