DNA-based programmable gate arrays for general-purpose DNA computing

Hui Lv, Nuli Xie, Mingqiang Li, Mingkai Dong, Chenyun Sun, Qian Zhang, Lei Zhao, Jiang Li, Xiaolei Zuo, Haibo Chen, Fei Wang () and Chunhai Fan ()
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Hui Lv: Shanghai Jiao Tong University
Nuli Xie: Shanghai Jiao Tong University
Mingqiang Li: Shanghai Jiao Tong University
Mingkai Dong: Shanghai Jiao Tong University
Chenyun Sun: Shanghai Jiao Tong University
Qian Zhang: Shanghai Jiao Tong University
Lei Zhao: Shanghai Jiao Tong University
Jiang Li: The Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences
Xiaolei Zuo: Shanghai Jiao Tong University
Haibo Chen: Shanghai Jiao Tong University
Fei Wang: Shanghai Jiao Tong University
Chunhai Fan: Shanghai Jiao Tong University

Nature, 2023, vol. 622, issue 7982, 292-300

Abstract: Abstract The past decades have witnessed the evolution of electronic and photonic integrated circuits, from application specific to programmable1,2. Although liquid-phase DNA circuitry holds the potential for massive parallelism in the encoding and execution of algorithms3,4, the development of general-purpose DNA integrated circuits (DICs) has yet to be explored. Here we demonstrate a DIC system by integration of multilayer DNA-based programmable gate arrays (DPGAs). We find that the use of generic single-stranded oligonucleotides as a uniform transmission signal can reliably integrate large-scale DICs with minimal leakage and high fidelity for general-purpose computing. Reconfiguration of a single DPGA with 24 addressable dual-rail gates can be programmed with wiring instructions to implement over 100 billion distinct circuits. Furthermore, to control the intrinsically random collision of molecules, we designed DNA origami registers to provide the directionality for asynchronous execution of cascaded DPGAs. We exemplify this by a quadratic equation-solving DIC assembled with three layers of cascade DPGAs comprising 30 logic gates with around 500 DNA strands. We further show that integration of a DPGA with an analog-to-digital converter can classify disease-related microRNAs. The ability to integrate large-scale DPGA networks without apparent signal attenuation marks a key step towards general-purpose DNA computing.

Date: 2023
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DOI: 10.1038/s41586-023-06484-9

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