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DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

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Enzyme-driven triplex structure-based DNA logic circuits.

Xiao Liu^1,2, Jing Zhang^1,3, Xuehao Zhang⁴

¹Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.

Journal of Nanobiotechnology

|December 24, 2025

Summary

This study introduces enzyme-powered triplex DNA logic circuits, improving biological computing with reduced complexity and faster reaction rates. These circuits offer efficient DNA computing for various applications.

Keywords:

DNA computing DNA logic circuits DNA nanotechnology

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Area of Science:

Biotechnology
Synthetic Biology
Molecular Computing

Background:

Existing DNA logic circuits, like strand-displacement and enzyme-driven systems, face challenges including high strand complexity, signal leakage, and limited scalability.
These limitations hinder the development of intricate biological computing systems.

Purpose of the Study:

To introduce and validate enzyme-powered triplex DNA logic circuits for enhanced biological computing.
To address limitations of existing DNA computing systems by simplifying strand design and improving operational efficiency.

Main Methods:

Development of enzyme-powered triplex DNA logic circuits utilizing Bst 3.0 polymerase.
Input-gate concatenation to form triplex structures, simplifying strand design.
Validation through single-gate analysis, multi-level cascades, complex logic circuits, and square root operations.

Main Results:

Single-gate circuits demonstrated <2-min half-completion times with minimal leakage.
Cascaded circuits showed minimal leakage and <8-min half-completion times.
10-gate square root circuits operated in <25-min, featuring 24.3% reduced strand complexity and 25% faster reaction rates.

Conclusions:

Enzyme-powered triplex DNA logic circuits offer a low-leakage architecture for efficient DNA computing.
The modular design supports scalable biological computing networks for applications in biosensing, data storage, and synthetic biology.