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DNA as a Genetic Template

DNA as a Genetic Template

Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...

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Updated: Feb 20, 2026

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

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

Published on: December 29, 2021

Conformation-programmed DNA computing.

Qian Ling¹, Bozhao Li^2,3, Yuhua Feng^1,4

¹School of Computer Science, Peking University, Beijing 100871, China.

Science Advances

|February 18, 2026

Summary

This summary is machine-generated.

This study introduces an allosteric DNA computing framework that integrates sequence and conformational dynamics for advanced signal processing. This DNA computing approach expands signaling capabilities for synthetic biology and precision therapeutics.

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Area of Science:

Synthetic biology
Biophysics
DNA computing

Background:

Biological systems use multidimensional signaling for cellular control.
Current synthetic DNA networks primarily rely on sequence programmability, limiting their regulatory scope.
Integrating conformational dynamics offers a new dimension for DNA-based computation.

Purpose of the Study:

To develop an allosteric DNA computing framework combining sequence and conformational signals.
To enable integrated, multilevel signal processing in synthetic DNA networks.
To bridge DNA computing with genetic control for in vivo applications.

Main Methods:

Encoding conformational signals using polythymidine loops of varying lengths.
Utilizing catalytic allosteric hairpin assemblies for signal amplification.
Designing allosteric DNA neural networks for conformational signal discrimination.
Developing microRNA-responsive DNA frameworks for gene expression regulation.

Main Results:

The framework executes loop-dependent logic operations with expanded signaling ranges.
Catalytic assemblies achieved ~30-fold signal amplification and improved signal-to-noise ratios.
DNA neural networks discriminated conformational signals with two-nucleotide resolution based on loop lengths.
MicroRNA-responsive systems demonstrated in vivo gene expression regulation.

Conclusions:

Established a novel conformational signal-processing paradigm for adaptive DNA computing.
Demonstrated the potential for integrating sequence and conformational dynamics in DNA networks.
Paved the way for advanced synthetic biology applications and precision therapeutics.