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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: May 14, 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

A DNA-based in vitroGenetic Program.

J A Rose¹, M Hagiya, R J Deaton

¹Department of Computer Science, The Universityof Tokyo, Japan.

Journal of Biological Physics

|January 25, 2013

Summary

This summary is machine-generated.

This study introduces a DNA-based evolutionary approach to molecular computation, modifying PNA-mediated Whiplash PCR (PWPCR) to evolve finite state machines for solving complex problems like the Hamiltonian Path problem.

Keywords:

DNA computing Hamiltonian Path genetic program in vitro

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Last Updated: May 14, 2026

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

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Published on: December 29, 2021

Combining Analysis of DNA in a Crude Virion Extraction with the Analysis of RNA from Infected Leaves to Discover New Virus Genomes

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Published on: July 27, 2018

Area of Science:

Molecular computation
Biotechnology
Computational complexity

Background:

PNA-mediated Whiplash PCR (PWPCR) uses DNA hairpins for molecular computation but faces limitations due to exponential solution space scaling.
Exhaustive search methods struggle with realistic problem sizes in molecular computation.
Evolutionary approaches may overcome limitations of exhaustive search by minimizing sampling of low-fitness areas.

Purpose of the Study:

To modify PWPCR for evolving finite state machines.
To develop a DNA-based evolutionary algorithm for solving NP-complete problems.
To address the scalability issues in molecular computation for complex problems.

Main Methods:

Modification of PWPCR to incorporate evolutionary principles.
Utilizing a population of finite state machines for computation.
Development of an in vitro algorithm for evolving approximate solutions.
Application to the Hamiltonian Path problem, an NP-complete problem.

Main Results:

Demonstration of a practical in vitro algorithm for evolving solutions.
Successful application of the modified PWPCR to approximate solutions for the Hamiltonian Path problem.
Potential for DNA-based evolutionary computation to solve complex problems beyond the scope of exhaustive search.

Conclusions:

The modified PWPCR offers a viable DNA-based evolutionary strategy for molecular computation.
This approach provides a potential solution to the scalability challenges in solving NP-complete problems using molecular methods.
The developed algorithm facilitates the evolution of approximate solutions for complex computational tasks.