ArticleScientific reports2018
Computing Mathematical Functions using DNA via Fractional Coding.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Processing DNA Storage through Programmable Assembly in a Droplet-Based Fluidics System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- DNA strand displacement based computational systems and their applications.Frontiers in genetics · 2023Review
- Article
- Neural network execution using nicked DNA and microfluidics.PloS one · 2023Article
- Chemical Reaction Networks' Programming for Solving Equations.Current issues in molecular biology · 2022Article
- DNA computing for gastric cancer analysis and functional classification.Frontiers in genetics · 2022Article
- Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning.Molecules (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper discusses the implementation of mathematical functions such as exponentials, trigonometric functions, the sigmoid function and the perceptron function with molecular reactions in general, and DNA strand displacement reactions in particular. The molecular constructs for these functions are predicated on a novel representation for input and output values: a fractional encoding, in which values are represented by the relative concentrations of two molecular types, denoted as type-1 and type-0. This representation is inspired by a technique from digital electronic design, termed stochastic logic, in which values are represented by the probability of 1's in a stream of randomly generated 0's and 1's. Research in the electronic realm has shown that a variety of complex functions can be computed with remarkably simple circuitry with this stochastic approach. This paper demonstrates how stochastic electronic designs can be translated to molecular circuits. It presents molecular implementations of mathematical functions that are considerably more complex than any shown to date. All designs are validated using mass-action simulations of the chemical kinetics of DNA strand displacement reactions.
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Registered trials
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