Chemical reaction networks for computing polynomials

Sayed Ahmad Salehi, Keshab K Parhi, Marc Riedel

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Chemical reaction networks (CRNs) provide a fundamental model in the study of molecular systems. Widely used as formalism for the analysis of chemical and biochemical systems, CRNs have received renewed attention as a model for molecular computation. This paper demonstrates that, with a new encoding, CRNs can compute any set of polynomial functions subject only to the limitation that these functions must map the unit interval to itself. These polynomials can be expressed as linear combinations of Bernstein basis polynomials with positive coefficients less than or equal to 1. In the proposed encoding approach, each variable is represented using two molecular types: A type-0 and a type-1. The value is the ratio of the concentration of type-1 molecules to the sum of the concentrations of type-0 and type-1 molecules. The proposed encoding naturally exploits the expansion of a power-form polynomial into a Bernstein polynomial. Molecular encoders for converting any input in a standard representation to the fractional representation as well as decoders for converting the computed output from the fractional to a standard representation are presented. The method is illustrated first for generic CRNs; then chemical reactions designed for an example are mapped to DNA strand-displacement reactions.

Original languageEnglish (US)
Pages (from-to)76-83
Number of pages8
JournalACS Synthetic Biology
Volume6
Issue number1
DOIs
StatePublished - Jan 1 2017

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Chemical reactions
Molecular Computers
Polynomials
DNA
Molecules

Keywords

  • DNA strand-displacement reaction
  • Mass-Action kinetics
  • Molecular computing
  • Polynomials

Cite this

Chemical reaction networks for computing polynomials. / Salehi, Sayed Ahmad; Parhi, Keshab K; Riedel, Marc.

In: ACS Synthetic Biology, Vol. 6, No. 1, 01.01.2017, p. 76-83.

Research output: Contribution to journalArticle

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