Molecular computations with competitive neural networks that exploit linear and nonlinear kinetics

5 pages•Published: June 22, 2012

Anthony J. Genot, Teruo Fujii and Yannick Rondelez

Abstract

We show how to exploit enzymatic saturation -an ubiquitous nonlinear effects in biochemistry- in order to process information in molecular networks. The networks rely on the linearity of DNA strand displacement and the nonlinearity of enzymatic replication. We propose a pattern-recognition network that is compact and should be robust to leakage.

Keyphrases: dna nanotechnology, enzymes, neural networks, nonlinear kinetics

In: Andrei Voronkov (editor). Turing-100. The Alan Turing Centenary, vol 10, pages 113-117.

Links:	https://easychair.org/publications/paper/SW
	https://doi.org/10.29007/rfzv

BibTeX entry

@inproceedings{Turing-100:Molecular_computations_with_competitive,
  author    = {Anthony J. Genot and Teruo Fujii and Yannick Rondelez},
  title     = {Molecular computations with competitive neural networks that exploit linear and nonlinear kinetics},
  booktitle = {Turing-100. The Alan Turing Centenary},
  editor    = {Andrei Voronkov},
  series    = {EPiC Series in Computing},
  volume    = {10},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/SW},
  doi       = {10.29007/rfzv},
  pages     = {113-117},
  year      = {2012}}

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