Supervised Learning in Adaptive DNA Strand Displacement Networks.
ACS Synth Biol
; 5(8): 885-97, 2016 08 19.
Article
em En
| MEDLINE
| ID: mdl-27111037
ABSTRACT
The development of engineered biochemical circuits that exhibit adaptive behavior is a key goal of synthetic biology and molecular computing. Such circuits could be used for long-term monitoring and control of biochemical systems, for instance, to prevent disease or to enable the development of artificial life. In this article, we present a framework for developing adaptive molecular circuits using buffered DNA strand displacement networks, which extend existing DNA strand displacement circuit architectures to enable straightforward storage and modification of behavioral parameters. As a proof of concept, we use this framework to design and simulate a DNA circuit for supervised learning of a class of linear functions by stochastic gradient descent. This work highlights the potential of buffered DNA strand displacement as a powerful circuit architecture for implementing adaptive molecular systems.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
DNA
Idioma:
En
Revista:
ACS Synth Biol
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
Estados Unidos