Supervised Learning in Adaptive DNA Strand Displacement Networks

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 arti...

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Bibliographic Details
Published in:ACS synthetic biology 2016-08, Vol.5 (8), p.885-897
Main Authors: Lakin, Matthew R, Stefanovic, Darko
Format: Article
Language:English
Subjects:
Computers, Molecular
DNA - genetics
Supervised Machine Learning
Synthetic Biology - methods
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Summary: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.
ISSN:2161-5063
2161-5063
DOI:10.1021/acssynbio.6b00009