Supervised Learning in Adaptive DNA Strand Displacement Networks

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• 作者：Matthew R. Lakin ; Darko Stefanovic
• 刊名：ACS Synthetic Biology
• 出版年：2016
• 出版时间：August 19, 2016
• 年：2016
• 卷：5
• 期：8
• 页码：885-897
• 全文大小：593K
• 年卷期：0
• ISSN：2161-5063

文摘

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.