Supervised Learning in Adaptive DNA Strand Displacement Networks
Supervised Learning in Adaptive DNA Strand Displacement Networks
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DOI：
10.1021/acssynbio.6b00009
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发表时间：
2016-08-01
影响因子：
4.7
通讯作者：
Stefanovic, Darko
中科院分区：
文献类型：
--
作者：
Lakin, Matthew R.;Stefanovic, Darko
开发具有适应性行为的工程生化电路是合成生物学和分子计算的一个关键目标。这种电路可用于生化系统的长期监测和控制，例如，预防疾病或促进人工生命的发展。在本文中，我们提出了一个使用缓冲 DNA 链置换网络开发自适应分子电路的框架，该框架扩展了现有的 DNA 链置换电路架构，以实现行为参数的直接存储和修改。作为概念证明，我们使用这个框架来设计和模拟 DNA 电路，通过随机梯度下降来监督学习一类线性函数。这项工作强调了缓冲 DNA 链置换作为实现自适应分子系统的强大电路架构的潜力。
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.