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 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.
Department of Chemical & Biological Engineering, ‡Department of Computer Science, and §Center for Biomedical Engineering, University of New Mexico , Albuquerque, New Mexico 87131, United States.
Matthew R
MR
Lakin
Department of Chemical & Biological Engineering, ‡Department of Computer Science, and §Center for Biomedical Engineering, University of New Mexico , Albuquerque, New Mexico 87131, United States.
Darko
D
Stefanovic
D039301:Computers, Molecular; D004247:DNA / Q000235:genetics*; D000069553:Supervised Machine Learning; D058615:Synthetic Biology / Q000379:methods
D004247:DNA
DNA strand displacement; adaptive algorithms; gradient descent; machine learning; molecular computing