Supervised Learning in Adaptive DNA Strand Displacement Networks

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.

Regulation by competition: a hidden layer of gene regulatory network

“…In addition, competition motif could perform a variety of functions by combining with other network motifs. For example, cooperating with the positive feedback motif, competition can generate the winner-take-all (WTA) behavior [52], which have been applied to design in vitro molecular circuits for supervised learning and pattern classification using DNA strand displacement [53,54]. The unified competition model gives inspirations for transferring knowledge among different molecular scenarios, since similar molecular network topologies may perform similar functions.…”

Quantitative understanding of molecular competition as a hidden layer of gene regulatory network

“…In addition, competition motif could perform a variety of functions by combining with other network motifs. For example, cooperating with the positive feedback motif, competition can generate the winner-take-all (WTA) behavior (48), which have been applied to design in vitro molecular circuits for supervised learning and pattern classification using DNA strand displacement (49,50).…”

A Molecular Circuit Regenerator to Implement Iterative Strand Displacement Operations

“…In conclusion, we have designed and characterized a DNA computing component capable of regeneration and multiple cycles of computation, in which the number of actuated cycles is tightly controlled by the initial amounts of U and S. Successful operation of the device relies on well‐designed sequences that minimize unwanted cross‐talk (“leak”) reactions. Future work combining our device with a regenerative amplifier, such as the buffered amplifier, would convert our device from analog to digital output, furthering its utility in a variety of computational architectures and allowing it to be one of many standard parts used in programming molecular tasks. As adaptive molecular programs are a major goal in DNA computing, our device should prove useful in complex architectures that use iterative computations to achieve enzyme‐free molecular automata.…”