Issue 17, 2023

A survey on molecular-scale learning systems with relevance to DNA computing

Abstract

DNA computing has emerged as a promising alternative to achieve programmable behaviors in chemistry by repurposing the nucleic acid molecules into chemical hardware upon which synthetic chemical programs can be executed. These chemical programs are capable of simulating diverse behaviors, including boolean logic computation, oscillations, and nanorobotics. Chemical environments such as the cell are marked by uncertainty and are prone to random fluctuations. For this reason, potential DNA-based molecular devices that aim to be deployed into such environments should be capable of adapting to the stochasticity inherent in them. In keeping with this goal, a new subfield has emerged within DNA computing, focusing on developing approaches that embed learning and inference into chemical reaction systems. If realized in biochemical contexts, such molecular machines can engender novel applications in fields such as biotechnology, synthetic biology, and medicine. Therefore, it would be beneficial to review how different ideas were conceived, how the progress has been so far, and what the emerging ideas are in this nascent field of ‘molecular-scale learning’.

Graphical abstract: A survey on molecular-scale learning systems with relevance to DNA computing

Article information

Article type
Minireview
Submitted
06 Aza. 2022
Accepted
02 Api. 2023
First published
10 Api. 2023

Nanoscale, 2023,15, 7676-7694

Author version available

A survey on molecular-scale learning systems with relevance to DNA computing

R. T. Nagipogu, D. Fu and J. H. Reif, Nanoscale, 2023, 15, 7676 DOI: 10.1039/D2NR06202J

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