DNA Reaction System that Acquires Classical Conditioning

Takashi Nakakuki (Corresponding author), Masato Toyonari, Kaori Aso, Keiji Murayama, Hiroyuki Asanuma, Tom F.A. de Greef

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
32 Downloads (Pure)

Abstract

Biochemical reaction networks can exhibit plastic adaptation to alter their functions in response to environmental changes. This capability is derived from the structure and dynamics of the reaction networks and the functionality of the biomolecule. This plastic adaptation in biochemical reaction systems is essentially related to memory and learning capabilities, which have been studied in DNA computing applications for the past decade. However, designing DNA reaction systems with memory and learning capabilities using the dynamic properties of biochemical reactions remains challenging. In this study, we propose a basic DNA reaction system design that acquires classical conditioning, a phenomenon underlying memory and learning, as a typical learning task. Our design is based on a simple mechanism of five DNA strand displacement reactions and two degradative reactions. The proposed DNA circuit can acquire or lose a new function under specific conditions, depending on the input history formed by repetitive stimuli, by exploiting the dynamic properties of biochemical reactions induced by different input timings.

Original languageEnglish
Pages (from-to)521-529
Number of pages9
JournalACS Synthetic Biology
Volume13
Issue number2
Early online date27 Jan 2024
DOIs
Publication statusPublished - 16 Feb 2024

Funding

This work was supported by JSPS KAKENHI Grant Numbers 20KK0331 (T.N.), 20H05971 (T.N.), 20K04549 (T.N.), 23H00506 (T.N.), 20H05970 (K.M.), 20H05968 (K.M.), and JP21H05025 (H.A.).

FundersFunder number
Japan Society for the Promotion of Science20H05970, 20H05971, 23H00506, 20K04549, 20KK0331, JP21H05025, 20H05968

    Keywords

    • classical conditioning
    • DNA strand displacement
    • forgetting
    • learning
    • memory

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