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Programming gel automata shapes using DNA instructions

Author

Listed:
  • Ruohong Shi

    (Johns Hopkins University)

  • Kuan-Lin Chen

    (Johns Hopkins University)

  • Joshua Fern

    (Johns Hopkins University)

  • Siming Deng

    (Johns Hopkins University
    Johns Hopkins University)

  • Yixin Liu

    (Johns Hopkins University)

  • Dominic Scalise

    (Johns Hopkins University
    Washington State University)

  • Qi Huang

    (Johns Hopkins University)

  • Noah J. Cowan

    (Johns Hopkins University
    Johns Hopkins University)

  • David H. Gracias

    (Johns Hopkins University
    Johns Hopkins University
    Johns Hopkins School of Medicine
    Johns Hopkins School of Medicine)

  • Rebecca Schulman

    (Johns Hopkins University
    Johns Hopkins University
    Johns Hopkins University
    Johns Hopkins University)

Abstract

The ability to transform matter between numerous physical states or shapes without wires or external devices is a major challenge for robotics and materials design. Organisms can transform their shapes using biomolecules carrying specific information and localize at sites where transitions occur. Here, we introduce gel automata, which likewise can transform between a large number of prescribed shapes in response to a combinatorial library of biomolecular instructions. Gel automata are centimeter-scale materials consisting of multiple micro-segments. A library of DNA activator sequences can each reversibly grow or shrink different micro-segments by polymerizing or depolymerizing within them. We develop DNA activator designs that maximize the extent of growth and shrinking, and a photolithography process for precisely fabricating gel automata with elaborate segmentation patterns. Guided by simulations of shape change and neural networks that evaluate gel automata designs, we create gel automata that reversibly transform between multiple, wholly distinct shapes: four different letters and every even or every odd numeral. The sequential and repeated metamorphosis of gel automata demonstrates how soft materials and robots can be digitally programmed and reprogrammed with information-bearing chemical signals.

Suggested Citation

  • Ruohong Shi & Kuan-Lin Chen & Joshua Fern & Siming Deng & Yixin Liu & Dominic Scalise & Qi Huang & Noah J. Cowan & David H. Gracias & Rebecca Schulman, 2024. "Programming gel automata shapes using DNA instructions," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51198-9
    DOI: 10.1038/s41467-024-51198-9
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    References listed on IDEAS

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