Programming gel automata shapes using DNA instructions

Ruohong Shi, Kuan-Lin Chen, Joshua Fern, Siming Deng, Yixin Liu, Dominic Scalise, Qi Huang, Noah J. Cowan, David H. Gracias () and Rebecca Schulman ()
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Ruohong Shi: Johns Hopkins University
Kuan-Lin Chen: Johns Hopkins University
Joshua Fern: Johns Hopkins University
Siming Deng: Johns Hopkins University
Yixin Liu: Johns Hopkins University
Dominic Scalise: Johns Hopkins University
Qi Huang: Johns Hopkins University
Noah J. Cowan: Johns Hopkins University
David H. Gracias: Johns Hopkins University
Rebecca Schulman: Johns Hopkins University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-51198-9

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