Programming gel automata shapes using DNA instructions

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.

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Publication Details

Journal
Nature Communications
Published
2024-09-05
DOI
https://doi.org/10.1038/s41467-024-51198-9
Citations
8
Primary Topic
DNA and Biological Computing
Type
article
Field-Weighted Citation Impact
1.87

Funders

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article

Programming gel automata shapes using DNA instructions

Joshua Fern, Siming Deng, Ruohong Shi, David H. Gracias et al.
8 citations
Nature Communications
DNA and Biological Computing
1.87
article

Programming gel automata shapes using DNA instructions

Joshua Fern, Siming Deng, Ruohong Shi, David H. Gracias, Kuan-Lin Chen, Dominic Scalise, Qi Huang, Rebecca Schulman, Noah J. Cowan, Yixin Liu
article en
8 citations

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.

Nature CommunicationsVol. 15(1)
Johns Hopkins University (US), Johns Hopkins Medicine (US), Sidney Kimmel Comprehensive Cancer Center (US), University of Baltimore (US), Washington State University (US)
National Science Foundation, Alfred P. Sloan Foundation, Army Research Office
Openalex Percentile: Top 14%
DNA and Biological Computing
1.87
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