Evidence map›Paper›PMID 39237499›Full record

ArticleNature communications2024

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, Rebecca Schulman

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ruohong ShiDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0001-9163-5607
Kuan-Lin ChenDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Joshua FernDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Siming DengDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-3111-8629
Yixin LiuDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Dominic ScaliseDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Qi HuangDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Noah J CowanDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-2502-3770
David H GraciasDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA. dgracias@jhu.edu.ORCID 0000-0003-2735-4725
Rebecca SchulmanDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA. rschulm3@jhu.edu.ORCID 0000-0003-4555-3162

Funding

Alfred P. Sloan Foundation 138412National Science Foundation (NSF) EAGER 2036803National Science Foundation (NSF) EFMA 1830893United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO) W911NF2010057
6 · The paper itself

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.

Indexed as

Acrylic ResinsDNAGelsAlgorithmsBase SequenceChemical PhenomenaNeural Networks, ComputerPolyacrylamidesAcrylic ResinsDNAGelsPolyacrylamides

Identifiers

PMID39237499
PMCPMC11377784

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.