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Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly

Evans, Constantine Glen and O'Brien, Jackson and Winfree, Erik and Murugan, Arvind (2022) Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly. . (Unpublished)

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Inspired by biology's most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Remarkably, analogous high-dimensional, highly-interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks. Might neuromorphic collective modes be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles such as protein synthesis, metabolism, or structural self-assembly? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically, we design a set of 917 DNA tiles that can self-assemble in three alternative ways such that competitive nucleation depends sensitively on the extent of co-localization of high-concentration tiles within the three structures. The system was trained in-silico to classify a set of 18 grayscale 30 x 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy monitoring during and after a 150-hour anneal established that all trained images were correctly classified, while a test set of image variations probed the robustness of the results. While slow compared to prior biochemical neural networks, our approach is surprisingly compact, robust, and scalable. This success suggests that ubiquitous physical phenomena, such as nucleation, may hold powerful information processing capabilities when scaled up as high-dimensional multicomponent systems.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Evans, Constantine Glen0000-0002-7053-1670
O'Brien, Jackson0000-0002-5221-2107
Winfree, Erik0000-0002-5899-7523
Murugan, Arvind0000-0001-5464-917X
Additional Information:We thank Michael Brenner, Aaron Dinner, David Doty, Deborah K. Fygenson, Stanislas Leibler, Richard M. Murray, Lulu Qian, Paul W. K. Rothemund, Chris Thachuk, Grigory Tikhomirov, Damien Woods, and Zorana Zeravcic. Ting Zhu, Thomas Ouldridge, Salvador Buse, Matthew Alexander, Mohini Misra, Anna Lapteva also provided valuable feedback on early drafts. Supported by National Science Foundation grants CCF-1317694 and CCF/FET-2008589, the Evans Foundation for Molecular Medicine, European Research Council grant 772766, Science Foundation Ireland grant 18/ERCS/5746, and the Carver Mead New Adventures Fund. JOB, AM were primarily supported by the University of Chicago Materials Research Science and Engineering Center, which is funded by National Science Foundation under award number DMR-2011854. AM acknowledges support from the Simons Foundation. Author Contributions. CGE, EW, AM conceived the study. CGE and EW designed the molecules. CGE, JOB, EW, AM wrote simulation code, designed the experiments and performed the experiments, analyzed the data and wrote the manuscript. The authors declare that they have no competing financial interests.
Funding AgencyGrant Number
Evans Foundation for Molecular MedicineUNSPECIFIED
European Research Council (ERC)772766
Science Foundation Ireland18/ERCS/5746
Carver Mead New Adventures FundUNSPECIFIED
Simons FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20220809-232339739
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116191
Deposited By: George Porter
Deposited On:12 Aug 2022 00:30
Last Modified:12 Aug 2022 00:30

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