Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly.
Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly.
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DOI：
10.1038/s41586-023-06890-z
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发表时间：
2024-01
期刊：
影响因子：
64.8
通讯作者：
Murugan, Arvind
中科院分区：
文献类型：
--
作者：
Evans, Constantine Glen;O'Brien, Jackson;Winfree, Erik;Murugan, Arvind
受生物学中最复杂的计算机——大脑的启发，神经网络构成了对计算原理的深刻重构。类似的高维、高度互联的计算架构也出现在活细胞内的信息处理分子系统中，如信号转导级联和遗传调控网络。类似于神经计算的集体模式是否会在其他物理和化学过程中被更广泛地发现，甚至是那些表面上起非信息处理作用的过程？在这里，我们研究了多组分结构自组装过程中的成核，表明高维浓度模式可以用类似于神经网络计算的方式进行区分和分类。具体来说，我们设计了一组917 DNA瓦片，它可以以三种不同的方式自组装，使得竞争成核敏感地依赖于三种结构中高浓度瓦片的共定位程度。该系统在计算机上进行训练，将一组18幅30 × 30像素的灰度图像分为三类。实验上，荧光和原子力显微镜在150小时退火期间和之后的测量确定了所有训练图像的正确分类，而图像变化的测试集则探测了结果的稳健性。虽然与以前的生化神经网络相比速度较慢，但我们的方法紧凑，鲁棒且可扩展。我们的发现表明，普遍存在的物理现象，如成核，当它们发生在高维多组分系统中时，可能具有强大的信息处理能力。对多组分结构自组装过程中成核的研究说明了无处不在的分子现象如何以类似于神经网络计算的方式固有地对高维浓度模式进行分类。
Inspired by biology’s most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. 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 collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? 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 colocalization of high-concentration tiles within the three structures. The system was trained in silico to classify a set of 18 grayscale 30 × 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy measurements during and after a 150 hour anneal established that all trained images were correctly classified, whereas a test set of image variations probed the robustness of the results. Although slow compared to previous biochemical neural networks, our approach is compact, robust and scalable. Our findings suggest that ubiquitous physical phenomena, such as nucleation, may hold powerful information-processing capabilities when they occur within high-dimensional multicomponent systems. Examination of nucleation during self-assembly of multicomponent structures illustrates how ubiquitous molecular phenomena inherently classify high-dimensional patterns of concentrations in a manner similar to neural network computation.
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影响因子：
56.9
作者：
ANDERSON, PW
通讯作者：
ANDERSON, PW
影响因子：
64.8
作者：
通讯作者：
--
DOI：
10.1126/science.1227268
发表时间：
2012-11-30
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Ke Y;Ong LL;Shih WM;Yin P
通讯作者：
Yin P
影响因子：
64.8
作者：
Hartwell, LH;Hopfield, JJ;Murray, AW
通讯作者：
Murray, AW
影响因子：
2.4
作者：
Battaglia, FP;Treves, A
通讯作者：
Treves, A