California Institute of Technology, Pasadena, CA, USA.
Inria, Paris, France.
Nature. 2019 Mar;567(7748):366-372. doi: 10.1038/s41586-019-1014-9. Epub 2019 Mar 20.
Molecular biology provides an inspiring proof-of-principle that chemical systems can store and process information to direct molecular activities such as the fabrication of complex structures from molecular components. To develop information-based chemistry as a technology for programming matter to function in ways not seen in biological systems, it is necessary to understand how molecular interactions can encode and execute algorithms. The self-assembly of relatively simple units into complex products is particularly well suited for such investigations. Theory that combines mathematical tiling and statistical-mechanical models of molecular crystallization has shown that algorithmic behaviour can be embedded within molecular self-assembly processes, and this has been experimentally demonstrated using DNA nanotechnology with up to 22 tile types. However, many information technologies exhibit a complexity threshold-such as the minimum transistor count needed for a general-purpose computer-beyond which the power of a reprogrammable system increases qualitatively, and it has been unclear whether the biophysics of DNA self-assembly allows that threshold to be exceeded. Here we report the design and experimental validation of a DNA tile set that contains 355 single-stranded tiles and can, through simple tile selection, be reprogrammed to implement a wide variety of 6-bit algorithms. We use this set to construct 21 circuits that execute algorithms including copying, sorting, recognizing palindromes and multiples of 3, random walking, obtaining an unbiased choice from a biased random source, electing a leader, simulating cellular automata, generating deterministic and randomized patterns, and counting to 63, with an overall per-tile error rate of less than 1 in 3,000. These findings suggest that molecular self-assembly could be a reliable algorithmic component within programmable chemical systems. The development of molecular machines that are reprogrammable-at a high level of abstraction and thus without requiring knowledge of the underlying physics-will establish a creative space in which molecular programmers can flourish.
分子生物学提供了一个令人鼓舞的原理证明，即化学系统可以存储和处理信息，以指导分子活动，例如从分子组件制造复杂结构。为了将基于信息的化学发展成为一种对物质进行编程以实现生物系统中未见功能的技术，有必要了解分子相互作用如何编码和执行算法。相对简单的单元自组装成复杂产物特别适合于此类研究。将数学平铺理论和分子结晶统计力学模型相结合的理论表明，算法行为可以嵌入分子自组装过程中，并且这已经使用多达 22 种类型的 DNA 纳米技术进行了实验证明。然而，许多信息技术都表现出一种复杂性阈值，例如通用计算机所需的最小晶体管数，超过该阈值，可重新编程系统的功能会发生质的变化，并且尚不清楚 DNA 自组装的生物物理学是否允许超过该阈值。在这里，我们报告了一种 DNA 平铺集的设计和实验验证，该平铺集包含 355 个单链平铺，可以通过简单的平铺选择进行重新编程，以实现各种 6 位算法。我们使用该集构建了 21 个执行包括复制、排序、识别回文和 3 的倍数、随机游走、从有偏随机源中进行无偏选择、选举领导者、模拟元胞自动机、生成确定性和随机化模式以及计数至 63 在内的算法的电路，总体每个平铺的错误率低于 1/3000。这些发现表明，分子自组装可能是可编程化学系统中的可靠算法组件。开发可重新编程的分子机器-在高度抽象的层面上，因此无需了解基础物理-将建立一个创造性的空间，使分子程序员可以蓬勃发展。
