SHF:Medium:Collaborative Research:Scaling Up Programmable and Algorithmic DNA Self-Assembly

SHF：中：合作研究：扩大可编程和算法 DNA 自组装

• 批准号: 1162589
• 负责人: Erik Winfree
• 金额: $ 40万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1162589&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Scaling

The dominant manufacturing paradigm for human technology has been top-down construction of objects, in the sense that a large entity manipulates smaller entities to put them together into a functional device. In contrast, for billions of years biological organisms have constructed objects using a bottom-up technology, in the sense that the pieces self-assemble or grow without outside assistance. For example, to make a complex molecular machine, enzymes within the cell might synthesize a number of proteins that then diffuse randomly until they bump into each other and click into place; while on a larger scale, a single cell might grow into an elephant. The bottom-up manufacturing paradigm has advantages that top-down methods are unlikely ever to achieve, such as the ability to create meter-scale objects from components with atomic-scale (nanometer) resolution and chemical functionality but it requires a level of exquisite control over molecular structure and function that human science and technology has not yet attained. We believe that the primary missing ingredient is information science and technology: information must be encoded within synthetic molecules to control their behavior and to create programmable molecular systems. In this research, the aim is to push the frontiers of information-based molecular self-assembly using DNA nanotechnology. The past fifteen years have seen the development of an abstract theory of algorithmic self-assembly (initiated by Winfree) that merges the mathematical theory of geometrical tiling, the statistical mechanical and kinetic theories of crystal growth, and the algorithmic theory of Turing machine computation. This theory shows how, in principle, synthetic DNA molecules called ?tiles? can be designed to carry information that directs their assembly into complex and sophisticated shapes and patterns. Just as a small program can produce a large and intricate output, a small tile set can result in the self-assembly of a large and intricate object the tile set is a program for controlling the molecular self-assembly process. Laboratory experiments in the past fifteen years have demonstrated the foundations of this theory using DNA tile sets on the order of two dozen tile types, i.e. very small molecular programs.In the past year, a new molecular motif for DNA tiles (developed by Yin) has been used to self-assemble molecular structures using up to 1000 distinct tile types that each has a unique target position within the structure, like a self-assembled molecular-scale jigsaw puzzle. This is the simplest type of molecular program. A major goal of the proposed work is demonstrating that the new ?single strand tile? motif can be used to create significantly more complicated self-assembly programs than have been seen to date by reusing distinct tile types in many locations and in an algorithmic fashion, much like living systems that reuse the same molecules in many different ways. Sophisticated algorithmic tile reuse of two dozen to perhaps 1000 or more distinct components vastly expands the capabilities of self-assembly programs. To achieve this, proposed work will (a) improve techniques for an important subroutine for controlling molecular growth, a binary counting process that terminates after growing a pre-specified distance; (b) develop methods and molecular structures for nucleating the growth of single-strand tiles with pre-specified information that serves as ?input? to the molecular program; (c) demonstrate algorithmic growth of single-strand tiles that perform Turing machine and/or cellular automaton computations; (d) investigate proofreading techniques for reducing the rate of errors during self-assembly; and (e) create software tools that facilitate the design and analysis of these complex molecular systems.

人类技术的主导制造范式是自上而下的物体构造，即大型实体操纵较小的实体将它们组合在一起形成功能设备。相比之下，数十亿年来，生物有机体使用自下而上的技术构建物体，即这些部件在没有外界帮助的情况下自我组装或生长。 例如，为了制造复杂的分子机器，细胞内的酶可能会合成许多蛋白质，然后这些蛋白质随机扩散，直到它们相互碰撞并卡入到位；而在更大的范围内，单个细胞可能会长成一头大象。 自下而上的制造范式具有自上而下的方法不可能实现的优点，例如能够从具有原子级（纳米）分辨率和化学功能的组件创建米级物体，但它需要一定程度的精确控制人类科学技术尚未达到的分子结构和功能。 我们认为，主要缺失的成分是信息科学和技术：信息必须在合成分子内编码，以控制其行为并创建可编程分子系统。 本研究的目的是利用 DNA 纳米技术推动基于信息的分子自组装的前沿。 在过去的十五年里，算法自组装的抽象理论（由 Winfree 发起）得到了发展，该理论融合了几何平铺的数学理论、晶体生长的统计力学和动力学理论以及图灵机计算的算法理论。 该理论表明，原则上，合成 DNA 分子（称为“tiles”）是如何进行的？可以被设计为携带信息，指导它们组装成复杂而精致的形状和图案。 正如一个小程序可以产生一个大而复杂的输出一样，一个小的瓦片集可以导致一个大而复杂的物体的自组装，瓦片集是一个用于控制分子自组装过程的程序。 过去十五年的实验室实验已经证明了这一理论的基础，使用大约两打瓦片类型的DNA瓦片集，即非常小的分子程序。在过去的一年里，DNA瓦片的一个新的分子图案（由Yin开发）已被用于使用多达 1000 种不同的图块类型来自组装分子结构，每种图块类型在结构内都有一个独特的目标位置，就像自组装分子规模的拼图游戏一样。这是最简单的分子程序类型。 拟议工作的一个主要目标是证明新的“单股瓦片”是可行的。通过在许多位置以算法方式重复使用不同的图块类型，主题可用于创建比迄今为止所见的更复杂的自组装程序，就像以多种不同方式重复使用相同分子的生命系统一样。复杂的算法可重复使用 20 个到 1000 个或更多不同的组件，极大地扩展了自组装程序的功能。 为了实现这一目标，拟议的工作将（a）改进用于控制分子生长的重要子程序的技术，这是一个在生长预先指定的距离后终止的二进制计数过程； (b) 开发方法和分子结构，利用预先指定的信息作为“输入”，使单链瓦片的生长成核。分子程序； (c) 演示执行图灵机和/或元胞自动机计算的单链瓦片的算法增长； (d) 研究校对技术以减少自组装过程中的错误率； (e) 创建软件工具以促进这些复杂分子系统的设计和分析。