NANO: Collaborative Research: Algorithmic error-correction in biologically inspired self-assembly and computation

NANO：协作研究：受生物启发的自组装和计算中的算法纠错

• 批准号: 0524783
• 负责人: Ashish Goel
• 金额: $ 22.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0524783&HistoricalAwards=false
• 关键词: NANO; Collaborative; Research; Algorithmic; error

Self-assembly is the process by which small ``components'' spontaneously form intricate aggregate structures. DNA self-assembly is a key tool for nano-technology, nano-robotics, and molecular computation. More generally, biological organisms are self-organized chemical systems that carry out algorithms encoded in the genetic material, DNA. Biology thus provides clear proof that autonomous chemical systems can be programmed, and that they can function reliably on a grand scale -- biological organisms can be composed of as many as 1024 molecular components!Recent experimental advances in the synthesis of artificial molecular systems have demonstrated that it is possible to program molecular self-assembly to carry out rudimentary logic. These experiments also suggested that the occurrence of errors is a major obstacle to scaling up DNA self-assembly and biologically inspired computation. This project will devise algorithmic tools and analysis techniques for error-correction and error-suppression in biologically inspired self-assembling and computational systems. It is our hope that our research will facilitate sophisticated tasks such as counting, growing molecular assemblies of pre-specified sizes (no larger, no smaller), and pattern recognition of complex chemical signals using inherently error-prone biomolecular operations at the nano-scale.In order to design and analyze our error-correction mechanisms, we will use high level models which are both sufficiently realistic to be useful and sufficiently abstract to be amenable to analysis. The basic elements of our models will be DNA tiles, transcriptional circuits, and DNA hybridization catalysts. Thus, our research will target assembly of and computation with large molecules such as long chains of DNA rather than smaller molecules such as proteins and amino acids.We will also develop course material on the basis of our research which will be taught at Caltech and at Stanford.

自组装是小的“组件”自发形成复杂的聚集结构的过程。DNA自组装是纳米技术、纳米机器人和分子计算的关键工具。更一般地说，生物有机体是自组织的化学系统，执行遗传物质DNA中编码的算法。因此，生物学提供了明确的证据，证明自主的化学系统可以被编程，并且它们可以在大规模上可靠地发挥作用-生物有机体可以由多达1024个分子组成！人工分子系统合成的最新实验进展表明，通过对分子自组装进行编程来实现基本逻辑是可能的。 这些实验还表明，错误的发生是扩大DNA自组装和生物启发计算的主要障碍。这个项目将设计算法工具和分析技术，用于生物启发的自组装和计算系统中的纠错和抑制错误。我们希望，我们的研究将有助于复杂的任务，如计数，生长预先指定大小的分子组装（不大也不小），以及在纳米尺度上使用固有的易错生物分子操作对复杂化学信号进行模式识别。为了设计和分析我们的纠错机制，我们将使用高层次的模型，这些模型既足够真实以有用，又足够抽象以便于分析。我们的模型的基本元素将是DNA瓦片，转录电路和DNA杂交催化剂。因此，我们的研究将针对大分子（如DNA长链）的组装和计算，而不是小分子（如蛋白质和氨基酸）。我们还将根据我们的研究开发课程材料，这些材料将在加州理工学院和斯坦福大学教授。