Workshop on DNA Computing by Self-Assembly

DNA自组装计算研讨会

• 批准号: 1428340
• 负责人: Matthew Patitz
• 金额: $ 1.2万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-15 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1428340&HistoricalAwards=false
• 关键词: Workshop; DNA; Computing; Self; Assembly

Algorithmic self-assembly uses computational thinking to design systems in which large numbers of relatively simple components autonomously combine to form complex structures or perform computations. Applying tools from computational theory to this domain has resulted in the development of a rich set of theoretical models which have provided insight into many fundamental properties, abilities, and limitations of self-assembling systems. This has also helped to form a road map for experimental work in developing artificial self-assembling systems in the laboratory. During this workshop, several of those theoretical models will be introduced. The workshop presenter will then survey a wide variety of results in which notions of simulation between systems within, and even across, models have been used to compare and contrast the powers of those models and the systems they contain. The discussion will focus on the application of specific notions of simulation, including intrinsic universality, and the insights they lend into various properties of self-assembling systems. Intellectual MeritThe field of algorithmic self-assembly requires expertise in a broad range of areas, from computational theory to biochemistry, to name a few. This workshop will be designed to be accessible to those with no previous experience in self-assembly, and will start with introductions to basic definitions and models, but will finish with discussions of recent technical results which will also be of interest to experts in the field. Broader ImpactThe main goal of this workshop is to introduce a larger population of researchers, especially students, to the area of algorithmic self-assembly. Workshop organizers will use the funds provided to cover the travel costs of students attending the workshop co-located with Unconventional Computation and Natural Computation 2014 conference. The organizers will focus especially on funding students from underrepresented groups and with a variety of backgrounds to further broaden the exposure of algorithmic self-assembly. Further development of this field has the potential to lead to technological innovations which impact technologies from materials science, to biomedicine, to advanced computing architectures.

算法自组装使用计算思维来设计系统，其中大量相对简单的组件自主组合以形成复杂的结构或执行计算。 将计算理论的工具应用到该领域已经开发出一套丰富的理论模型，这些模型提供了对自组装系统的许多基本属性、能力和局限性的深入了解。 这也有助于为实验室开发人工自组装系统的实验工作制定路线图。 在本次研讨会期间，将介绍其中的几个理论模型。 然后，研讨会主持人将调查各种各样的结果，其中模型内部甚至模型之间的系统之间的模拟概念被用来比较和对比这些模型及其包含的系统的能力。 讨论将集中于特定模拟概念的应用，包括内在的普遍性，以及它们对自组装系统的各种属性的见解。 智力优势算法自组装领域需要广泛领域的专业知识，从计算理论到生物化学等等。 本次研讨会旨在让那些没有自组装经验的人也能参加，将从介绍基本定义和模型开始，最后讨论该领域专家也感兴趣的最新技术成果。 更广泛的影响本次研讨会的主要目标是向更多的研究人员（尤其是学生）介绍算法自组装领域。研讨会组织者将使用提供的资金来支付参加 2014 年非常规计算和自然计算会议同一地点研讨会的学生的旅费。 组织者将特别关注资助来自代表性不足群体和具有各种背景的学生，以进一步扩大算法自组装的曝光度。 该领域的进一步发展有可能带来技术创新，影响从材料科学到生物医学再到先进计算架构的技术。