BIC: Large-Scale DNA Associative Memories

智力优势：在DNA记忆中，信息被编码到DNA序列中，并通过DNA寡核苷酸之间的模板匹配杂交反应来检索。DNA寡核苷酸之间的模板匹配、杂交反应在生物技术和医学中具有重要意义（DNA微阵列）和纳米技术（DNA指导的纳米结构自组装）。该项目将导致更好地理解如何将信息编码到杂交DNA寡核苷酸的集合中，以及更好的表征大集合的模型和方法，简单的协议可以用来操纵存储器的内容，实现信息处理.在统计DNA存储器中，DNA分子的集合用试管协议训练以再现特定的输入/输出映射，类似于人工神经网络。信息不是一对一的映射，而是概率性地编码到DNA序列及其杂交相互作用中。这两个架构将进行实验测试和模拟计算和物理，最后模拟，以更好地了解如何通过DNA模板匹配杂交反应存储和操纵信息。更广泛的影响：研究生将参与该项目，因此，将在跨学科的知识和方法，使用生物系统的计算研究所需的培训。他们将得到指导，以制作研究出版物，参加专业会议和撰写研究生论文。将编制与研究有关的课程单元，以便纳入生物和计算机课程。这些模块将提供主题的指导和演示，展示学科之间的相互作用，以及实验，理论和模拟方法。我们的目标是培养学生更好地为生物学和计算的新兴综合领域的职业和挑战做好准备。项目成果将通过一个专门的网站以及相关学科的会议和期刊进行传播。

Intellectual Merit:In a DNA memory, information is encoded into DNA sequences and retrieved through template-matching hybridization reactions among DNA oligonucleotides. The template matching, hybridization reaction between DNA oligonucleotides is important in biotechnology and medicine (DNA microarrays),and nanotechnology (DNA directed self-assembly of nanostructures).The project should lead to a better understanding of how information can be encoded into an ensemble of hybridizing DNA oligonucleotides, and to better models andways of characterizing large ensembles.Simple protocols can be used to manipulate the contents of the memory to achieve information processing. In the statistical DNA memory, an ensemble of DNA molecules are trained with test tube protocols to reproduce particular input/output mappings, similar to artificial neural networks. Rather than a one to one mapping, information is encoded probabilistically into the DNA sequences and their hybridization interactions. These two architectures will be experimentally tested and modeled computationally and physically, and finally simulated to better understand how information is stored and manipulated through DNA template-matching hybridization reactions.Broader Impacts:Graduate students will participate in the project, and thus, will be trained in the interdisciplinary knowledge and methods required of computational research using biological systems. They will be mentored to produce research publications, attend professional conferences and write graduate theses. Curricular modules associated with the research will be developed for incorporation into both biological and computer curricula. These modules will provide instruction and demonstrations in the topic that show the interplay between the disciplines, as well as experimental, theoretical, and simulation methods. The goal is to produce students that are better prepared for careers and challenges in the emerging synthesis of biology and computation. The project results will be disseminated through a dedicated web site, and through conference and journals in the relevant disciplines.