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。模板匹配、DNA寡核苷酸之间的杂交反应在生物技术和医学（DNA微阵列）以及纳米技术（DNA引导的纳米结构自组装）中具有重要意义。该项目将有助于更好地理解如何将信息编码到杂交DNA寡核苷酸的集合中，以及更好的模型和表征大集合的方法。可以使用简单的协议来操作存储器的内容，以实现信息处理。在统计DNA记忆中，一组DNA分子用试管协议进行训练，以重现特定的输入/输出映射，类似于人工神经网络。而不是一对一的映射，信息被概率编码到DNA序列和它们的杂交相互作用中。这两种架构将进行实验测试，计算和物理建模，最后进行模拟，以更好地了解信息是如何通过DNA模板匹配杂交反应存储和操作的。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上进行训练，这些知识和方法是使用生物系统进行计算研究所必需的。他们将接受指导，制作研究出版物，参加专业会议和撰写研究生论文。将开发与研究相关的课程模块，以便纳入生物和计算机课程。这些模块将提供指示和演示的主题，显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新兴的生物学和计算综合领域的职业和挑战做好准备。项目结果将通过一个专门的网站以及通过有关学科的会议和期刊传播。

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.