SemiSynBio: Nucleic Acid Memory

SemiSynBio:核酸记忆

基本信息

  • 批准号:
    1807809
  • 负责人:
  • 金额:
    $ 112.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-07-15 至 2023-06-30
  • 项目状态:
    已结题

项目摘要

The rapid proliferation of cloud computing and the emergence of big data for massive scientific, financial, governmental, and genetic records is creating an information storage crisis. These data, once generated, cascade through the information storage lifecycle -- from primary storage media in the form of hard disks and solid-state drives to archival media such as magnetic tape. While innovations in information density, stability, and energy consumption routinely occur, existing memory materials are approaching their physical and economic finish lines. As imagined by the Semiconductor Synthetic Biology (SemiSynBio) Roadmap, DNA-based massive information storage is a brand new start for memory manufacturing. As a result, the research team proposes to pioneer a cold storage paradigm by designing, building, and testing two accessible, editable, and non-volatile memory technologies made from DNA. Inspired by DNA circuits and made possible by state-of-the-art optical physics, the team will: (1) biologically synthesize DNA molecules, (2) engineer substrates made from said molecules, (3) write digital information onto the substrates using additional DNA molecules, (4) minimize encoding and decoding errors using computer science algorithms, and (5) read, as well as edit digital information onto the substrates using reversible DNA binding. In full support of this interdisciplinary project, the research team includes expertise in: DNA nanotechnology, nanoscale characterization, optical physics, biologically-inspired algorithms, and synthetic biology. Modeled after the faculty collaboration, a new cadre of students will work and study at the confluence of the biological, computational, and engineering sciences in anticipation of the emerging field called Nucleic Acid Memory (NAM). As active participants in and co-owners of a Vertically Integrated Project called NAM, undergraduate and graduate students will enroll into a multi-year and multi-disciplinary research team that provides ongoing course and teaching credit.The focal points of this proposal are two storage medium prototypes, digital Nucleic Acid (dNAM) and sequence Nucleic Acid Memory (seqNAM). Each offer a novel approach to coding information using DNA, and both use super-resolution microscopy to read information. In dNAM, information is encoded into defined spatial arrangements of DNA sequences on top of addressable DNA origami nanostructures, called NAM storage nodes. DNA origami provides a convenient pathway and a proven approach to high-yield and rapid prototyping of NAM node structures. Staple strands will be extended from the NAM node structures with a unique sequence for site-specific attachment of NAM data strands. When bound, data strands serve as docking sites for complementary data imager strands, which are employed in a DNA-based form of super-resolution microscopy (SRM) called DNA-PAINT. DNA PAINT is a stochastic super-resolution imaging technique that uses repetitive, transient binding of fluorescently labeled data imager strands to circumvent the diffraction limit of light. Thus, data imager strands act as the read head and reveal the state of each site of the NAM storage node with better than 7 nm resolution. Binary states at each data cell can be defined by the presence (1) or absence (0) of the NAM data strand, as determined by SRM. Increasing site-specific bit-density from 1 to 3 bits can be simply achieved by multiple orthogonal sequences. Editing of data strands is performed by either adding a required data strand to a vacant data cell or by removing an existing data strand via toehold-mediated strand displacement. Built upon a similar storage node platform, seqNAM employs two data cells to arrange data strands into ordered arrays. In seqNAM, information is encoded within portions of the data strands that remain single stranded. The sequences of the data strands are read using a multi-color super-resolution sequencing (SRS) process that uses a library of locked nucleic acid imager strands. Editing is performed by removing the target data strands with complementary sequences using toehold-mediated strand invasion and then adding the replacement data strands. seqNAM exceeds dNAM by storing information within DNA sequences at a potentially higher density. In addition, it creates a new enzyme-free sequencing platform.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
云计算的迅速普及和大量科学、金融、政府和基因记录的大数据的出现正在造成信息存储危机。这些数据一旦生成,就会在信息存储生命周期中级联--从硬盘和固态驱动器形式的主存储介质到磁带等归档介质。虽然信息密度、稳定性和能耗方面的创新经常发生,但现有的存储材料正在接近其物理和经济的终点线。正如半导体合成生物学(SemiSynBio)路线图所设想的那样,基于DNA的海量信息存储是存储器制造的全新开始。因此,研究小组建议通过设计、构建和测试两种由DNA制成的可访问、可编辑和非易失性存储器技术来开创冷存储模式。受DNA电路的启发,并通过最先进的光学物理学实现,该团队将:(1)生物合成DNA分子,(2)设计由所述分子制成的基底,(3)使用附加的DNA分子将数字信息写入基底上,(4)使用计算机科学算法使编码和解码错误最小化,以及(5)读取,以及使用可逆DNA结合将数字信息编辑到基底上。为了全力支持这个跨学科项目,研究团队包括以下方面的专业知识:DNA纳米技术,纳米级表征,光学物理,生物启发算法和合成生物学。在教师合作之后,一个新的学生骨干将在生物,计算和工程科学的融合中工作和学习,以期待称为核酸记忆(NAM)的新兴领域。作为一个名为NAM的垂直整合项目的积极参与者和共同拥有者,本科生和研究生将加入一个多年和多学科的研究团队,提供持续的课程和教学学分。该提案的重点是两个存储介质原型,数字核酸(dNAM)和序列核酸记忆(seqNAM)。它们都提供了一种使用DNA编码信息的新方法,并且都使用超分辨率显微镜来读取信息。在dNAM中,信息被编码到可寻址DNA折纸纳米结构顶部的DNA序列的定义空间排列中,称为NAM存储节点。DNA折纸提供了一个方便的途径和一个行之有效的方法,高产量和快速原型的NAM节点结构。缝钉链将从NAM节点结构延伸,具有用于NAM数据链的位点特异性连接的独特序列。当结合时,数据链作为互补数据成像链的对接位点,其用于称为DNA-PAINT的基于DNA的超分辨率显微镜(SRM)形式。DNA PAINT是一种随机超分辨率成像技术,它使用荧光标记的数据成像链的重复、瞬时结合来规避光的衍射极限。因此,数据成像器链充当读取头,并以优于7 nm的分辨率揭示NAM存储节点的每个位点的状态。每个数据单元处的二进制状态可以由SRM确定的NAM数据串的存在(1)或不存在(0)来定义。通过多个正交序列可以简单地实现将站点特定比特密度从1比特增加到3比特。通过将所需数据链添加到空数据单元或通过经由立足点介导的链置换移除现有数据链来执行数据链的编辑。seqNAM基于类似的存储节点平台构建,采用两个数据单元将数据串排列成有序阵列。在seqNAM中,信息被编码在保持单链的数据链的部分内。使用多色超分辨率测序(SRS)过程读取数据链的序列,该过程使用锁核酸成像链的文库。通过使用立足点介导的链侵入去除具有互补序列的靶数据链,然后添加替换数据链来进行编辑。seqNAM通过以潜在的更高密度在DNA序列内存储信息而超过dNAM。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A PCR-free approach to random access in DNA
一种无需 PCR 的 DNA 随机存取方法
  • DOI:
    10.1038/s41563-021-01089-x
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    41.2
  • 作者:
    Piantanida, Luca;Hughes, William L.
  • 通讯作者:
    Hughes, William L.
Correlative Super-Resolution and Atomic Force Microscopy of DNA Nanostructures and Characterization of Addressable Site Defects
  • DOI:
    10.1021/acsnano.1c01976
  • 发表时间:
    2021-06-17
  • 期刊:
  • 影响因子:
    17.1
  • 作者:
    Green, Christopher M.;Hughes, William L.;Kuang, Wan
  • 通讯作者:
    Kuang, Wan
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Tim Andersen其他文献

Scale free projections arise from bipartite random networks
High-Throughput Virtual Screening Molecular Docking Software for Students and Educators
适合学生和教育工作者的高通量虚拟筛选分子对接软件
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tim Andersen;Owen M. McDougal
  • 通讯作者:
    Owen M. McDougal
Random Processes with High Variance Produce Scale Free Networks
具有高方差的随机过程产生无规模网络

Tim Andersen的其他文献

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{{ truncateString('Tim Andersen', 18)}}的其他基金

TensorLABE - Robust Characterization of Data Tensors and Synthetic Data Generation
TensorLABE - 数据张量的稳健表征和合成数据生成
  • 批准号:
    2223932
  • 财政年份:
    2022
  • 资助金额:
    $ 112.5万
  • 项目类别:
    Standard Grant
EAGER: Tensor500: A Streaming Analytics High Performance Computing Benchmark
EAGER:Tensor500:流分析高性能计算基准
  • 批准号:
    1849463
  • 财政年份:
    2018
  • 资助金额:
    $ 112.5万
  • 项目类别:
    Standard Grant
EAGER: Stream500: A New Benchmark and Infrastructure for Streaming Analytics
EAGER:Stream500:流分析的新基准和基础设施
  • 批准号:
    1641774
  • 财政年份:
    2016
  • 资助金额:
    $ 112.5万
  • 项目类别:
    Standard Grant

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