SemiSynBio-III: Scalable Nucleic Acid Memory

SemiSynBio-III：可扩展核酸内存

• 批准号: 2227626
• 负责人: Benjamin Johnson
• 金额: $ 150万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-15 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227626&HistoricalAwards=false
• 关键词: SemiSynBio; III; Scalable; Nucleic; Acid

Worldwide digital data generation is growing at a rate that rapidly outpaces the capacity of current data-storage technologies. Over 150 billion terabytes a year are predicted to be generated by 2025. DNA has emerged as a next-generation data storage solution due to its high stability and high information density. However, the potential of DNA-based information storage has not been fully exploited due to the cost and energy required for DNA synthesis and sequencing. In this project, self-assembled DNA nanostructures place DNA data strands with nanometer precision in all three dimensions. The stored information is recovered optically by time-resolved super-resolution microscopy. As a result, the need for unique DNA strands is significantly reduced with a corresponding decrease in the cost of synthesis. Furthermore, data readout is non-destructive and storage volume scales beyond terabyte levels. This project develops and delivers workforce development training at the interface of Synthetic Biology and Semiconductor research. Students trained through this project will be immersed in an interdisciplinary research team with expertise in Material Science, Synthetic Biology, Computer Science and Electrical Engineering. The research results will be disseminated through high-impact journals, premier conferences, websites, and social media, and will be integrated into multiple courses at Boise State. The research team will partner with the Boise State Institute for Inclusive and Transformative Scholarship to recruit and retain students from underrepresented backgrounds.The project aims to advance DNA-based information storage through robust data encoding/decoding algorithms, deep neural network-based image processing, scalable synthetic biology, custom imaging arrays, and an evolution-inspired sequence optimization algorithm. To do so, a rotation-invariant data-encoding, error-correction scheme will be developed along with a state-of-the-art deep neural network-based approach for adaptive image processing, and a Bayes-optimal data recovery/error correction algorithm to optimize probe localization and drift correction. Large custom DNA “scaffolds” with orthogonal DNA staple strands will be created to construct origami with more data sites per assembly. DNA scaffolds will be created to allow multiple storage nodes to be built in a single “one-pot” synthesis. A time-correlated super-resolution microscopy technique will be developed, which seamlessly integrates super-resolution and fluorescence lifetime microscopy to record interactions between dye-labeled imager strands and their sequence complement in three dimensions. Custom imaging arrays based on state-of-art single-photon avalanche diode technology will be designed and manufactured by commercial foundry services to achieve 5 X 5 X 1 nm readout resolution. Finally, an evolution-inspired sequence optimization algorithm will be developed to optimize DNA sequences both for readout and origami synthesis. In doing so, this work will increase information density that can be stored in DNA nanostructures and the efficiency of read, which reduces the amount of DNA needed for large scale data storage, improving both the scalability and sustainability of DNA-based information storage.The project was jointly funded by Division of Electrical, Communications and Cyber Systems (ECCS) in the Directorate for Engineering (ENG), Division of Computing and Communication Foundations (CCF) in the Directorate for Computer and Information Science and Engineering (CISE), Division of Molecular and Cellular Biosciences (MCB) in the Directorate for Biological Sciences (BIO), and the Division of Materials Research (DMR) in the Directorate for Mathematical and Physical Sciences (MPS).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.

全球数字数据生成的速度正在迅速超过当前数据存储技术的容量，预计到 2025 年，每年将生成超过 1500 亿兆字节。DNA 因其高性能而成为下一代数据存储解决方案。然而，由于DNA合成和测序所需的成本和能量，基于DNA的信息存储的潜力尚未得到充分开发。在该项目中，自组装DNA纳米结构放置DNA数据。在所有三个维度上都具有纳米精度的链，通过时间分辨超分辨率显微镜以光学方式恢复存储的信息，因此，对独特 DNA 链的需求显着减少，同时合成成本也相应降低。读出是非破坏性的，存储容量规模超过 TB 级。通过该项目培训的学生将在合成生物学和半导体研究领域开发并提供劳动力发展培训。沉浸在材料科学、合成生物学、计算机科学和电气工程专业知识的跨学科研究团队中，研究成果将通过高影响力期刊、重要会议、网站和社交媒体传播，并将整合到多个课程中。该研究团队将与博伊西州立包容性和变革奖学金研究所合作，招募和留住来自弱势背景的学生。该项目旨在通过强大的数据编码/解码算法推进基于 DNA 的信息存储，基于深度神经网络的图像处理、可扩展的合成生物学、定制成像阵列和受进化启发的序列优化算法为此，将开发旋转不变的数据编码、纠错方案以及状态。 -将创建基于深度神经网络的自适应图像处理方法，以及用于优化探针定位和漂移校正的贝叶斯最优数据恢复/纠错算法，以实现具有正交DNA主链的大型定制DNA“支架”。将创建每个组装具有更多数据位点的折纸，以允许在单个“一锅”合成中构建多个存储节点，从而无缝集成超分辨率显微镜技术。分辨率和荧光寿命显微镜将设计和制造基于最先进的单光子雪崩二极管技术的定制成像阵列，以记录染料标记的成像链及其序列互补之间的相互作用。最后，将开发一种受进化启发的序列优化算法，以优化读出和折纸合成的 DNA 序列。存储在 DNA 纳米结构中并提高读取效率，从而减少了大规模数据存储所需的 DNA 数量，提高了基于 DNA 的信息存储的可扩展性和可持续性。该项目由电气、通信和网络部门联合资助​​系统（ECCS）工程理事会 (ENG)、计算机和信息科学与工程理事会 (CISE) 计算和通信基础部 (CCF)、生物科学理事会 (BIO) 分子和细胞生物科学部 (MCB) ，以及数学和物理科学理事会 (MPS) 下的材料研究部 (DMR)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，认为值得支持。以及更广泛的影响审查标准。

项目成果

In-vitro validated methods for encoding digital data in deoxyribonucleic acid (DNA)

用于在脱氧核糖核酸 (DNA) 中编码数字数据的体外验证方法

• DOI: 10.1186/s12859-023-05264-6
• 发表时间: 2023-04
• 期刊: BMC Bioinformatics
• 影响因子: 3
• 作者: Mortuza, Golam Md;Guerrero, Jorge;Llewellyn, Shoshanna;Tobiason, Michael D.;Dickinson, George D.;Hughes, William L.;Zadegan, Reza;Andersen, Tim
• 通讯作者: Andersen, Tim