SemiSynBio-III: Scalable Nucleic Acid Memory

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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.