Publication Date

8-2023

Date of Final Oral Examination (Defense)

July 2023

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Computing

Department

Computer Science

Major Advisor

Tim Andersen, Ph.D.

Advisor

William L. Hughes, Ph.D.

Advisor

Reza Zadegan, Ph.D.

Abstract

The rapid growth of data generation from electronic devices has created a critical demand for efficient and sustainable data storage solutions. Traditional storage systems face challenges regarding reliability, energy consumption, and scalability, necessitating the exploration of alternative technologies. This dissertation explores the potential of Deoxyribonucleic Acid (DNA) as an alternative storage medium, along with the associated challenges and potential solutions.

This dissertation focuses on Digital Nucleic Acid Memory (dNAM), which utilizes Single Molecule Localization Microscopy (SMLM) to encode and store data within DNA structures called DNA origami. SMLM surpasses the limitations of light’s diffraction limit, enabling the imaging of biological samples at a molecular scale. The robustness and data density of the dNAM algorithm rely heavily on the accuracy and performance of SMLM. Within dNAM, emitter localization and error correction are crucial steps, and this dissertation primarily focuses on these aspects.

To improve emitter localization in dNAM, Deep Learning (DL) techniques are employed. This dissertation investigates the impact of multi-emitter situations, where multiple emitters are attached during data acquisition. A neural network based image up-sampling algorithm is developed to progressively increase the resolution of the image. The developed algorithm preserves the emitter centroid position while upsampling it to a higher-resolution image, effectively isolating attached emitters. By extracting the emitter centroid positions from multiple resolutions, the dissertation analyzes the impact of attached emitters on localization accuracy.

Additionally, the dissertation addresses the development of an advanced error correction algorithm for dNAM. A preliminary algorithm is initially used to successfully store 20 bytes of digital information in DNA. However, to improve performance and accuracy, the algorithm was enhanced by incorporating the intensity information of each data point. The impact of this addition is thoroughly studied. Furthermore, the error correction algorithm is extended to support arbitrary-shaped 3D/2D DNA origami structures, enabling scalability and versatility.

The findings of this research highlight the potential of DNA as a viable storage medium and shed light on the challenges and solutions specific to dNAM. The incorporation of DL techniques for emitter localization demonstrates improved accuracy and efficiency. Moreover, the advanced error correction algorithm enhances the reliability and capacity of dNAM. These outcomes contribute to the overall robustness and efficiency of dNAM as a data storage method.

DOI

https://doi.org/10.18122/td.2118.boisestate

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