Native DNA-Based Data Storage and Computing

Abstract

Major Goals: 1. Develop new DNA-based data storage system that use native DNA, or combinations of native and synthetic DNA for massive DNA storage of heterogeneous data in order to enable parallel writing, fast random access and single-bit random access, reduce the cost and latency of the reading/writing process. 2. Enable multidimensional storage both in the DNA content and backbone via a combination of synthetic DNA and superimposed nicks for metadata. Extend the capability of molecular recorders to allow for easy privacy-preserving information erasure and rewriting. 3. Develop new coding and machine learning methods specialized for the DNA storage media that increase the reliability of the recording system. 4. Develop watermarking and other authentication protocols for DNA-encoded data da and implement multilevel flash memory storage systems via enzymatic synthesis of length-modulated tails at the sites of nicks. 5. Develop the first known parallel, in-memory computing paradigm that operates directly on data stored in DNA. In particular, demonstrate massively parallel single instruction set register incrementing, Rule 110 and sorting in memory on nicked data.

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Document Details

Document Type
Technical Report
Publication Date
Apr 18, 2022
Accession Number
AD1189656

Entities

People

  • Olgica Milenkovic

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automata
  • Chemical Kinetics
  • Chemical Reactions
  • Chemical Synthesis
  • Computational Science
  • Computer Programming
  • Computer Science
  • Computers
  • Data Storage Systems
  • Differential Equations
  • Image Processing
  • Information Theory
  • Instruction Set Architecture
  • Integrated Circuits
  • Logic Gates
  • Machine Learning
  • Random Variables
  • Recording Systems
  • Reliability
  • Signal Processing

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Cybersecurity.
  • Molecular Genetics

Technology Areas

  • AI & ML