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Neural network execution using nicked DNA and microfluidics.

Arnav Solanki1, Zak Griffin2, Purab Ranjan Sutradhar2

  • 1Department of Electrical and Computer Engineering, University of Minnesota Twin-Cities, Minneapolis, MN, United States of America.

Plos One
|October 19, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a microfluidic integrated circuit for performing computations directly on DNA data storage. This novel approach enables in-memory computing for dense, bio-compatible applications without electrical conversion.

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Area of Science:

  • Biotechnology
  • Data Storage
  • Bio-compatible Computing

Background:

  • DNA offers potential for dense, durable, and energy-efficient data storage.
  • Performing computations directly on DNA data is an underexplored challenge.
  • Conventional computing requires data conversion, limiting molecular data applications.

Purpose of the Study:

  • To propose a microfluidic integrated circuit for performing computations on DNA data.
  • To enable in-memory computing within the molecular domain for DNA data.
  • To explore applications in bio-compatible systems like Organ-on-Chips.

Main Methods:

  • Utilizing microfluidics and enzymes (nickases) to topologically modify DNA strands.
  • Developing a novel stochastic data representation scheme based on nicked DNA molecule concentration.
  • Designing and detailing the biochemical aspects and microfluidic device operation.

Main Results:

  • Demonstration of complex operations, including artificial neural network (ANN) computation, on DNA data.
  • Successful in-memory computing within the molecular domain.
  • Benchmarking results for neural network computation performance.

Conclusions:

  • The proposed microfluidic IC enables direct computation on DNA data, advancing molecular data storage.
  • This technology is suitable for dense, throughput-demanding, bio-compatible applications.
  • The system computes entirely in the molecular domain, offering a novel computing paradigm.