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Memory and Combinatorial Logic Based on DNA Inversions: Dynamics and Evolutionary Stability.

Jesus Fernandez-Rodriguez1, Lei Yang1, Thomas E Gorochowski1

  • 1Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States.

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|November 10, 2015
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Summary
This summary is machine-generated.

This study demonstrates genetic memory using DNA inversion enzymes. While the memory switch is stable, the NOT gate circuit evolved instability due to host strain adaptation reducing enzyme expression.

Keywords:
design automationgenetic circuitgenetic compilersynthetic biologysystems biology

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Area of Science:

  • Synthetic biology
  • Molecular engineering
  • Genetics

Background:

  • Genetic memory systems can be engineered using enzymes that catalyze DNA inversions.
  • Each DNA orientation can represent a binary state ('bit') for information storage.

Purpose of the Study:

  • To engineer and evaluate genetic memory and logic circuits using DNA invertases.
  • To assess the evolutionary stability and failure modes of these synthetic biological circuits.

Main Methods:

  • Utilized two DNA invertases, FimE and HbiF, for irreversible DNA reorientation between two states.
  • Constructed a genetic memory switch set by FimE and reset by HbiF.
  • Built a NOT gate circuit with FimE controlled by an input promoter and HbiF constitutively expressed.

Main Results:

  • The memory switch demonstrated stability over 400 hours (14 state changes).
  • The NOT gate circuit failed within 54 hours due to host strain evolution reducing invertase expression.
  • Genome sequencing confirmed the circuit remained intact, but the host evolved to mitigate continuous invertase expression.

Conclusions:

  • Enzyme-based genetic memory circuits show promise but require careful consideration of evolutionary robustness.
  • Host strain adaptation can lead to circuit failure, highlighting the need to evaluate long-term stability and failure modes.
  • Further research is needed to design more evolutionarily stable synthetic genetic circuits for complex applications.