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Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks.

Alec A K Nielsen1, Christopher A Voigt2

  • 1Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Molecular Systems Biology
|November 26, 2014
PubMed
Summary
This summary is machine-generated.

Researchers engineered programmable genetic logic gates using dCas9 and guide RNAs in E. coli. These synthetic biology tools enable complex computations within living cells, controlling cellular functions with high precision.

Keywords:
CRISPRTALETetR homologuegenetic compilersynthetic biology

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Area of Science:

  • Synthetic biology
  • Molecular biology
  • Biotechnology

Background:

  • Genetic circuits are essential for cellular computation.
  • Scalable methods for building genetic circuits are needed.
  • dCas9 and guide RNAs offer programmable gene regulation.

Purpose of the Study:

  • To develop and characterize dCas9-based transcriptional logic gates.
  • To construct and connect these gates into larger synthetic circuits.
  • To interface synthetic circuits with native cellular regulatory networks.

Main Methods:

  • Designed synthetic Escherichia coli σ70 promoters responsive to specific sgRNAs.
  • Utilized dCas9 for targeted gene repression via sgRNAs.
  • Connected logic gates to form NOR gates and multi-gate circuits.
  • Interfaced synthetic circuits with the malT transcription factor to control cellular phenotypes.

Main Results:

  • Developed NOT gates with high on-target repression (56-440 fold) and low off-target effects (<1.3 fold).
  • Successfully constructed Boolean-complete NOR gates and a 4-layer sgRNA circuit.
  • Demonstrated phenotypic control (sugar utilization, chemotaxis, phage resistance) by linking synthetic circuits to E. coli's malT.

Conclusions:

  • dCas9-sgRNA systems provide a robust and scalable platform for building complex genetic circuits.
  • Synthetic circuits can be precisely controlled and interfaced with native cellular machinery.
  • This work advances the potential for programming cellular behavior and computation.