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Related Experiment Video

Updated: Feb 11, 2026

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
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Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design.

Chelsea Y Hu1,2, Melissa K Takahashi1,3, Yan Zhang1,4

  • 1Robert F. Smith School of Chemical and Biomolecular Engineering , Cornell University , Ithaca , New York 14850 , United States.

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|May 8, 2018
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Summary
This summary is machine-generated.

Researchers created synthetic gene networks using small RNAs (sRNAs) to build transcriptional negative autoregulation (NAR) systems. These RNA-based networks speed up cellular responses and lower gene expression, enhancing synthetic biology tools.

Keywords:
RNA synthetic circuitrymodel-guided designnegative autoregulationparameterizationsensitivity analysistranscriptional sRNA regulator

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Area of Science:

  • Synthetic biology
  • Molecular and cell biology
  • Genetic engineering

Background:

  • Small RNAs (sRNAs) are versatile regulators in synthetic biology.
  • Transcriptional negative autoregulation (NAR) is a fundamental genetic network motif found in nature.
  • NAR motifs are known to decrease network response times and steady-state signal levels.

Purpose of the Study:

  • To construct a novel transcriptional negative autoregulation (NAR) network using small RNAs (sRNAs).
  • To design and prototype sRNA-based NAR constructs using cell-free systems and computational modeling.
  • To demonstrate the functional transfer of these sRNA NAR networks into living cells (Escherichia coli).

Main Methods:

  • Utilized cell-free transcription-translation (TX-TL) reactions for initial design and prototyping.
  • Employed computational modeling and parameter sensitivity analysis for accurate prediction of NAR function.
  • Validated sRNA NAR network performance in both cell-free systems and the bacterium Escherichia coli.

Main Results:

  • Successfully designed and prototyped sRNA-based transcriptional negative autoregulation (NAR) constructs.
  • Demonstrated that the sRNA NAR networks significantly reduce network response time.
  • Showed a reduction in steady-state gene expression levels in Escherichia coli using the developed sRNA networks.

Conclusions:

  • Expanded the synthetic biology toolbox with a new class of RNA-based genetic networks.
  • Validated the predictable function of sRNA NAR networks in both cell-free and cellular environments.
  • Paved the way for constructing more complex and predictable RNA genetic networks.