A Microrna Based Synthetic Network as Stable Expression Unit in Mammalian Cells

  • Type:
    Conference Presentation
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 7, 2010
  • Duration:
    30 minutes
  • Skill Level:
  • PDHs:

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The utility and scalability of complex synthetic gene networks is hampered by fluctuations in stoichiometry between different gene products in individual cells. A critical contribution to the fluctuations arises from variation in the basic transcription efficiency of a gene product that in turn is determined by the cellular environment, the DNA state, and most importantly by the gene's copy number. This is typically the case when the expression unit consists of a promoter combined with an open reading frame. Therefore it is critical to construct sophisticated expression units whose gene product will depend only weakly on the number of unit copies in a cell and on the global transcription efficiency. In other words, the output protein expression will not depend on the number of DNA molecules that code for this protein, a property we call adaptation. This would raise a number of interesting hypotheses: (a) that such circuitry might be found in nature when a constant level of gene expression is required, (b) that such circuits could become valuable tools in the construction of increasingly complex synthetic circuits because they will contribute to overall robustness of the system by decreasing natural variability in the circuits' components, and (c) that such circuits could enable finely controlled gene delivery methods in gene therapy applications. Here we show that a specific feed-forward loop network topology functions as a stable expression unit in mammalian cells. We utilize transcriptional and post-transcriptional machinery and show adaptivity of an output compared to a promoter-ORF combination. We used transient transfections to characterize the units, and developed a modeling approach that takes sources of fluctuations into account and generally reproduces raw experimental data.

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