Difference between revisions of "SURF 2012: Effects of circuit "layout" on circuit dynamics"

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* http://openwetware.org/wiki/TABASCO (highly detailed simular, similar to simulac)
* http://openwetware.org/wiki/TABASCO (highly detailed simular, similar to simulac)
* http://paulsson.med.harvard.edu/Web_pdfs/paulsson2001QRBiophis.pdf
* http://paulsson.med.harvard.edu/Web_pdfs/paulsson2001QRBiophis.pdf
* http://www.nature.com/nbt/journal/v22/n7/abs/nbt988.html

Revision as of 02:58, 4 February 2012

The goal of this project is to measure variability in gene expression that is relevant for synthetically designed circuits. The issue that we are trying to understand is now much variability arises for the expression of a given circuit under degrees of freedom that are typically not controlled in synthetic designs:

  • Location and orientation of circuit elements in the plasmid
  • Vectors used for expressing the circuit, including copy number and antibiotic resistance
  • Growth conditions (temperature, oxygen, media, growth phase)

To understand how these (and other) factors will affect circuit operation, a simple genetic circuit consisting of 1 or 2 promoters will be built and implemented in a variety of conditions. The dynamic response of the circuit will be measured, including cell-to-cell variability (via flow cytometry or microscopy).

Project objectives:

  • Characterize the differences (if any) in mean expression level for a standard circuit (PtetR:GFP) in different backbones, growth media, antibiotic concentrations, inducer levels, etc
  • Characterize differences in expression distributions (if any) using flow cytometry; use cell sorting to check for differences in cell phenotype
  • Construct a simple circuit using different design choices and characterize differences in expression level (and distribution)

The circuit that we test could be a genetic switch, repressilator, incoherent feedforward loop, etc. We could do both simulation and experiments (plus comparisons). This would likely involve cloning, measuring using flow cytometer and/or microscope, modeling using stochastic simulation and (possibly) differential equations. More to do here than one person can do, so lots of possibilities.