Difference between revisions of "Understanding the Effect of Compositional Context on Biocircuit Performance"

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(Created page with "Synthetic biocircuits often involve multiple components --- different genes that work together to achieve a desired function. For example, the repressilator employs three dis...")
 
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* http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0020074 (Eukaryote analysis of " ")
* http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0020074 (Eukaryote analysis of " ")
* http://ec.asm.org/content/10/1/43.short (for Yeast of " " as well as effect of adding an interspacing sequence/reporter cassette )
* http://ec.asm.org/content/10/1/43.short (for Yeast of " " as well as effect of adding an interspacing sequence/reporter cassette )
* http://www.sciencemag.org/content/307/5717/1965/suppl/DC1  (Noise propagates possibly from adjacency in gene layout)  
* http://www.sciencemag.org/content/307/5717/1965/suppl/DC1  (Noise propagates possibly from adjacency in gene layout)
=== Sources of Variability in Gene Expression to Control ===
* Variation in plasmid copy numbers from cell to cell may lead to significant differences in gene expression across a colony of cells. 
* Variation from terminator efficiency.
* Variation from promoter affinity.
* Variation from cell health/exposure to environmental stress.
* Variation from stochastic nature of (low copy number) molecular  reactions involved in transcription and translation.
* Variation from comparing different reporters with different maturation times.
 
=== Source of Variability in Gene Expression to Test ===
* Variation from spatial layout of circuit - we will test the effect of convergent vs divergent promoter orientation and control for distance from construct to replication origin (as this may also be a source of variability).

Revision as of 01:19, 4 January 2013

Synthetic biocircuits often involve multiple components --- different genes that work together to achieve a desired function. For example, the repressilator employs three distinct genes that are designed to inhibit each other with a cyclic structure to produce oscillation. The toggle switch uses two genes, both repressing each other as they compete to be the dominant active gene. A signal cascade involves a sequence of genes where the arrival of a chemical signal triggers the first gene to activate a second (downstream) gene, which in turn activates a third gene, and so forth. The increasing number of biocircuits fuels hope for the assembly of complex synthetic systems constructed from multiple biocircuits. However, synthetic biologists and engineers are finding that biocircuit context can impact system performance. Biocircuit context can be classified roughly into three categories: compositional context, host context, and environmental context.

The goal of this project is to understand the effect of compositional context on biocircuit performance. Compositional context refers to the effects arising from coupling multiple genes or undesigned interactions between genes located on the same molecule. We will explore how spatial composition, or layout, of different genes can impact performance of one or more biocircuits. To understand how these factors will affect circuit operation, we will take a simple genetic circuit consisting of 2 or more genes and implement it in multiple ways, varying the orientation and relative location of the genes to each other. The dynamic response of the circuit will be measured, including cell-to-cell variability (via flow cytometry or microscopy). The circuit that we test could be a genetic switch, repressilator, incoherent feedforward loop, etc. The project could involve both simulations of circuit dynamics and experimental quantification of circuit dynamics, with comparisons between the two. This would likely involve intensive cloning, measuring using flow cytometer and/or microscope, modeling using stochastic simulation and (possibly) differential equations.

Possible SURF Activities:

  • Construct multiple versions of that simple circuit to systematically explore the compositional context space.
  • Characterize the mean expression dynamics (using a plate reader) and expression distribution (using flow cytometry or fluorescence microscopy) of each version of the circuit.
  • Quantify differences in mean and distributional expression (if any) between the versions of the circuit.
  • Build a simulation for each version of the circuit dynamics using a stochastic simulation or differential equations.
  • Develop design strategies to attenuate or insulate against the effects of compositional context (in simulation or experimentally)


References: