Understanding the Effect of Compositional Context on Biocircuit Performance

2013 SURF project description

• Mentor: Richard Murray
• Co-mentor: Enoch Yeung

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

1. Michael Elowitz, Alan Levine, Eric Siggia, Peter.Swain (2002) Stochastic Gene Expression in a Single Cell, Science 297: 1183-1186

[1]

1. Jason Kelly et al (2009) Measuring the activity of Biobrick promoters using an in vivo reference standard, Journal of Biological Engineering 3:4.

[2]

• http://openwetware.org/wiki/TABASCO (highly detailed simular, similar to simulac)
• http://paulsson.med.harvard.edu/Web_pdfs/paulsson2001QRBiophis.pdf
• http://www.nature.com/nbt/journal/v22/n7/abs/nbt988.html (E. Coli analysis of local-bidirectionality layout)
• 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://www.sciencemag.org/content/307/5717/1965/suppl/DC1 (Noise propagates possibly from adjacency in gene layout)

• http://www.cds.caltech.edu/~murray/wiki/index.php/BE_262_project,_2010