Team:UCSF/Modeling

From 2013.igem.org

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If we can get this result from our model, then it would help us figure out how much inducer to add to our experiments in order to get the desired result.  
If we can get this result from our model, then it would help us figure out how much inducer to add to our experiments in order to get the desired result.  
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Before getting into any modeling, we had to first figure out what the design of the synthetic circuit would be. It’s essentially the same diagram as the one shown on the synthetic circuit page (link here), but we added letters to represent each variable for our model. (R:C – gRNA/dCas9 complex; R – gRNA; C – dCas9; L – low inducible promoter; H – high inducible promoter)
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Before getting into any modeling, we had to first figure out what the design of the synthetic circuit would be. It’s essentially the same diagram as the one shown on the synthetic circuit page (link here), but we added letters to represent each variable for our model. <br>(R:C – gRNA/dCas9 complex; R – gRNA; C – dCas9; L – low inducible promoter; H – high inducible promoter)
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<br><b><FONT COLOR="#008000">Micro-Transformations, Macro Changes: </FONT COLOR="#008000"></b> The goal of our exhibit was to provide relatable information about the general techniques scientists use every day in the laboratory. We presented brief “elevator talks,” broken down into two topics to more clearly present the information for the public.  The first talk explained the <a href="https://static.igem.org/mediawiki/2013/d/d3/Central_Dogma.pdf" target="_blank">Central Dogma of Biology</a><span>, where DNA is transcribed into mRNA, and mRNA translated into protein. The second elevator talk was about the execution of <a href="https://static.igem.org/mediawiki/2013/b/b9/MicroTransformation.pdf" target="_blank">transformation in molecular biology</a><span> and the basic experimental concepts, while also giving real life examples of how it is used as an application.  In addition to our presentations, we gave away synthetic biology informational <a href="https://static.igem.org/mediawiki/2013/1/10/Book_Mark_Handouts_Exploratorium.pdf" target="_blank">bookmarks</a><span> and scientific temporary tattoos, and brought culture plates with E. coli transformed with GFP and RFP for visual demonstration of transformations in cells.
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<br><b><FONT COLOR="#008000">ASSUMPTIONS: </FONT COLOR="#008000"></b>While creating the model for our system, we made a few assumptions about some of the aspects of the model that would be impossible for us to know within a few months. We made four assumptions: 1) protein degradation is linear; 2) protein production is based on a hill function and also depends on inducer concentration; 3) repression is governed by a hill function and depends on the concentration of dCas9 and gRNA complex; and 4) that the binding and unbinding of dCas9 and gRNA complex happens much faster than the production/degradation of gRNA and fluorescent proteins (the complex is at <a href="http://en.wikipedia.org/wiki/Steady_State_theory#Quasi-steady_state" target="_blank">Quasi Steady State</a><span>).
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Revision as of 18:14, 19 September 2013

Modeling: Decision Making Circuit

The primary goal of the modeling portion for the synthetic circuit project is to create a model that will allow us to predict how our circuit will react to different concentrations of inducer. Since our circuit should express GFP at lower inducer concentrations and RFP at high inducer concentrations, we should expect the graph to look something like the one below:


If we can get this result from our model, then it would help us figure out how much inducer to add to our experiments in order to get the desired result.
Before getting into any modeling, we had to first figure out what the design of the synthetic circuit would be. It’s essentially the same diagram as the one shown on the synthetic circuit page (link here), but we added letters to represent each variable for our model.
(R:C – gRNA/dCas9 complex; R – gRNA; C – dCas9; L – low inducible promoter; H – high inducible promoter)


ASSUMPTIONS: While creating the model for our system, we made a few assumptions about some of the aspects of the model that would be impossible for us to know within a few months. We made four assumptions: 1) protein degradation is linear; 2) protein production is based on a hill function and also depends on inducer concentration; 3) repression is governed by a hill function and depends on the concentration of dCas9 and gRNA complex; and 4) that the binding and unbinding of dCas9 and gRNA complex happens much faster than the production/degradation of gRNA and fluorescent proteins (the complex is at Quasi Steady State).
The UCSF iGEM team interacts with patrons at the Exploratorium: After Dark event