Difference between revisions of "Team:Paris Bettencourt/Model"

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<img src="https://2017.igem.org/File:Alma_eq2_PB.png">
 
<img src="https://2017.igem.org/File:Alma_eq2_PB.png">
  
<div class=text2><div class=text2left>https://static.igem.org/mediawiki/2017/a/a2/Alma_model_fig1_PB.png</div>
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<div class=text2><div class=text2left><img src="https://static.igem.org/mediawiki/2017/a/a2/Alma_model_fig1_PB.png"><span><b>Figure 1</b>: </div>
  
<div class=text2right>Using the experimental data acquired from single repressible promoters, we were able to simulate the behavior of corresponding dually repressible promoters. The experimental data was obtained by testing the impact of different configurations of repressors.</div></div>
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<div class=text2right>Using the experimental data acquired from single repressible promoters, we were able to simulate the behavior of corresponding dually repressible promoters. The experimental data was obtained by testing the impact of different configurations of operators (Figure 1) on gene expression. Results from the data obtained in configuration A and B were combined  <i>in silico</i> to create a large number of promoters. Indeed, we worked with 11 different repressors, leading to a total of 110 promoters. The results obtained for the entirety of this library are available here. We will focus for now on three promoters.</div></div>
  
  

Revision as of 02:55, 1 November 2017

MODELLING

OPTIC MODEL

your text
RNA is a light cost nucleotide material in the cell, We aim to recreate RNA agglomerations as formed in mammalian cells with triple repeat disorders,
which show liquid phase separation, forming a organelle-like vesicle, where local concentrations of enzymes can be created.
RNA is a light cost nucleotide material in the cell, We aim to recreate RNA agglomerations as formed in mammalian cells with triple repeat disorders, which show liquid phase separation, forming a organelle-like vesicle, where local concentrations of enzymes can be created.
RNA is a light cost nucleotide material in the cell, We aim to recreate RNA agglomerations as formed in mammalian cells with triple repeat disorders, which show liquid phase separation, forming a organelle-like vesicle, where local concentrations of enzymes can be created.
RNA is a light cost nucleotide material in the cell, We aim to recreate RNA agglomerations as formed in mammalian cells with triple repeat disorders, which show liquid phase separation, forming a organelle-like vesicle, where local concentrations of enzymes can be created.

SECOND MODEL

your text
RNA is a light cost nucleotide material in the cell, We aim to recreate RNA agglomerations as formed in mammalian cells with triple repeat disorders, which show liquid phase separation, forming a organelle-like vesicle, where local concentrations of enzymes can be created.
RNA is a light cost nucleotide material in the cell, We aim to recreate RNA agglomerations as formed in mammalian cells with triple repeat disorders, which show liquid phase separation, forming a organelle-like vesicle, where local concentrations of enzymes can be created.
RNA is a light cost nucleotide material in the cell, We aim to recreate RNA agglomerations as formed in mammalian cells with triple repeat disorders, which show liquid phase separation, forming a organelle-like vesicle, where local concentrations of enzymes can be created.
RNA is a light cost nucleotide material in the cell, We aim to recreate RNA agglomerations as formed in mammalian cells with triple repeat disorders, which show liquid phase separation, forming a organelle-like vesicle, where local concentrations of enzymes can be created.

Logic circuit modeling

Recent work on transcription elements showed that assembling insulated synthetic operator upstream and downstream of a insulated T7 promoter core allowed for a more diverse control of gene expression and a more specific response time (Zong et al., 2017).
More importantly, the expression of a gene regulated by such repressible promoters can be well-described by a simple equation:
α, β , ηA, KA are respectively the maximal and basal promoter activity, the Hill coefficient and the dissociation constant of the transcriptional activator-promoter core pair. ηR and ΚR represent the Hill coefficient and dissociation constant of the binding of a repressor to its cognate operator. δR represents the relaxation time, the expected time in which an operator is not bound to any repressor.
Making the assumption that the elements are insulated, we can easily combine them to create not single but dually repressible promoters, and predict their performance by generalising equation 1. In Equation (2), the fact that more than one repressor type binding to the promoter was taken into account and changes to relaxation time and the number of total microstates in the equilibrium were made accordingly.
Figure 1:
Using the experimental data acquired from single repressible promoters, we were able to simulate the behavior of corresponding dually repressible promoters. The experimental data was obtained by testing the impact of different configurations of operators (Figure 1) on gene expression. Results from the data obtained in configuration A and B were combined in silico to create a large number of promoters. Indeed, we worked with 11 different repressors, leading to a total of 110 promoters. The results obtained for the entirety of this library are available here. We will focus for now on three promoters.


Centre for Research and Interdisciplinarity (CRI)
Faculty of Medicine Cochin Port-Royal, South wing, 2nd floor
Paris Descartes University
24, rue du Faubourg Saint Jacques
75014 Paris, France
bettencourt.igem2017@gmail.com