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<div id=header1 class="header">MODELLING</div> | <div id=header1 class="header">MODELLING</div> | ||
− | <div class=textbody><div class=line><a href="#header2" class=buttons> | + | <div class=textbody><div class=line><a href="#header2" class=buttons>Gel optics modelling</a><a href="#header3" class=buttons>RNA organelle modelling</a><a href="#header4" class=buttons >Logic circuit modelling</a></div></div> |
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Revision as of 21:10, 1 November 2017
MODELLING
Gel Optics Modelling
Characterizing the light signal inside the gel medium in which our bacteria grow is necessary to predict the performance of our optic control system.
We developed a strategy to measure the light intensity landscape created by a laser going through a gel (See Gel Optics page ) . We described this intensity landscape by fitting its absorption and scattering. Since the scattering observed was low, we tested whether we could predict the intensity landscape in a given gel (0.5% Gelrite with LB media) based on a simple cuvette absorbance reading.
The absorbance readings were used to find [ε*C], the parameter which governs the logarithmic decrease of the laser (see Table 2 on Gel Optics page).
We assumed the light scattering in 0.5% Gelrite to be the same as in 1% Gelrite. This is an oversimplification but predicting the scattering of a gel based on its concentration would make it necessary to acquire light diffusion data for a range of gel concentrations which was outside the scope of our model development.
We observed that our predicted light profiles were close to the fit of the intensity data recorded. This suggest that the intensity profile of a laser in the gel can be characterize from a single absorbance reading.
Laser Beam Shape Modelling
To predict and visualize the volume of gel in which the immobilized bacteria would be activated, we modelled the 3D shapes of the laser in the different gels. We assumed that bacteria would be sharply activated in an ON/OFF manner when light reached an intensity threshold equal to 80% the intensity used in the optogenetic work of Fernandez-Rodriguez et al. This allowed us to predict the volume activated by our two intersecting laser at any point within the three gel characterized.At 6cm depth, the activating volume was of the order of a few mm2 for the 1% gels. This is very promising as it suggests that a sub-mm2 resolution could be obtained with lasers having a smaller beam diameter in less concentrated gels.
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 repressors with interesting behaviors:
Figure 2: Modeling results for an in silico dually repressible promoter.
These three candidates were chosen for further testing depending on parameter values: the dissociation constant of repressor-operator binding had to be reasonably low and the Hill coefficient η needed to allow for a cooperative behavior. They are also easily available on the iGEM registry and are very commonly used in research. Each promoter was designed to contain two out of the three chosen repressors - TetR, P22c2, HKcI. For each couple of repressors, four different arrangements of the operators were characterized experimentally under different input combinations. More information on the experimental part of this project can be found here.
References
Zong, Y., Zhang, H., Lyu, C., Ji, X., Hou, J., Guo, X., Ouyang, Q. and Lou, C. (2017). Insulated transcriptional elements enable precise design of genetic circuits. Nature Communications, 8(1).