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 with interesting behaviors.