Difference between revisions of "Team:SKLMT-China/Model Overview"
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<h2 class="title">Model Overview</h2> | <h2 class="title">Model Overview</h2> | ||
| − | <p>We set up a model for facilitating the transformation of new chassis bacteria Pseudomonas fluorescenspf-5, such as building a promoter library. In our model，encode each three-base sequence with quaternary number(A,T,C,G represents quaternary number 0,1,2,3 ),and then get a vector with 64 dimensions from AAA to GGG, which calculates the frequency of appearance for each type in DNA sequence.</p> | + | <p>We set up a model for facilitating the transformation of new chassis bacteria <latin>Pseudomonas fluorescenspf-5</latin>, such as building a promoter library. In our model，encode each three-base sequence with quaternary number(A,T,C,G represents quaternary number 0,1,2,3 ),and then get a vector with 64 dimensions from AAA to GGG, which calculates the frequency of appearance for each type in DNA sequence.</p> |
<p>We preliminarily assume that there is a simple linear relationship between the 64 components of this vector and the final result, and preliminarily confirm our hypothesis by comparing the difference between calculated value and measured value by solving sparse equations. Subsequently, we further strengthened our model by using the neural network model with stronger stability and more relevant parameters, and obtained an algorithm to predict the promoter strength. </p> | <p>We preliminarily assume that there is a simple linear relationship between the 64 components of this vector and the final result, and preliminarily confirm our hypothesis by comparing the difference between calculated value and measured value by solving sparse equations. Subsequently, we further strengthened our model by using the neural network model with stronger stability and more relevant parameters, and obtained an algorithm to predict the promoter strength. </p> | ||
| − | <p>We tested 25 promoters’ strength with firefly and another variable goes to promoter sequence gotten from pf5 genome in ncbi. As we know, house-keeping genes sometiomes express strongly and consistently. So we picked up 70 base pairs up from several genes of them as strong promoters in pf-5. As for the weak promoter, we selected weak promoters that change insignificantly under different ionic stresses from the transcriptome information | + | <p>We tested 25 promoters’ strength with firefly luciferase and another variable goes to promoter sequence gotten from pf5 genome in ncbi. As we know, house-keeping genes sometiomes express strongly and consistently. So we picked up 70 base pairs up from several genes of them as strong promoters in <latin>pf-5</latin>. As for the weak promoter, we selected weak promoters that change insignificantly under different ionic stresses from the transcriptome information in <latin>Lim, C. K.<sup>1 </sup></latin>The method is the same as the construction of strong promoter. </p> |
<p>By combining three bases to encode DNA, solving a sparse system of equations,and strengthening the linear relationship between system input and promoter strength by neural network, a good correlation was found between the promoter sequence and promoter strength. It is through this mutually-informing modelling that we were able to figure out the relationship between promoter sequence and strength in P. fluorescence. This model informs us a promoter strength based on its specific sequence and it has also been verified by using other parts. This approach is also of valuable reference for future teams’ project. </p> | <p>By combining three bases to encode DNA, solving a sparse system of equations,and strengthening the linear relationship between system input and promoter strength by neural network, a good correlation was found between the promoter sequence and promoter strength. It is through this mutually-informing modelling that we were able to figure out the relationship between promoter sequence and strength in P. fluorescence. This model informs us a promoter strength based on its specific sequence and it has also been verified by using other parts. This approach is also of valuable reference for future teams’ project. </p> | ||
Revision as of 00:12, 16 October 2018
Model Overview
We set up a model for facilitating the transformation of new chassis bacteria
We preliminarily assume that there is a simple linear relationship between the 64 components of this vector and the final result, and preliminarily confirm our hypothesis by comparing the difference between calculated value and measured value by solving sparse equations. Subsequently, we further strengthened our model by using the neural network model with stronger stability and more relevant parameters, and obtained an algorithm to predict the promoter strength.
We tested 25 promoters’ strength with firefly luciferase and another variable goes to promoter sequence gotten from pf5 genome in ncbi. As we know, house-keeping genes sometiomes express strongly and consistently. So we picked up 70 base pairs up from several genes of them as strong promoters in
By combining three bases to encode DNA, solving a sparse system of equations,and strengthening the linear relationship between system input and promoter strength by neural network, a good correlation was found between the promoter sequence and promoter strength. It is through this mutually-informing modelling that we were able to figure out the relationship between promoter sequence and strength in P. fluorescence. This model informs us a promoter strength based on its specific sequence and it has also been verified by using other parts. This approach is also of valuable reference for future teams’ project.