Team:SUSTech Shenzhen/Model/For Reporter
Kinetic model for wnt signaling pathway
Simplified Wnt signaling pathway
For the modeling part, the most important thing is formulating the scenario, so that the secretion and reception can be regarded as two systems that both have inputs and outputs.
After simplification, the diagram is like this. At the secretion stage,the wnt gene acts as an input. Factors and parameters influence the maturing of wnt protein, which is the output. At the signaling stage, however, Wnt protein becomes the input. And the output changes into target genes of the Wnt pathway.
For the signaling process, we want to define the importance of components in the pathway and use the results to optimize our cell line construction. We extracted the key genes out, including FZD, APC, and TCF as X. Each gene contributes a bit to the final expression of Wnt target genes. Here, we choose C-MYC as our output target. Besides, we choose three genes that never affect Wnt pathway as our control.
Grey relation analysis model
When it comes to the actions, we first collected 50 RNA-seq datasets of normal tissues from TCGA and used grey relation analysis to confirm that all of the samples are under similar conditions. Then we did cluster analysis of selected genes and found that the results actually resemble the real cases, and we used gene distance to pre-define some correlation parameters which are used in the next step.
Cluster analysis model
Pathway analysis model
During the pathway analysis, we included the explicit and implicit connection between genes. Here, the x-axis is gene and the y-axis shows the relative contribution coefficient, which indicates the impact caused by the change of X against target genes. The results showed that TCF is the major role in Wnt pathway response. No matter how much is P, it represents the strength of explicit pathway modified correlation coefficient. These results may help us to overexpress TCF in our receptor cell line.
Finally, for part two—Wnt secretion, we wanted to use co-expression analysis to screen the potential factors behind the wnt secretion pathway. We used these known factors, like enzymes involved in wnt modification, to predict new unknown factors. This is the basic principal of co-expression analysis, which compares the expression patterns of two genes across samples and defines their co-expressing correlation values. The higher the value, the more similar they are, which may undertake the same function. And once we get the co-expressing gene list, we can design a more specific gRNA library to do our engineered screening.
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