Theoretical and computational methods to reverse engineer signaling networks have been developed using genome-wide proteomic, expression, and deletion data, and these techniques employ Boolean methods, mutual information, Bayesian inference, regulation matrix methods based on differential equations, and machine learning approaches (reviewed in [19], [20]).
With the results of the differential expression analysis, a "regulation matrix" was created.
Suppose we have a binding strength matrix W with the same dimension of the regulation matrix A (N × M ) showing the possible connections between N genes and M TFs.
To further understand regulatory relationships between transcription factors and the genes involved in C9 and C10 response, a regulation matrix was generated by YEASTRACT (http://www.yeastract.com/) [ 33].
We obtained a binary regulation matrix of transcription factors and H2H genes after completing the above analysis (see Additional file 4).
Metastatic carcinomas had a significant up-regulation of genes associated with cell cycle regulation, matrix modulation, protein folding and proteasomal degradation whereas cell differentiation genes, growth factor pathway genes and regulators of actin organization were significantly down-regulated.
In this regard, the expression of genes that are targets for regulation by Nupr1 has been implicated in key protumorigenic pathways, including cell cycle regulation, matrix remodeling, autophagy, cell cannibalism and apoptosis inhibition.
To generate the regulation matrix, the YEASTRACT database (http://www.yeastract.com/) [ 24] was solicited, in which the differentially expressed genes identified by the RNA-seq analysis were searched against all of the transcription factors (TFs) in the YEASTRACT database (only documented regulations with direct or indirect evidences were taken into consideration).
A pairwise co-regulation matrix was built including the information from all the hits.
To test the significance of our network we picked those non-hit genes from our co-regulation matrix, which are best connected within the network and considered them to be part of the clusters in our network.
