Hi there, I do a little project where I analyze regulatory networks motifs (such as feedforward/feedback loops etc.). Specifically, I explore attractor landscape of Boolean networks representing these motifs. What I would like is take some real biological appearances of these motifs in real regulatory networks, with some context (e.g. all 1-edge neighbours of motif nodes) and see how the motif behaves in these contexts, compared to isolated behavior.
I looked at RegulonDB. There are several different versions of the transcription regulatory network: TF-Gene, TF-Operon, TF-TF. My 1st question: What's difference between these? Which one would you suggest is most relevant to use here? The explanation on RegulonDB didn't help me much.
As I looked into the network files, the regulations were usually edges from TFs to genes, while the genes/TFs usually appear in both roles - the same gene/TF in different roles is differentiated by small/big first letter. That leads to bipartite graph in Cytoscape. This certainly makes sense, but in my use-case (that is, representation as a Boolean network), I don't want to distinguish between gene and its protein functioning as TF - I just want the network to represent the fact that gene A positively/negatively regulates gene B. I therefore solved this using "toLower()" on all the genes/TFs identifiers, merging genes and TFs together. 2nd question: Is this merging "correct" (does the resulting data make sense)? Do you know of any better data source for my use-case?
Any answers, ideas, help will be appreciated :-)