How can I build a protein network pathway from a gene expression quantification file? Assume I have found the top 0.01% most frequent genes from a gene expression file. 

Let's say, these are 10 genes and I want to study the protein protein interactions, the protein network and pathway.

I thought to use string-db or interactome, but I am not sure what would be a plausible way to approach this problem and to build the protein network etc. Are there other more suitable databases?

How can I build a mathematical graph or network for these data?
There are multiple ways to do this, and multiple protein interaction databases besides the ones you mentioned, such as BioGRID or IntAct. Interaction databases are different in how interactions are defined, sometimes it can be experimental evidence of interaction, sometimes coexpression, orthology-based predictions, etc. 

There is no single solution to your problem. For String-DB you can use their R package STRINGdb.



# string-db
library(STRINGdb)

# retrieve full graph for H. sapiens
string_db &lt;- STRINGdb$new(version="10", species=9606,
                          score_threshold=400, input_directory="" )

# define genes of interest
genes.of.interest &lt;- ...

# get their neighbors
neighbors &lt;- string_db$get_neighbors(genes.of.interest)

# get the subgraph of interest (your genes + their neighbors)
my.subgraph &lt;- string_db$get_subnetwork(c(genes.of.interest, neighbors))

# look how many genes and interactions you have
my.subgraph


If you prefer GUI, you might consider using Cytoscape. They have multiple addons for interaction databases, e.g. for STRINGdb. There you can just provide your gene list and get a network in a few clicks.