  Inflammatory bowel diseases (IBD) are complex diseases in which the gut
microbiota is attacked by the immune system of genetically predisposed subjects
when they are exposed to yet unclear environmental factors. The complexity of
this class of diseases makes them suitable to be represented and studied with
network science. In the project, the metagenomic data of the gut microbiota of
control, Crohn's disease, and ulcerative colitis subjects were divided in three
ranges (prevalent, common, uncommon). Then, correlation networks and
co-expression networks were used to represent this data. The former networks
involved the calculation of the Pearson's correlation and the use of the
percolation threshold to binarize the adjacency matrix, whereas the latter
involved the construction of the bipartite networks and the monopartite
projection after binarization of the biadjacency matrix. Then, centrality
measures and community detection were used on the so-built networks. The main
results obtained were about the modules of "Bacteroides", which were connected
in control subjects' correlation network, "Faecalibacterium prausnitzii", where
co-enzyme A became central in IBD correlation networks and "Escherichia coli",
which module has different position in the different diagnoses networks.
