Glioblastoma multiforme (GBM) is an astrocytic brain tumor with extremely low survival rate despite surgical and pharmacological treatment, with a predominantmigration across myelinated tracts . One of the main clues being currently investigated is the «metabolic coupling» among invasive cells and tumor microenvironment, for example the tumor avidity for neoglucogenic aminoacids and lactate [2,3].
Multi-omics techniques generate heterogeneous big data to study metabolites and transcripts (transcriptomics, fluxomics, metabolomics), and a context-specific analysis is required. This work is centered on the development of bioinformatic methods perform an integrated analysis of heterogeneous datasets in order to elucidate the GBM invasiveness. We are specifically concentrating on the integration of transciptomic (bulk and single cell) and metabolic data, which can be modeled through complex networks or by using multi-block analyses to identify critical modules. Modern probabilistic approaches will then allow us to estimate parameters related to the association of metabolic coupling (and regulatory elements) and the invasive phenotype.