Bioinformatics for the study of metabolism in glioblastoma

Glioblastoma (GB) is an astrocytic brain tumor with extremely low survival rate despite surgical and pharmacological treatment, with a predominant migration across myelinated tracts [1]. One of the main clues being currently investigated is the  «metabolic coupling» among invasive cells and the tumoral microenvironment, where lactate plays a major role [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 GB invasiveness. We are specifically concentrating on the integration of transciptomic and metabolic data, which can be modeled through complex networks or by using multi-block analyses to identify critical modules. Results should help to better understand the association of metabolic coupling and the invasive phenotype.