Networks and bioinformatics

In the “omics” era, the Superti-Furga laboratory is specialized in translating different omics data into biological significance. The laboratory is characterizing the different biological functions of cells as the outcome of molecular networks of gene products such as proteins, protein complexes, RNAs and metabolites. Inevitably, a pathogenic status is manifested in alterations of these molecular networks. Entire biological pathways are mapped and linked with other cellular processes. We also characterize the basic functional units of cellular action, the multi-protein complexes forming molecular machines, in sufficient detail to identify potential medical exploitation.

Antiviral networks

Decades of research have unravelled the mechanisms by which the host immune system detects invading threats and eventually elicits the innate immune response; and more importantly, how viruses, with their limited arsenal of proteins, have evolved to sabotage the host innate immune response by modulating key effector proteins and signalling pathways. We previously showed (Pichlmair et al., Nature 2012) that viruses employ a multitude of ways to both subvert as well as coup the host cellular system, and that the host largely relies on the homeostasis of the cellular system to detect and inhibit viral intrusion. Current efforts are focused on understanding the dynamics of the anti-viral networks and to identify novel mechanisms and genes that play a critical role in restoring the cellular homeostasis.


A systems-level characterization of SoLute Carriers (SLCs)

In an effort to globally characterize and understand SLC biology, the Superti-Furga laboratory is involved in systematically mapping SLC interactions at many different levels (genetic interactions, protein-protein interactions, drug-transporter relationships, etc) The successful integration of all these layers of information into a comprehensive, functional SLC-based network is key in order to obtain what will be the most advanced SLC data-driven model to date. The computational analysis of this integrated network by different network-oriented approaches (analysis of modularity, search of repetitive or specific motifs...) will help us to achieve a global, systems-level characterization of the function, organization, and regulation of SLCs as a gene family. At the same time, it will allow us to prioritize molecular hypotheses regarding the function and regulation of individual SLC members, leading therefore to their "de-orphanization".