The study of complex biological systems is best approached by incorporating many perspectives. Thus omics strategies are the perfect toolset to deliver quantitative information from multi molecular layers of an investigated system such as differentiating stem cells. By combining proteomics and lipidomics approaches novel control mechanisms such as network based interconnected feedback influencing cell fate decision will be identified and elucidated. The aim of this project is to develop novel strategies in the field of integrative biology allowing the in deep investigation of dynamic systems. To achieve this goal, still a lot of effort has to be invested too continuously improve and to develop novel analytical approaches in separation, detection and quantification of different lipid classes.
Diffuse gliomas are the most frequent primary human brain tumors with glioblastoma being the most aggressive among them. The mean survival for patients suffering from glioblastoma is restricted to 12 months, despite multimodal therapy regimens. Tumor cells commonly exhibit high levels of endoplasmic reticulum stress, which triggers the unfolded protein response (UPR), a mechanism, which recently has gained a lot of attention in the treatment of malignancies. Despite its broad clinical importance, quantitative models that systematically describe the UPR in cancer cells are missing so far. In order to lever the UPR for therapeutic intervention in glioma, strong needs exist for an integrated vision of how this molecular pathway contributes to tumor growth and infiltration.
The aim of the SUPR-G systems biology approach is to combine interdisciplinary approaches and state of the art methodology – including translatome and proteome analyses, computational modeling, human glioma specimen and in vivo animal model target validation – to gain novel and system-wide insights into the UPR.
These data will serve to establish the first highly integrated quantitative network model of the UPR in glioma to reveal potential therapeutic candidates for subsequent validation using the individual model systems of the consortium. The constructed model will be made publicly available via a web based interface and will be integrated into already existing online tools enabling the scientific community to develop novel targeted therapies interfering with UPR-mediated cell fate decisions in the context of glioma and beyond.