Mathematical models are usually built and validated in the context of studies focused on a specific molecular layer (typically signaling, metabolism, or gene regulation) and based on a single type of omic data (transcriptomic, metabolic, (phosphor)proteomic). However, in the biological reality, the distinction between these molecular layers disappear as proteins, metabolites and nucleic acids are all deeply interacting and influencing each others. Therefore, in order to better understand the interplay among these processes , it is critical to develop models that are able to span over multiple omics layers and account for trans-lmic interactions (Yugi et al,2016).
We are specifically focusing on trying to bridge together signaling pathways and metabolism through the development of models integrating metabolomic, proteomic and phosphoproteomic data. In order to do so, we are building resources that account for signaling/metabolic pathways crosstalks and build mechanistic models integrating these layers. This will also be achieved through collaborations with wet lab experimentalist able to generate dataset accounted for proteomic, phosphoproteomic and metabolomics in parallel. We will also expand tools developed by our team such as CellNopt or PHONEMeS to train mechanistic models using trans-omic resources and experimental dataset. These models will allow getting better insights into the characteristics of signaling and metabolic pathways, and will be used to perform predictions of relevant drug targets in the context of cancer and metabolic diseases.
Research in this area is partially funded by the European H2020 MCSA Innovative Training Network SyMBioSys.