HPC Center of Excellence in Personalised Medicine

The HPC/Exascale Centre of Excellence in Personalised Medicine (PerMedCoE) aims to provide an efficient and sustainable entry point to the HPC/Exascale-upgraded methodology to translate omics analyses into actionable models of cellular functions of medical relevance. Simulation of cellular mechanistic models is essential for the translation of omic data to medical-relevant actions, and these should be accessible to the end-users in the appropriate environment of the PerMed-specific big confidential data. To achieve the goal, the project will optimise the core applications for cell-level simulations to the new pre-exascale platforms and integrate PerMed into the new European HPC/Exascale ecosystem by offering access to HPC/Exascale-adapted and optimised software. The project will run a comprehensive set of PerMed use cases and build the basis for the sustainability of the project outputs by coordinating PerMed and HPC communities and reaching out to industrial and academic end-users, with use cases, training, expertise, and best practices. The PerMedCoE cell-level simulations will fill the gap between the molecular and organ-level simulations from the CompBioMed and BioExcel CoEs, with which the project is aligned at different levels. It will connect methods developers with HPC, HTC and HPDA experts (at POP and HiDALGO CoEs). The PerMedCoE will also work with biomedical consortia (i.e. ELIXIR, LifeTime initiative) and pre-exascale infrastructures (BSC and CSC), including a substantial co-design effort. 

CSC will contribute to PerMedCoe by leading the work on the optimization and usability of PerMed workflows in HPC/HPDA. CSC also contributes to the software optimisation for Exascale architecture and to the implementation and benchmarking of HPC/Exascale solutions to support PerMed use cases.

At CSC, PerMedCoe project is part of the ELIXIR programme.



This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 951773.