Use Cases

PerMedCoE works on a comprehensive set of use cases to drive the development of cell-level simulations, with the aim to:

  • Drive the PerMedCoE developments, including experiences and examples on how to implement the specific PerMed solutions in HPC/Exascale environments and how to engineer and redesign the software to meet the application needs.
  • Provide the community with specific technical solutions to their problems, in the form of workflows optimised and adapted to HPC, HTC and HPDA, that are easily instantiable with the users’ specific data, and portable to their own environments, including the required security and privacy aspects.

Cancer Diagnosis Based on omics Information

This use case will propose cancer treatments for individual patients using associated clinical information employing omics data and personalised cell-level models.

Drug synergies for cancer treatment

This use case will find effective drug combinations for cancer by using drug-response experiments and publicly available databases, personalised cell-level models and BioExcel’s GROMACS simulations.

Personalised modelling of groups of rare-disease related patients

This use case will focus on simulations of individual disease characteristics, such as the degree of severity, in support of the rare diseases’ diagnoses where only a small number of cases are available.

Tumour evolution based on single-cell omics and imaging

This is the most complex use case both in terms of the biological problem and the computational needs. These simulations will handle hundreds of thousands to millions of individual cells, each one of them with their own omics data and individual models of metabolism, regulation and signalling. Cells will interact with each other (agent-based models) and with the environment, modelled as fluids, from where cells get nutrients and input information, in gradients related to their relative position to the other cells.

COVID-19 multiscale modelling of the virus and patients’ tissue

This use case will use disease maps to build COVID-19 models and omics data to personalise models to different patients’ groups (by age, gender, nationality, etc).

More information on how our HPC-enabled multiscale simulation helps uncover mechanistic insights of the SARS-CoV-2 infection is available here.