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Mathematical models of the biological processes that are deregulated in diseases show a high complexity not only because of the number of genes and pathways involved, but also because of the numerous patients or samples to include in the simulations in order to be predictive. One way to address these issues is to combine High Performance Computer (HPC)-based methods to scale up the power of the computation with mechanistic and statistical modelling approaches.

In the context of the PerMedCoE project, we have brought together computer scientists and modellers to define the needs and the methods that need to be developed to optimise the simulations of these computationally-demanding models. Two use cases were defined: the first one describes a workflow that inputs omics data of cancer patients and outputs personalised combinations of drugs per patient based on a model of intracellular signalling pathways; and the second one aims at uncovering COVID-19-related mechanisms that explain the differences in severity among patients using personalised agent-based models of different cell types.

We will conclude by briefly discussing the possible clinical applications of such types of models.