Mathematical tumour growth modelling: from in-vitro tumour organoids to patient-specific models

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The simulation of tumour growth presents many challenges from a modelling point of view, both when performing in vitro models and when corresponding to tumours in patients. Obviously, the difficulties are much more relevant for patient-specific models because the amount of information available to build and validate the model is much smaller. However, modelling in vitro tumour organoids provides much more data that, when combined with deep learning tools, may allow us to delve deeper into the mechanisms by which tumour cells self-organize to form tumours as a function of the microenvironment.

In both cases, the challenges focus on the highly dynamic and heterogeneous behaviour present in the tumour, which requires involving multiple temporal and spatial scales, generally including extracellular, cellular, and intracellular variables.

In this talk, several examples will be presented where the modelling of tumour growth under both conditions has been studied.