Speaker
Description
Cancer-on-Chip experiments reproduce complex biological environments to study the immune response to cancer and test the effect of therapies. Following a digital-twin approach, mathematical models reproducing Cancer-on-Chips dynamics have the potential to be able to produce in-silico different scenarios and to be largely economically convenient. However, a critical aspect in the employment of mathematical model outcomes in biomedical research is the necessity of a careful calibration of the model parameters, to ensure reliable outcomes.
In this context, we focus on a nonlocal integro-differential model for Cancer-on-Chip experiments where chemotherapy-treated tumour cells release chemical signals activating the immune response. Model reliability is assessed through a Global Sensitivity Snalysis to capture parameter influence and nonlinear effects.
The impact of 13 parameters is evaluated over a region of the parameter space using 11 outputs describing immune cell spatial distribution and temporal dynamics. To address computational costs, a two-step approach is adopted: the Morris method first ranks parameter importance, identifying six key parameters affecting all outputs; the extended Fourier Amplitude Sensitivity Test (eFAST) then quantifies their contributions.
Results highlight the feasibility of the parameter space and identify parameters related to the chemical field and cell–substrate adhesion as dominant. These findings suggest model simplifications—such as neglecting cell–cell alignment in the absence of experimental evidence—and emphasize the need for additional data to reduce uncertainty in the most influential parameters and improve predictive accuracy.
