Speaker
Description
In this talk, we will present our work to predict the dynamics of cancer-on-chip experiments using data-informed differential models \cite{article_bbbtz}.
We will consider a complex one-dimensional network along which tumor and immune cells evolve in response to chemotactic signals. This model is managed by coupled partial differential equations and solved by a Hybridized Discontinuous Galerkin method \cite{url_b}.
Synthetic data will be used to tune the parameters of the governing equations employing Bayesian inversion techniques. After this, we will assess the uncertainty on the predictions of the dynamics due to the uncertainty remaining on the parameters after the tuning procedure (i.e., their posterior distribution).
Finally, we will focus on surrogate models to make the analysis computationally affordable. Especially, we will exploit surrogates to approximate the mapping of the uncertain parameters to the quantities of interest of the system (e.g. the concentration of immune and tumor cells or the total amount of chemoattractant).
Bibliography
@misc{article_bbbtz,
title={Towards uncertainty quantification of a model for cancer-on-chip experiments},
author={Silvia Bertoluzza and Vittoria Bianchi and Gabriella Bretti and Lorenzo Tamellini and Pietro Zanotti},
year={2026},
eprint={2602.06018},
archivePrefix={arXiv},
primaryClass={cs.CE},
url={https://arxiv.org/abs/2602.06018},
}
@software{url_b,
author = {S. Bertoluzza},
title = {{B}io{N}et{F}lux},
url = {https://github.com/silvia-bertoluzza/BioNetFlux},
year = {2026},
}
