Towards uncertainty quantification of a model for cancer-on-chip experiments

2026-02-05

AI Summary

This study addresses the challenge of predictive reliability in cancer-on-chip experiments arising from parameter uncertainty by employing a Keller–Segel-type chemotaxis model to describe interactions between cancer and white blood cells. The work introduces an integrated computational framework that innovatively combines global sensitivity analysis (via Sobol and Morris methods), Bayesian inversion, forward uncertainty propagation, and a sparse grid surrogate model, with efficient numerical solution of the underlying partial differential equations achieved through a hybrid discontinuous Galerkin method. This approach substantially enhances computational efficiency, successfully identifies influential parameters, yields data-driven posterior parameter distributions, and quantifies the impact of residual parameter uncertainty on model outputs, thereby providing a robust theoretical foundation for reliable prediction and control in cancer-on-chip experimentation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This study is a first step towards using data-informed differential models to predict and control the dynamics of cancer-on-chip experiments. We consider a conceptualized one-dimensional device, containing a cancer and a population of white blood cells. The interaction between the cancer and the population of cells is modeled by a chemotaxis model inspired by Keller-Segel-type equations, which is solved by a Hybridized Discontinuous Galerkin method. Our goal is using (synthetic) data to tune the parameters of the governing equations and to assess the uncertainty on the predictions of the dynamics due to the residual uncertainty on the parameters remaining after the tuning procedure. To this end, we apply techniques from uncertainty quantification for parametric differential models. We first perform a global sensitivity analysis using both Sobol and Morris indices to assess how parameter uncertainty impacts model predictions, and fix the value of parameters with negligible impact. Subsequently, we conduct an inverse uncertainty quantification analysis by Bayesian techniques to compute a data-informed probability distribution of the remaining model parameters. Finally, we carry out a forward uncertainty quantification analysis to compute the impact of the updated (residual) parametric uncertainties on the quantities of interest of the model. The whole procedure is sped up by using surrogate models, based on sparse-grids, to approximate the mapping of the uncertain parameters to the quantities of interest.

Problem

• uncertainty quantification
• cancer-on-chip
• parameter uncertainty
• model prediction
• differential models

Innovation

• uncertainty quantification
• cancer-on-chip
• Hybridized Discontinuous Galerkin
• Bayesian inverse problem
• surrogate modeling