Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs

• URL: http://arxiv.org/abs/2510.13431v1
• Date: Wed, 15 Oct 2025 11:28:18 GMT
• Title: Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs
• Authors: Kayode Olumoyin, Katarzyna Rejniak,
• Abstract summary: Physics-informed neural networks (PINNs) are neural networks that embed the laws of dynamical systems into their loss function as constraints.<n>In this work, we present a PINN framework applied to oncology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Physics-informed neural networks (PINNs) are neural networks that embed the laws of dynamical systems modeled by differential equations into their loss function as constraints. In this work, we present a PINN framework applied to oncology. Here, we seek to learn time-varying interactions due to a combination therapy in a tumor microenvironment. In oncology, experimental data are often sparse and composed of a few time points of tumor volume. By embedding inductive biases derived from prior information about a dynamical system, we extend the physics-informed neural networks (PINN) and incorporate observed biological constraints as regularization agents. The modified PINN algorithm is able to steer itself to a reasonable solution and can generalize well with only a few training examples. We demonstrate the merit of our approach by learning the dynamics of treatment applied intermittently in an ordinary differential equation (ODE) model of a combination therapy. The algorithm yields a solution to the ODE and time-varying forms of some of the ODE model parameters. We demonstrate a strong convergence using metrics such as the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

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