CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Area of Science:

• Computational Biology
• Mathematical Modeling
• Systems Biology
• Artificial Intelligence in Oncology

Background:

• Tumor-immune interactions are modeled using differential equations, but parameter estimation and uncovering unknown biological mechanisms from limited, noisy data are significant challenges.
• Existing models often face limitations due to incomplete understanding of tumor-immune dynamics and experimental measurement constraints, leading to unknown terms in equations.
• These challenges fall under gray-box identification, requiring integration of experimental data with partial system knowledge to recover unknown parameters and model components.

Purpose of the Study:

• To develop a novel cancer biology-informed neural network model (CBINN) for inferring unknown parameters and discovering missing mechanisms in tumor-immune dynamics.
• To address the dual challenges of accurate parameter estimation and the discovery of governing mathematical equations from sparse and noisy biological data.
• To provide a robust framework for gray-box identification in complex dynamical systems, specifically within cancer biology.

Main Methods:

• Development of a cancer biology-informed neural network (CBINN) framework designed to handle sparse and noisy measurements.
• Application and testing of the CBINN model on three distinct nonlinear compartmental tumor-immune models.
• Evaluation of the CBINN's robustness across various synthetic noise levels and discussion of parameter identifiability using computational and Fisher information matrix analysis.

Main Results:

• The CBINN framework successfully inferred unknown parameters within the system of equations for nonlinear compartmental tumor-immune models.
• The model demonstrated efficacy in uncovering underlying physical laws and mathematical structures governing tumor-immune dynamics from scattered, noisy data.
• Robust performance was observed across multiple noise levels, validating the generalizability and efficacy of the proposed methodology for complex dynamical systems.

Conclusions:

• The developed CBINN provides an effective solution for gray-box identification problems in modeling tumor-immune dynamics, accurately estimating parameters and discovering missing biological mechanisms.
• This approach offers a powerful tool for researchers dealing with inverse problems and incomplete knowledge in complex dynamical systems, particularly in cancer research.
• The study validates the CBINN's generalizability and efficacy, offering valuable guidance for advancing computational approaches in systems biology and oncology.