Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data.

Area of Science:

• Mathematical Biology
• Computational Oncology
• Biophysics

Background:

• Mathematical models are crucial for predicting tumor spatiotemporal development.
• Challenges include limited data for parameter calibration and quantifying experimental/modeling uncertainties.

Purpose of the Study:

• To systematically calibrate parameters (apoptosis, proliferation, necrosis, mobility) in a phase-field tumor growth model.
• To quantify uncertainties in model parameters using a Bayesian framework.
• To develop a generalizable experiment-computational approach for multi-parameter model calibration.

Main Methods:

• Designed a sequence of in vitro experiments with increasing complexity.
• Characterized human liver carcinoma cell behavior under varying conditions (cell concentration, nutrients, treatment).
• Employed a Bayesian framework for uncertainty quantification of model parameters.

Main Results:

• Achieved average differences between 11.54%-14.04% for apoptosis, 7.33%-23.30% for proliferation, and 8.12%-31.55% for necrosis experiments.
• Demonstrated accurate estimations of proliferation, apoptosis, and necrosis rates.
• Validated the generalizability of the proposed approach for multi-parameter models.

Conclusions:

• The presented experiment-computational strategy effectively calibrates phase-field tumor growth models.
• Accurate parameter estimation is achievable through systematic, multi-stage experimental design.
• This approach enhances the reliability of mathematical models in cancer research.