期刊
SCIENTIFIC REPORTS
卷 8, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-018-32347-9
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资金
- Cancer Prevention Research Institute of Texas (CPRIT) [RR160005]
- NIH [NCI U01CA174706, NCI R01CA186193, NIBIB R21EB019646]
- U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program [DE-5C0009286]
- German Science Foundation (DFG) [WO-671 11-1]
- NATIONAL CANCER INSTITUTE [U01CA174706, R01CA186193] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R21EB019646] Funding Source: NIH RePORTER
Two of the central challenges of using mathematical models for predicting the spatiotemporal development of tumors is the lack of appropriate data to calibrate the parameters of the model, and quantitative characterization of the uncertainties in both the experimental data and the modeling process itself. We present a sequence of experiments, with increasing complexity, designed to systematically calibrate the rates of apoptosis, proliferation, and necrosis, as well as mobility, within a phase-field tumor growth model. The in vitro experiments characterize the proliferation and death of human liver carcinoma cells under different initial cell concentrations, nutrient availabilities, and treatment conditions. A Bayesian framework is employed to quantify the uncertainties in model parameters. The average difference between the calibration and the data, across all time points is between 11.54% and 14.04% for the apoptosis experiments, 7.33% and 23.30% for the proliferation experiments, and 8.12% and 31.55% for the necrosis experiments. The results indicate the proposed experiment-computational approach is generalizable and appropriate for step-by-step calibration of multi-parameter models, yielding accurate estimations of model parameters related to rates of proliferation, apoptosis, and necrosis.
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