Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data
Abstract
Abstract 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.
- Publication Date:
- Research Org.:
- Univ. of Texas, Austin, TX (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI Identifier:
- 1619585
- Alternate Identifier(s):
- OSTI ID: 1506470
- Grant/Contract Number:
- 5C0009286; SC0009286
- Resource Type:
- Published Article
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Name: Scientific Reports Journal Volume: 8 Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES
Citation Formats
Lima, E. A. B. F., Ghousifam, N., Ozkan, A., Oden, J. T., Shahmoradi, A., Rylander, M. N., Wohlmuth, B., and Yankeelov, T. E. Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data. United Kingdom: N. p., 2018.
Web. doi:10.1038/s41598-018-32347-9.
Lima, E. A. B. F., Ghousifam, N., Ozkan, A., Oden, J. T., Shahmoradi, A., Rylander, M. N., Wohlmuth, B., & Yankeelov, T. E. Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data. United Kingdom. https://doi.org/10.1038/s41598-018-32347-9
Lima, E. A. B. F., Ghousifam, N., Ozkan, A., Oden, J. T., Shahmoradi, A., Rylander, M. N., Wohlmuth, B., and Yankeelov, T. E. Fri .
"Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data". United Kingdom. https://doi.org/10.1038/s41598-018-32347-9.
@article{osti_1619585,
title = {Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data},
author = {Lima, E. A. B. F. and Ghousifam, N. and Ozkan, A. and Oden, J. T. and Shahmoradi, A. and Rylander, M. N. and Wohlmuth, B. and Yankeelov, T. E.},
abstractNote = {Abstract 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.},
doi = {10.1038/s41598-018-32347-9},
journal = {Scientific Reports},
number = 1,
volume = 8,
place = {United Kingdom},
year = {Fri Sep 28 00:00:00 EDT 2018},
month = {Fri Sep 28 00:00:00 EDT 2018}
}
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