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

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.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1];  [1];  [2];  [1]
  1. Univ. of Texas, Austin, TX (United States)
  2. Technical University of Munich, Garching (Germany)
Publication Date:
Research Org.:
Univ. of Texas, Austin, TX (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1506470
Grant/Contract Number:  
SC0009286
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
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 States: 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 States. 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., and Yankeelov, T. E. Fri . "Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data". United States. doi:10.1038/s41598-018-32347-9. https://www.osti.gov/servlets/purl/1506470.
@article{osti_1506470,
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 = {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 States},
year = {2018},
month = {9}
}

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