Journal article
Jiancheng Yang, J. Virostko, D. Hormuth, Junyan Liu, A. Brock, J. Kowalski, T. Yankeelov
bioRxiv, 2020
bioRxiv, 2020
Semantic Scholar
DOI
PubMedCentral
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APA
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Yang, J., Virostko, J., Hormuth, D., Liu, J., Brock, A., Kowalski, J., & Yankeelov, T. (2020). An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines. BioRxiv.
Chicago/Turabian
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Yang, Jiancheng, J. Virostko, D. Hormuth, Junyan Liu, A. Brock, J. Kowalski, and T. Yankeelov. “An Experimental-Mathematical Approach to Predict Tumor Cell Growth as a Function of Glucose Availability in Breast Cancer Cell Lines.” bioRxiv (2020).
MLA
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Yang, Jiancheng, et al. “An Experimental-Mathematical Approach to Predict Tumor Cell Growth as a Function of Glucose Availability in Breast Cancer Cell Lines.” BioRxiv, 2020.
BibTeX Click to copy
@article{jiancheng2020a,
title = {An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines},
year = {2020},
journal = {bioRxiv},
author = {Yang, Jiancheng and Virostko, J. and Hormuth, D. and Liu, Junyan and Brock, A. and Kowalski, J. and Yankeelov, T.}
}
Abstract
We present the development and validation of a mathematical model that predicts how glucose dynamics influence metabolism and therefore tumor cell growth. Glucose, the starting material for glycolysis, has a fundamental influence on tumor cell growth. We employed time-resolved microscopy to track the temporal change of the number of live and dead tumor cells under different initial glucose concentrations and seeding densities. We then constructed a family of mathematical models (where cell death was accounted for differently in each member of the family) to describe overall tumor cell growth in response to the initial glucose and confluence conditions. The Akaikie Information Criteria was then employed to identify the most parsimonious model. The selected model was then trained on 75% of the data to calibrate the system and identify trends in model parameters as a function of initial glucose concentration and confluence. The calibrated parameters were applied to the remaining 25% of the data to predict the temporal dynamics given the known initial glucose concentration and confluence, and tested against the corresponding experimental measurements. With the selected model, we achieved an accuracy (defined as the fraction of measured data that fell within the 95% confidence intervals of the predicted growth curves) of 77.2 ± 6.3% and 87.2 ± 5.1% for live BT-474 and MDA-MB-231 cells, respectively.
