An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.

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 th...

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Autores principales:	Jianchen Yang, Jack Virostko, David A Hormuth, Junyan Liu, Amy Brock, Jeanne Kowalski, Thomas E Yankeelov
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/150d9accb7674f75a65bd44e8bc827b6
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spelling	oai:doaj.org-article:150d9accb7674f75a65bd44e8bc827b62021-12-02T20:05:05ZAn experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.1932-620310.1371/journal.pone.0240765https://doaj.org/article/150d9accb7674f75a65bd44e8bc827b62021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0240765https://doaj.org/toc/1932-6203We 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.Jianchen YangJack VirostkoDavid A HormuthJunyan LiuAmy BrockJeanne KowalskiThomas E YankeelovPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0240765 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jianchen Yang Jack Virostko David A Hormuth Junyan Liu Amy Brock Jeanne Kowalski Thomas E Yankeelov An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.
description	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.
format	article
author	Jianchen Yang Jack Virostko David A Hormuth Junyan Liu Amy Brock Jeanne Kowalski Thomas E Yankeelov
author_facet	Jianchen Yang Jack Virostko David A Hormuth Junyan Liu Amy Brock Jeanne Kowalski Thomas E Yankeelov
author_sort	Jianchen Yang
title	An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.
title_short	An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.
title_full	An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.
title_fullStr	An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.
title_full_unstemmed	An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.
title_sort	experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/150d9accb7674f75a65bd44e8bc827b6
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An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.

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