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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: Yang, Jianchen, Virostko, Jack, Hormuth, David A., Liu, Junyan, Brock, Amy, Kowalski, Jeanne, Yankeelov, Thomas E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277046/
https://www.ncbi.nlm.nih.gov/pubmed/34255770
http://dx.doi.org/10.1371/journal.pone.0240765
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author Yang, Jianchen
Virostko, Jack
Hormuth, David A.
Liu, Junyan
Brock, Amy
Kowalski, Jeanne
Yankeelov, Thomas E.
author_facet Yang, Jianchen
Virostko, Jack
Hormuth, David A.
Liu, Junyan
Brock, Amy
Kowalski, Jeanne
Yankeelov, Thomas E.
author_sort Yang, Jianchen
collection PubMed
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.
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spelling pubmed-82770462021-07-20 An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines Yang, Jianchen Virostko, Jack Hormuth, David A. Liu, Junyan Brock, Amy Kowalski, Jeanne Yankeelov, Thomas E. PLoS One Research Article 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. Public Library of Science 2021-07-13 /pmc/articles/PMC8277046/ /pubmed/34255770 http://dx.doi.org/10.1371/journal.pone.0240765 Text en © 2021 Yang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yang, Jianchen
Virostko, Jack
Hormuth, David A.
Liu, Junyan
Brock, Amy
Kowalski, Jeanne
Yankeelov, Thomas E.
An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277046/
https://www.ncbi.nlm.nih.gov/pubmed/34255770
http://dx.doi.org/10.1371/journal.pone.0240765
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