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

• Published in: PloS one Vol. 16; no. 7; p. e0240765
• Main Authors: Yang, Jianchen, Virostko, Jack, Hormuth, David A., Liu, Junyan, Brock, Amy, Kowalski, Jeanne, Yankeelov, Thomas E.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.07.2021; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Biomedical engineering; Breast cancer; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Cell culture; Cell death; Cell growth; Cell Line, Tumor; Cell Proliferation; Confidence intervals; Confluence; Diagnosis; Engineering; Female; Glucose; Glucose - metabolism; Glycolysis; Growth; Growth curves; Health aspects; Humans; Kinases; Mathematical models; Medical research; Medicine and Health Sciences; Metabolism; Microscopy; Model accuracy; Models, Biological; Models, Theoretical; Oncology; Ordinary differential equations; Parameter identification; Partial differential equations; Physical Sciences; Research and analysis methods; Tumor cell lines; Tumor cells; Tumors; United States; United States > US; Texas
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0240765

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.