ArticlePloS one2021
An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- A data assimilation framework for predicting the spatiotemporal response of high-grade gliomas to chemoradiation.BMC cancer · 2025Article
- Stochastic pharmacodynamics of a heterogeneous tumour-cell population.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.Cancer biology & therapy · 2024Article
- Comparing mechanism-based and machine learning models for predicting the effects of glucose accessibility on tumor cell proliferation.Scientific reports · 2023Article
- A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making.Cancers · 2023Article
- The Role of Reprogrammed Glucose Metabolism in Cancer.Metabolites · 2023Review
- Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics.American journal of physiology. Cell physiology · 2023Review
- Hybrid computational models of multicellular tumour growth considering glucose metabolism.Computational and structural biotechnology journal · 2023Review
- Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis.PLoS computational biology · 2023Article
- A data assimilation framework to predict the response of glioma cells to radiation.Mathematical biosciences and engineering : MBE · 2023Article
- Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.