Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model

The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key...

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Bibliographic Details
Published in:PLoS computational biology 2022-03, Vol.18 (3), p.e1009953
Main Authors: Mohammad Mirzaei, Navid, Changizi, Navid, Asadpoure, Alireza, Su, Sumeyye, Sofia, Dilruba, Tatarova, Zuzana, Zervantonakis, Ioannis K, Chang, Young Hwan, Shahriyari, Leili
Format: Article
Language:English
Subjects:
Adipocytes
Animals
Biology and Life Sciences
Breast cancer
Breast Neoplasms - metabolism
Cancer therapies
Cell Line, Tumor
Cytokines
Cytotoxicity
Dendritic cells
Disease Models, Animal
Dynamic tests
Female
Gene expression
Humans
Interleukin 6
Investigations
Kinases
Mathematical models
Medicine and Health Sciences
Mice
Molecular dynamics
Optimization
Ordinary differential equations
Parameter estimation
Parameter identification
Parameter sensitivity
Physiological aspects
Relative abundance
Research and Analysis Methods
Sensitivity analysis
Tumor Microenvironment
Tumors
Variables
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Summary:The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model's parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009953