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

Navid Mohammad Mirzaei, Navid Changizi, Alireza Asadpoure, Sumeyye Su, Dilruba Sofia, Zuzana Tatarova, Ioannis K. Zervantonakis, Young Hwan Chang, Leili Shahriyari

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere1009953
JournalPLoS computational biology
Volume18
Issue number3
DOIs
StatePublished - Mar 2022
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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