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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\u2019s 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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009953","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T17:47:13Z","timestamp":1647452833000},"page":"e1009953","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0971-0514","authenticated-orcid":true,"given":"Navid","family":"Mohammad 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