TY - JOUR
T1 - Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model
AU - Mirzaei, Navid Mohammad
AU - Changizi, Navid
AU - Asadpoure, Alireza
AU - Su, Sumeyye
AU - Sofia, Dilruba
AU - Tatarova, Zuzana
AU - Zervantonakis, Ioannis K.
AU - Chang, Young Hwan
AU - Shahriyari, Leili
N1 - Funding Information:
This project has been funded in whole or in part with Federal funds from the Department of Energy under Award Number DE-SC0021630 (N. M., A.A., I.K.Z., Y.H.C., L.S.), the National Cancer Institute, National Institutes of Health, under Subcontract No. 21X131F part of the Leidos Biomed’s prime Contract No. 75N91019D00024, Task Order No. 75N91019F00134 (N.M., L.S.), and the National Cancer Institute, National Institutes of Health, under Award Number U54CA209988 (Z.T., I.K.Z., Y.H.C., L.S.). The content of this publication does not necessarily reflect the views or policies of the funding agencies, nor does mention of trade names, commercial products or organizations imply endorsement by the U.S. Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
Copyright: © 2022 Mohammad Mirzaei et al.
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1009953
DO - 10.1371/journal.pcbi.1009953
M3 - Article
C2 - 35294447
AN - SCOPUS:85126939981
VL - 18
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 3
M1 - e1009953
ER -