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

doi:10.1371/journal.pcbi.1009953

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 journal › Article › peer-review

10 Scopus citations

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 language	English (US)
Article number	e1009953
Journal	PLoS computational biology
Volume	18
Issue number	3
DOIs	https://doi.org/10.1371/journal.pcbi.1009953
State	Published - Mar 2022

Funding

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.

Funders	Funder number
U.S. government
Author National Institutes of Health National Institutes of Health National Institutes of Health National Institutes of Health The Bev Hartig Huntington's Disease Foundation National Institutes of Health	21X131F, 75N91019D00024, 75N91019F00134
U.S. Department of Energy	DE-SC0021630
National Institute of Health-National Cancer Institute	U54CA209988

ASJC Scopus subject areas

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

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10.1371/journal.pcbi.1009953

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

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{\textquoteright}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.",

author = "Mirzaei, \{Navid Mohammad\} and Navid Changizi and Alireza Asadpoure and Sumeyye Su and Dilruba Sofia and Zuzana Tatarova and Zervantonakis, \{Ioannis K.\} and Chang, \{Young Hwan\} and Leili Shahriyari",

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year = "2022",

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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

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

Abstract

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ASJC Scopus subject areas

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