Modeling cancer progression: an integrated workflow extending data-driven kinetic models to bio-mechanical PDE models

Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There are many mathematical models which investigate can...

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Bibliographic Details
Published in:Physical biology 2024-03, Vol.21 (2), p.22001
Main Authors: Mohammad Mirzaei, Navid, Shahriyari, Leili
Format: Article
Language:English
Subjects:
data-driven model
gene expression
ordinary differential equation
parameter estimation
partial differential equation
sensitivity analysis
tumor microenvironment
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Summary:Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There are many mathematical models which investigate cancer through different approaches, from sub-cellular to tissue scale, and from treatment to diagnostic points of view. In this study, we lay out a step-by-step methodology for a data-driven mechanistic model of the tumor microenvironment. We discuss data acquisition strategies, data preparation, parameter estimation, and sensitivity analysis techniques. Furthermore, we propose a possible approach to extend mechanistic ODE models to PDE models coupled with mechanical growth. The workflow discussed in this article can help understand the complex temporal and spatial interactions between cells and cytokines in the tumor microenvironment and their effect on tumor growth.
ISSN:1478-3975
1478-3967
1478-3975
DOI:10.1088/1478-3975/ad2777