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

Area of Science:

• Computational biology
• Cancer research
• Mathematical modeling

Background:

• Computational modeling aids in understanding complex cancer dynamics.
• Advancements in cancer databases and data analysis enhance model robustness.
• Mathematical models explore cancer from sub-cellular to tissue scales, covering treatment and diagnostics.

Purpose of the Study:

• To provide a step-by-step methodology for a data-driven mechanistic model of the tumor microenvironment.
• To discuss essential components of model development, including data acquisition and parameter estimation.
• To propose an extension of ordinary differential equation models to partial differential equation models coupled with mechanical growth.

Main Methods:

• Data acquisition strategies and preparation.
• Parameter estimation techniques.
• Sensitivity analysis.
• Extension of mechanistic ordinary differential equation models to PDE models coupled with mechanical growth.

Main Results:

• A comprehensive workflow for developing data-driven mechanistic models of the tumor microenvironment.
• Methods for understanding temporal and spatial interactions between cells and cytokines.
• Insights into the effects of these interactions on tumor growth.

Conclusions:

• The proposed workflow facilitates a deeper understanding of tumor microenvironment dynamics.
• Mechanistic modeling, including extensions to PDE models, is crucial for advancing cancer research.
• This approach aids in comprehending complex cell-cytokine interactions and their impact on tumor progression.