  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.
