  The landscape of computational modeling in cancer systems biology is diverse,
offering a spectrum of models and frameworks, each with its own trade-offs and
advantages. Ideally, models are meant to be useful in refining hypotheses, to
sharpen experimental procedures and, in the longer run, even for applications
in personalized medicine. One of the greatest challenges is to balance model
realism and detail with experimental data to eventually produce useful
data-driven models. We contribute to this quest by developing a transparent,
highly parsimonious, first principles silico model of a growing avascular
tumor. We initially formulate the physiological considerations and the specific
model within a stochastic cell-based framework. We next formulate a
corresponding mean-field model using partial differential equations which is
amenable to mathematical analysis. Despite a few notable differences between
the two models, we are in this way able to successfully detail the impact of
all parameters in the stability of the growth process and on the eventual tumor
fate of the stochastic model. This facilitates the deduction of Bayesian priors
for a given situation, but also provides important insights into the underlying
mechanism of tumor growth and progression. Although the resulting model
framework is relatively simple and transparent, it can still reproduce the full
range of known emergent behavior. We identify a novel model instability arising
from nutrient starvation and we also discuss additional insight concerning
possible model additions and the effects of those. Thanks to the framework's
flexibility, such additions can be readily included whenever the relevant data
become available.
