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The landscape of computational modeling in Cancer Systems Biology is diverse,offering a spectrum of models and frameworks, each with its own trade-offs andadvantages. Ideally, models are meant to be useful in refining hypotheses, tosharpen experimental procedures and, in the longer run, even for applicationsin personalized medicine. One of the greatest challenges is to balance modelrealism and detail with experimental data to eventually produce usefuldata-driven models. We contribute to this quest by developing a transparent,highly parsimonious, first principle in silico model of a growing avasculartumor. We initially formulate the physiological considerations and the specificmodel within a stochastic cell-based framework. We next formulate acorresponding mean-field model using partial differential equations which isamenable to mathematical analysis. Despite a few notable differences betweenthe two models, we are in this way able to successfully detail the impact ofall parameters in the stability of the growth process and on the eventual tumorfate of the stochastic model. This facilitates the deduction of Bayesian priorsfor a given situation, but also provides important insights into the underlyingmechanism of tumor growth and progression. Although the resulting modelframework is relatively simple and transparent, it can still reproduce the fullrange of known emergent behavior. We identify a novel model instability arisingfrom nutrient starvation and we also discuss additional insight concerningpossible model additions and the effects of those. Thanks to the framework'sflexibility, such additions can be readily included whenever the relevant databecome available.

Morphological stability for in silico models of avascular tumors