  During the last decades, medical observations and multiscale data concerning
tumor growth are mounting. At the same time, contemporary imaging techniques
well established in clinical practice, provide a variety of information on
real-time, in-vivo tumor growth. Mathematical and in-silico modeling has been
widely recruited to provide means for further understanding of pertinent
biological phenomena. However, despite the vast amounts of new evidence
compiled by medical doctors, there are still many aspects of tumor growth that
remain largely unknown. There is still a large variety of mechanisms to be
better understood and therefore, many hypotheses to be tested. To approach this
problem, starting from mathematical elaborations, we have developed a model of
the early phases of tumor growth consisting of several algorithmic modules,
each one corresponding to a particular biological mechanism. The modularity of
the model allows keeping track of the assumptions made in each step and
facilitates re-adjustment, in case new hypotheses need to be considered.
Simulations showed good qualitative agreement with biological observations, and
revealed a non-trivial interplay between between oxygen requirements of cancer
cells and their maximum mitosis rates. The proposed model has, at least in
principle, the potential to exploit data from contemporary imaging techniques
and is eligible for utilizing multicore computation.
