  From a systems biology perspective the majority of cancer models, although
interesting and providing a qualitative explanation of some problems, have a
major disadvantage in that they usually miss a genuine connection with
experimental data. Having this in mind, in this paper, we aim at contributing
to the improvement of many cancer models which contain a proliferation term. To
this end, we propose a new non-local model of cell proliferation. We select
data which are suitable to perform a Bayesian inference for unknown parameters
and we provide a discussion on the range of applicability of the model.
Furthermore, we provide proof of the stability of a posteriori distributions in
total variation norm which exploits the theory of spaces of measures equipped
with the weighted flat norm. In a companion paper, we provide a detailed proof
of the well-posedness of the problem and we investigate the convergence of the
EBT algorithm applied to solve the equation.
