  As a concrete setting where stochastic partial differential equations (SPDEs)
are able to model real phenomena, we propose a stochastic Meinhardt model for
cell repolarisation and study how parameter estimation techniques developed for
simple linear SPDE models apply in this situation. We establish the existence
of mild SPDE solutions and we investigate the impact of the driving noise
process on pattern formation in the solution. We then pursue estimation of the
diffusion term and show asymptotic normality for our estimator as the space
resolution becomes finer. The finite sample performance is investigated for
synthetic and real data.
