  The paper presents an approach for overcoming modeling problems of typical
life science applications with partly unknown mechanisms and lacking
quantitative data: A model family of reaction diffusion equations is built up
on a mesoscopic scale and uses classes of feasible functions for reaction and
taxis terms. The classes are found by translating biological knowledge into
mathematical conditions and the analysis of the models further constrains the
classes. Numerical simulations allow comparing single models out of the model
family with available qualitative information on the solutions from
observations. The method provides insight into a hierarchical order of the
mechanisms. The method is applied to the clinics for liver inflammation such as
metabolic dysfunction-associated steatohepatitis (MASH) or viral hepatitis
where reasons for the chronification of disease are still unclear and time- and
space-dependent data is unavailable.
