Spatial phenomena attract increasingly interest in computational
biology. Molecular crowding, i.e. a dense population
of macromolecules, is known to have a significant impact
on the kinetics of molecules. However, an in-detail inspection
of cell behavior in time and space is extremely costly.
To balance between cost and accuracy, multi-resolution approaches
offer one solution. Particularly, a combination of
individual and lattice-population based algorithms promise
an adequate treatment of phenomena like macromolecular
crowding. In realizing such an approach, central questions
are how to specify and synchronize the interaction between
population and individual spatial level, and to decide what
is best treated at a specific level, respectively. Based on an
algorithm which combines the Next Subvolume Method and
a simple, individual-based spatial approach, we will present
possible answers to these questions, and will discuss first
experimental results.