  Cells exhibit a wide variety of different shapes. This diversity poses a
challenge for computational approaches that attempt to shed light on the role
cell geometry plays in regulating cell physiology and behavior. The simulation
platform Simmune is capable of embedding the computational representation of
signaling pathways into realistic models of cellular morphology. However,
Simmune's current approach to account for the cell geometry is limited to
deterministic models of reaction-diffusion processes, thus providing a
coarse-grained description that ignores stochastic local fluctuations. Here we
present an extension of Simmune that removes these limitations by employing an
alternative computational representation of cellular geometry that is smooth
and grid-free. These features make it possible to incorporate a fully
stochastic, spatially resolved description of the cellular biochemistry. The
alternative computational representation is compatible with Simmune's current
approach for specifying molecular interactions. This means that a modeler using
the approach needs to create a model of cellular biochemistry and morphology
only once to be able to use it for both, deterministic and stochastic
simulations.
