  Spatial heterogeneity can have dramatic effects on the biochemical networks
that drive cell regulation and decision-making. For this reason, a number of
methods have been developed to model spatial heterogeneity and incorporated
into widely used modeling platforms. Unfortunately, the standard approaches for
specifying and simulating chemical reaction networks become untenable when
dealing with multi-state, multi-component systems that are characterized by
combinatorial complexity. To address this issue, we developed MCell-R, a
framework that extends the particle-based spatial Monte Carlo simulator, MCell,
with the rule-based model specification and simulation capabilities provided by
BioNetGen and NFsim. The BioNetGen syntax enables the specification of
biomolecules as structured objects whose components can have different internal
states that represent such features as covalent modification and conformation
and which can bind components of other molecules to form molecular complexes.
The network-free simulation algorithm used by NFsim enables efficient
simulation of rule-based models even when the size of the network implied by
the biochemical rules is too large to enumerate explicitly, which frequently
occurs in detailed models of biochemical signaling. The result is a framework
that can efficiently simulate systems characterized by combinatorial complexity
at the level of spatially-resolved individual molecules over biologically
relevant time and length scales.
