Both formats have formal grammars for describing similar concepts, however they are different and to my knowledge one is not a proper subset of the other. While one could do a comparison of a syntactic nature, a simple Google search of just both acronyms reveals BioNetGen 2.2: Advances in Rule-Based Modeling
In here can be found:
2.4 SBML-to-BNGL translation
SBML is a widely-used model exchange format in systems biology (Hucka et al., 2003). Models encoded in SBML
are flat, i.e., their species do not have internal structure, which
limits their utility for rule-based modeling. BioNetGen 2.2 includes
an SBML-to-BNGL translator, called Atomizer (also available as a web
tool at ratomizer.appspot.com), that can extract implicit molecular
structure from flat species (Tapia and Faeder, 2013). A full report on
Atomizer and its application to the BioModels database (Li et al.,
2010) is currently in preparation. However, Tapia and Faeder (2013)
reported that an early version of the tool could recover implicit
structure for about 60% of species in models within the database that
contain ≥20 species. Thus, Atomizer makes available a large set of
pre-existing models in a rule-based format, facilitating their
visualization (Wenskovitch et al., 2014) and extension (Chylek et al.,
2015).
While not directly related to the question this also helped in understanding more about BioNetGen
Wikipedia: Multi-state modeling of biomolecules
Biological signaling systems often rely on complexes of biological
macromolecules that can undergo several functionally significant
modifications that are mutually compatible. Thus, they can exist in a
very large number of functionally different states. Modeling such
multi-state systems poses two problems: The problem of how to describe
and specify a multi-state system (the "specification problem") and the
problem of how to use a computer to simulate the progress of the
system over time (the "computation problem"). To address the
specification problem, modelers have in recent years moved away from
explicit specification of all possible states, and towards rule-based
formalisms that allow for implicit model specification, including the
κ-calculus, BioNetGen, the Allosteric Network Compiler and others. To
tackle the computation problem, they have turned to particle-based
methods that have in many cases proved more computationally efficient
than population-based methods based on ordinary differential
equations, partial differential equations, or the Gillespie stochastic
simulation algorithm. Given current computing technology,
particle-based methods are sometimes the only possible option.
Particle-based simulators further fall into two categories:
Non-spatial simulators such as StochSim, DYNSTOC, RuleMonkey, and
NFSim and spatial simulators, including Meredys, SRSim and MCell.
Modelers can thus choose from a variety of tools; the best choice
depending on the particular problem. Development of faster and more
powerful methods is ongoing, promising the ability to simulate ever
more complex signaling processes in the future.
