Boolean networks provide a useful tool to address questions on the structure of large biochemical interactions since they do not require kinetic details and, in addition, a wide range of computational tools and algorithms is available to exactly compute and study the dynamical properties of these models. A recently developed method has shown that the attractors, or asymptotic behavior, of an asynchronous Boolean network can be computed at a much lower cost if the network is written as an interconnection of two smaller modules. We have applied this methodology to study the interconnection of two Boolean models to explore bacterial growth and its interactions with the cellular gene expression machinery, with a focus on growth dynamics as a function of ribosomes, RNA polymerase and other “bulk” proteins inside the cell. The discrete framework permits easier testing of different combinations of biochemical interactions, leading to hypotheses elimination and model discrimination, and thus providing useful insights for the construction of a more detailed dynamical growth model.