Abstract
It is well known that stochasticity in gene expression is an important source of noise that can have profound effects on the fate of a living cell. In the galactose genetic switch in yeast, the unbinding of a transcription repressor is induced by high concentrations of sugar particles activating gene expression of sugar transporters. This response results in high propensity for all reactions involving interactions with the metabolite. The reactions for gene expression, feedback loops and transport are typically described by chemical master equations (CME). Sampling the CME using the stochastic simulation algorithm (SSA) results in large computational costs as each reaction event is evaluated explicitly. To improve the computational efficiency of cell simulations involving high particle number systems, the authors have implemented a hybrid stochastic-deterministic (CME-ODE) method into the publically available, GPU-based lattice microbes (LM) software suite and its python interface pyLM. LM and pyLM provide a convenient way to simulate complex cellular systems and interface with high-performance RDME/CME/ODE solvers. As a test of the implementation, the authors apply the hybrid CME-ODE method to the galactose switch in Saccharomyces cerevisiae, gaining a 10-50× speedup while yielding protein distributions and species traces similar to the pure SSA CME.