Understanding stochastic simulations of the smallest genetic networks

Abstract

Because genetic networks function with few molecules, such systems are better described by stochastic models than by macroscopic kinetics. Stochastic simulations of a self-regulating gene are compared with analytical solutions of the master equations, showing how the dynamics depends on the average number of proteins in the system, the repression strength, and the relative speed of the binding/unbinding and synthesis/degradation events. Steady-state and transient probability distributions for the toggle switch along with typical trajectories show that strongly repressed systems are better candidates for "good switches."