title: 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."
authors.affiliation: Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, California 92093-0374, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Schultz
authors.forename: José N
authors.initials: JN
authors.lastname: Onuchic
authors.forename: Peter G
authors.initials: PG
authors.lastname: Wolynes
mesh_terms: D001703:Biophysics / Q000379:methods*; D002627:Chemistry, Physical / Q000379:methods*; D004247:DNA / Q000737:chemistry; D012321:DNA-Directed RNA Polymerases / Q000737:chemistry* / Q000235:genetics; D005786:Gene Expression Regulation; D007700:Kinetics; D008956:Models, Chemical; D008957:Models, Genetic; D015233:Models, Statistical; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013269:Stochastic Processes; D049490:Systems Biology; D014158:Transcription, Genetic
chemical_list: D011506:Proteins; D004247:DNA; D012321:DNA-Directed RNA Polymerases