Mean-field versus stochastic models for transcriptional regulation.
We introduce a minimal model description for the dynamics of transcriptional regulatory networks. It is studied within a mean-field approximation, i.e., by deterministic ODE's representing the reaction kinetics, and by stochastic simulations employing the Gillespie algorithm. We elucidate the different results that both approaches can deliver, depending on the network under study, and in particular depending on the level of detail retained in the respective description. Two examples are addressed in detail: The repressilator, a transcriptional clock based on a three-gene network realized experimentally in E. coli, and a bistable two-gene circuit under external driving, a transcriptional network motif recently proposed to play a role in cellular development.
Biological Nanosystems, Interdisciplinary Research Institute, Lille University of Science and Technology, USR 3078 CNRS, Parc Scientifique de la Haute Borne, 50, Avenue Halley, F-59658 Villeneuve d'Ascq, France.
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Blossey
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Giuraniuc
D000465:Algorithms; D020816:Amino Acid Motifs; D001703:Biophysics / Q000379:methods*; D004926:Escherichia coli / Q000378:metabolism; D005786:Gene Expression Regulation; D007700:Kinetics; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D013269:Stochastic Processes; D013816:Thermodynamics; D014158:Transcription, Genetic