Type of noise defines global attractors in bistable molecular regulatory systems.
The aim of this study is to demonstrate that in molecular dynamical systems with the underlying bi- or multistability, the type of noise determines the most strongly attracting steady state or stochastic attractor. As an example we consider a simple stochastic model of autoregulatory gene with a nonlinear positive feedback, which in the deterministic approximation has two stable steady state solutions. Three types of noise are considered: transcriptional and translational - due to the small number of gene product molecules and the gene switching noise - due to gene activation and inactivation transitions. We demonstrate that the type of noise in addition to the noise magnitude dictates the allocation of probability mass between the two stable steady states. In particular, we found that when the gene switching noise dominates over the transcriptional and translational noise (which is characteristic of eukaryotes), the gene preferentially activates, while in the opposite case, when the transcriptional noise dominates (which is characteristic of prokaryotes) the gene preferentially remains inactive. Moreover, even in the zero-noise limit, when the probability mass generically concentrates in the vicinity of one of two steady states, the choice of the most strongly attracting steady state is noise type-dependent. Although the epigenetic attractors are defined with the aid of the deterministic approximation of the stochastic regulatory process, their relative attractivity is controlled by the type of noise, in addition to noise magnitude. Since noise characteristics vary during the cell cycle and development, such mode of regulation can be potentially employed by cells to switch between alternative epigenetic attractors.
Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland. jjarusz@ippt.gov.pl
Joanna
J
Jaruszewicz
Pawel J
PJ
Zuk
Tomasz
T
Lipniacki
D000818:Animals; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D009010:Monte Carlo Method; D014176:Protein Biosynthesis / Q000235:genetics*; D013269:Stochastic Processes; D014158:Transcription, Genetic; D015533:Transcriptional Activation