title: Roles of factorial noise in inducing bimodal gene expression.
abstract: Some gene regulatory systems can exhibit bimodal distributions of mRNA or protein although the deterministic counterparts are monostable. This noise-induced bimodality is an interesting phenomenon and has important biological implications, but it is unclear how different sources of expression noise (each source creates so-called factorial noise that is defined as a component of the total noise) contribute separately to this stochastic bimodality. Here we consider a minimal model of gene regulation, which is monostable in the deterministic case. Although simple, this system contains factorial noise of two main kinds: promoter noise due to switching between gene states and transcriptional (or translational) noise due to synthesis and degradation of mRNA (or protein). To better trace the roles of factorial noise in inducing bimodality, we also analyze two limit models, continuous and adiabatic approximations, apart from the exact model. We show that in the case of slow gene switching, the continuous model where only promoter noise is considered can exhibit bimodality; in the case of fast switching, the adiabatic model where only transcriptional or translational noise is considered can also exhibit bimodality but the exact model cannot; and in other cases, both promoter noise and transcriptional or translational noise can cooperatively induce bimodality. Since slow gene switching and large protein copy numbers are characteristics of eukaryotic cells, whereas fast gene switching and small protein copy numbers are characteristics of prokaryotic cells, we infer that eukaryotic stochastic bimodality is induced mainly by promoter noise, whereas prokaryotic stochastic bimodality is induced primarily by transcriptional or translational noise.
authors.affiliation: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Peijiang
authors.initials: P
authors.lastname: Liu
authors.affiliation: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Zhanjiang
authors.initials: Z
authors.lastname: Yuan
authors.affiliation: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Lifang
authors.initials: L
authors.lastname: Huang
authors.affiliation: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Tianshou
authors.initials: T
authors.lastname: Zhou
mesh_terms: D005057:Eukaryotic Cells / Q000378:metabolism; D005786:Gene Expression Regulation; D007700:Kinetics; D008957:Models, Genetic; D011401:Promoter Regions, Genetic / Q000235:genetics; D014176:Protein Biosynthesis; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D013269:Stochastic Processes
chemical_list: D012333:RNA, Messenger