Stochastic switching in gene networks can occur by a single-molecule event or many molecular steps.
Due to regulatory feedback, biological networks can exist stably in multiple states, leading to heterogeneous phenotypes among genetically identical cells. Random fluctuations in protein numbers, tuned by specific molecular mechanisms, have been hypothesized to drive transitions between these different states. We develop a minimal theoretical framework to analyze the limits of switching in terms of simple experimental parameters. Our model identifies and distinguishes between two distinct molecular mechanisms for generating stochastic switches. In one class of switches, the stochasticity of a single-molecule event, a specific and rare molecular reaction, directly controls the macroscopic change in a cell's state. In the second class, no individual molecular event is significant, and stochasticity arises from the propagation of biochemical noise through many molecular pathways and steps. As an example, we explore switches based on protein-DNA binding fluctuations and predict relations between transcription factor kinetics, absolute switching rate, robustness, and efficiency that differentiate between switching by single-molecule events or many molecular steps. Finally, we apply our methods to recent experimental data on switching in Escherichia coli lactose metabolism, providing quantitative interpretations of a single-molecule switching mechanism.
Choi
Paul J
PJ
Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA.
Xie
X Sunney
XS
Shakhnovich
Eugene I
EI
D004926:Escherichia coli / Q000235:genetics*; D025461:Feedback, Physiological; D015964:Gene Expression Regulation, Bacterial; D053263:Gene Regulatory Networks / Q000235:genetics*; D007700:Kinetics; D007763:Lac Operon / Q000235:genetics; D008957:Models, Genetic; D014176:Protein Biosynthesis; D013269:Stochastic Processes; D014157:Transcription Factors / Q000378:metabolism; D014158:Transcription, Genetic
D014157:Transcription Factors