Cells with identical genomes often exhibit biochemically distinct phenotypic states ; stochastic switchings among them are one of the functional roles of "noise" in a genetic circuitry. To quantify the stabilities of these phenotypic states and, more importantly, the transition rates among them, we study a minimal model of gene regulation that incorporates positive feedbackwith multiple gene states. We first show that starting from a full Delbr\"uck-Gillespie-process description of the gene regulation network, two much simpler stochastic biochemical kinetic systems can be deduced, respectively, in the limit of either the stochastic gene-state switching or transcription factor copy-number fluctuation being dominant among other sources of noise. We then propose two saddle-crossing rate formulas for the simplified dynamics. They are associated with the barriers of different nonequilibrium landscape functions, whichexhibit different dependence on the switching rates of gene states. Keeping exactly the same mean-field deterministic dynamics, incorporating noise can possibly yield opposite predictions on the relative stability of the coexisted phenotypic states when the single-molecule switching between different gene states, compared to the protein copy-number fluctuation, is relatively slow or sufficiently rapid. This quantitative theory emphasizes noises from different origins with distinct characteristics as an additional complexity within gene regulation .
Multiple phenotypic states often arise in a single cell with different gene-expression states that undergo transcription regulation with positive feedback. Recent experiments have shown that at least in E. coli, the gene state switching can be neither extremely slow nor exceedingly rapid as many previous theoretical treatments assumed. Rather it is in the intermediate region which is difficult to handle mathematically.Under this condition, from a full chemical-master-equation description we derive a model in which the protein copy-number, for a given gene state, follow a deterministic mean-field description while the protein synthesis rates fluctuate due to stochastic gene-state switching . The simplified kinetics yields a nonequilibrium landscape function, which, similar to the energy function for equilibrium fluctuation, provides the leading orders of fluctuations around each phenotypic state, as well as the transition rates between the two phenotypic states. This rate formula is analogous to Kramers theory for chemical reactions. The resulting behaviors are significantly different from the two limiting cases studied previously .
R
Cells with identical genomes often exhibit biochemically distinct phenotypic states ; stochastic switchings among them are one of the functional roles of "noise" in a genetic circuitry. To quantify the stabilities of these phenotypic states and, more importantly, the transition rates among them, we study a minimal model of gene regulation that incorporates positive feedbackwith multiple gene states. We first show that starting
Multiple phenotypic states often arise in a single cell with different gene-expression states that undergo transcription regulation with positive feedback. Recent experiments have shown that at least in E. coli, the gene state switching can be neither extremely slow nor exceedingly rapid as many previous theoretical treatments assumed. Rather it is in the intermediate region which is difficult to handle mathematically.Under this condition,
R
Delbr\"uck-Gillespie-process description of the gene regulation network, two much simpler stochastic biochemical kinetic systems can be deduced, respectively, in the limit of either the
chemical-master-equation description we derive a model in which the protein copy-number, for a given gene state, follow a deterministic mean-field description while the protein synthesis rates fluctuate due to
R
or transcription factor copy-number fluctuation being dominant among other sources of noise. We then propose two saddle-crossing rate formulas for the simplified dynamics. They are associated with the barriers of different nonequilibrium landscape functions, whichexhibit different dependence on the switching rates of gene states. Keeping exactly the same mean-field deterministic dynamics, incorporating noise can possibly yield opposite predictions on the relative stability of the coexisted phenotypic states when the single-molecule switching between different gene states, compared to the protein copy-number fluctuation, is relatively slow or sufficiently rapid. This quantitative theory emphasizes noises from different origins with distinct characteristics as an additional complexity within gene regulation
. The simplified kinetics yields a nonequilibrium landscape function, which, similar to the energy function for equilibrium fluctuation, provides the leading orders of fluctuations around each phenotypic state, as well as the transition rates between the two phenotypic states. This rate formula is analogous to Kramers theory for chemical reactions. The resulting behaviors are significantly different from the two limiting cases studied previously