Adaptation Dynamics in Densely Clustered Chemoreceptors
Figure 4
Large fluctuations arise from the saturated kinetics of localized enzymes.
(A) Variance of receptor activity σaa at steady state is significantly larger for the analytical model with localization (black) than without localization (gray; analytical version of model B1) for all values of total CheR RTot. The analytical model with localization (inset, black) exhibits signaling noise with σa/a0 up to ∼7% while noise in the model with no localization (analytical version of B1) remains at or below 3% of the mean output (inset, gray). (B) Mean receptor activity a0 at steady state as a function of CheR to CheB ratio. When plotted as a function of the total CheR to total CheB ratio, a0 exhibits a similar relatively robust profile for both the analytical model with localization (black) and without localization (gray; analytical version of B1). In contrast the mean receptor activity is ultrasensitive to the ratio of the localized CheR to localized CheB-P counts (gray, dot-dashed), . (Inset) Variance in receptor activity σaa (black, solid) decomposed into components due to fluctuation in localized CheR (black, dashed), localized CheB (gray, dashed), and small intrinsic fluctuations in the methylation rates (gray, dot-dashed) as in Eq. (11). All quantities are plotted as functions of relative RTot. (C) In the stochastic simulation of M1, steady-state activity a0 also has ultrasensitive dependence on the ratio of tethered CheR/CheB-P (gray), despite the weak dependence on total CheR/CheB (black). (Inset) 500 s simulation trace of instantaneous mean receptor activity a(t) (black) and instantaneous localized CheR/CheB-P (gray), smoothed with a 30 s sliding window average. (D) Comparison of the dependence of a0 on localized CheR/CheB-P for the simulated models M1 (black), M2 (light gray), and M3 (dark gray) from Fig. 2. This dependence is significantly weaker for the more processive models.