A common repressor pool results in indeterminacy of extrinsic noise - PubMed (original) (raw)

A common repressor pool results in indeterminacy of extrinsic noise

Michail Stamatakis et al. Chaos. 2011 Dec.

Abstract

For just over a decade, stochastic gene expression has been the focus of many experimental and theoretical studies. It is now widely accepted that noise in gene expression can be decomposed into extrinsic and intrinsic components, which have orthogonal contributions to the total noise. Intrinsic noise stems from the random occurrence of biochemical reactions and is inherent to gene expression. Extrinsic noise originates from fluctuations in the concentrations of regulatory components or random transitions in the cell's state and is imposed to the gene of interest by the intra- and extra-cellular environment. The basic assumption has been that extrinsic noise acts as a pure input on the gene of interest, which exerts no feedback on the extrinsic noise source. Thus, multiple copies of a gene would be uniformly influenced by an extrinsic noise source. Here, we report that this assumption falls short when multiple genes share a common pool of a regulatory molecule. Due to the competitive utilization of the molecules existing in this pool, genes are no longer uniformly influenced by the extrinsic noise source. Rather, they exert negative regulation on each other and thus extrinsic noise cannot be determined by the currently established method.

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Figures

Figure 1

A gene could be viewed as a noisy machine that accepts a random signal as input (extrinsic noise E) and transforms it through an inherently stochastic process (intrinsic noise, I), thereby generating a noisy output signal (f(E,I)). Consequently, the latter contains two orthogonal noise components: extrinsic, due to the input noise, and intrinsic due to the noise produced within the “machine.” In our discussion, the output is the protein expression level: f(E,I) = P(E,I).

Figure 2

Schematic representation of the interactions taken into account in the two promoter-reporter system. For species notation see Table Table I..

Figure 3

Comparison of the analytical

(ɛ) approximation for the stationary probability P⌢0s(LacT) with simulation results. Panel (a): plot of Eq. 38 and of the stationary LacT probability estimate from samples obtained from Gillespie simulations. Panel (b): sampling error falls with the inverse square root of the sample size.

Figure 4

(Color) Scatter plots of the Yfp versus Cfp concentration assuming constant repressor content (kLac = 0 nM/min, λLac = 0 min−1). Panel (a): total LacI content equal to 1 molecule; parameter values: kr = 10 (nM·min)−1, k−r = 1 min−1, km = 10 min−1, kp = 10 min−1, λm = 0.4 min−1, λp = 0.1 min−1. Panel (b): total LacI content equal to 4 molecules; parameter values: kr = 50 (nM·min)−1, k−r = 1 min−1, km = 10 min−1, kp = 1000 min−1, λm = 0.4 min−1, λp = 0.1 min−1. Colors indicate relative density of points on the Yfp-Cfp plane: warmer colors correspond to higher densities.

Figure 5

Plots of the intrinsic (panel a) and extrinsic (panel b) noise for given q = LacT concentration as calculated from Eqs. 42, 43.

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A common repressor pool results in indeterminacy of extrinsic noise - PubMed (original) (raw)