Skip to main content
Figure 4 | BMC Systems Biology

Figure 4

From: On the spontaneous stochastic dynamics of a single gene: complexity of the molecular interplay at the promoter

Figure 4

Complexity of the steady-state. (A) This prokaryotic-like example corresponds to an energy-independent promoter regulated by two TF molecules (A and C) and the looping of DNA with typical parameters from the literature. A binds cooperatively at its two binding sites (ΔGA-A= -2 kcal/mol) and competitively with C at one of its sites (ΔGA-C= 1.5 kcal/mol) [68, 79]. The energetic cost of DNA looping (typically between 8 and 10 kcal/mol) is ΔGloop = 9 kcal/mol and is overcompensated by the interaction energy with two TFs of type A that maintain the loop (ΔGloop-A= -5.5 kcal/mol for each site) [67, 93, 94]. The closed state of DNA looping slows down the association/dissociation of C (ΔEloop-C= 2.5 kcal/mol). Bimolecular TF-DNA residence times were taken in the shorter range reported by [90] (1/ = 20 s at both sites and 1/ = 60 s) and the time for DNA to loop when both sites of A are occupied is very fast 1/kclose = 1 s [93]. Concentration ranges ([10-2; 103] nM) and equilibrium constants ( = 20 nM and = 1 nM) were set to physiological values [79, 89]. Transcription is promoted by the unlooped state, the presence of C and slightly by the presence of A at one site (see table S3 of Additional file 1 for details). RNA life-time (5 min) and abundance (between 10 and 70 copies per cell) were chosen as reported by [84, 91]. (B) Exploration of the system's behavior as a function of concentrations [A] and [C] is presented in terms of mean RNA level (B1), normalized variance (B2) and distribution (C) (represented along an arbitrary path of interest because of a too large dimensionality). (D) Changing the energies of activation E0 by adding a normally distributed energy (s.d. = 3 kcal/mol) to both direction of each reaction while keeping state energies G0 unchanged does not influence the mean behavior of expression but has a profound impact on its variability. This shows that mean expression can hide most of the complexity of regulation and that stochastic aspects can reveal much kinetic information.

Back to article page