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Recent experiments have shown that drug efflux pump deficiency not only increases the susceptibility of pathogens to antibiotics, but also seems to "mask" the effects of mutations, that decrease the affinities of drugs to their intracellular targets, on the growth rates of drug-exposed bacteria. That is, in the presence of drugs, the growth rates of drug-exposed WT and target mutated strains are the same in a drug efflux pump deficient background, but the mutants grow faster than WT in a drug efflux pump proficient background. Here, we explain the mechanism of target resistance masking and show that it occurs in response to drug efflux pump inhibition among pathogens with high-affinity drug binding targets, low cell-membrane drug-permeability and insignificant intracellular drug degradation. We demonstrate that target resistance masking is fundamentally linked to growth-bistability, i.e., the existence of 2 different steady state growth rates for one and the same drug concentration in the growth medium. We speculate that target resistance masking provides a hitherto unknown mechanism for slowing down the evolution of target resistance among pathogens.

The spatiotemporal oscillations of the Escherichia coli proteins MinD and MinE direct cell division to the region between the chromosomes. Several quantitative models of the Min system have been suggested before, but no one of them accounts for the behavior of all documented mutant phenotypes. We analyzed the stochastic reaction-diffusion kinetics of the Min proteins for several E. coli mutants and compared the results to the corresponding deterministic mean-field description. We found that wild-type (wt) and filamentous (ftsZ(-)) cells are well characterized by the mean-field model, but that a stochastic model is necessary to account for several of the characteristics of the spherical (rodA(-)) and phospathedylethanolamide-deficient (PE-) phenotypes. For spherical cells, the mean-field model is bistable, and the system can get trapped in a non-oscillatory state. However, when the intrinsic noise is considered, only the experimentally observed oscillatory behavior remains. The stochastic model also reproduces the change in oscillation directions observed in the spherical phenotype and the occasional gliding of the MinD region along the inner membrane. For the PE- mutant, the stochastic model explains the appearance of randomly localized and dense MinD clusters as a nucleation phenomenon, in which the stochastic kinetics at low copy number causes local discharges of the high MinD(ATP) to MinD(ADP) potential. We find that a simple five-reaction model of the Min system can explain all documented Min phenotypes, if stochastic kinetics and three-dimensional diffusion are accounted for. Our results emphasize that local copy number fluctuation may result in phenotypic differences although the total number of molecules of the relevant species is high.

Quantitative analysis of biochemical networks often requires consideration of both spatial and stochastic aspects of chemical processes. Despite significant progress in the field, it is still computationally prohibitive to simulate systems involving many reactants or complex geometries using a microscopic framework that includes the finest length and time scales of diffusion-limited molecular interactions. For this reason, spatially or temporally discretized simulations schemes are commonly used when modeling intracellular reaction networks. The challenge in defining such coarse-grained models is to calculate the correct probabilities of reaction given the microscopic parameters and the uncertainty in the molecular positions introduced by the spatial or temporal discretization. In this paper we have solved this problem for the spatially discretized Reaction-Diffusion Master Equation; this enables a seamless and physically consistent transition from the microscopic to the macroscopic frameworks of reaction-diffusion kinetics. We exemplify the use of the methods by showing that a phosphorylation-dephosphorylation motif, commonly observed in eukaryotic signaling pathways, is predicted to display fluctuations that depend on the geometry of the system.

The value of the rRNA chain elongation rate in bacteria is an important physiological parameter, as it affects not only the rRNA promoter activity but also the free-RNA polymerase concentration and thereby the transcription of all genes. On average, rRNA chains elongate at a rate of 80 to 90 nucleotides (nt) per s, and the transcription of an entire rrn operon takes about 60 s (at 37 degrees C). Here we have analyzed a reported distribution obtained from electron micrographs of RNA polymerase molecules along rrn operons in E. coli growing at 2.5 doublings per hour (S. Quan, N. Zhang, S. French, and C. L. Squires, J. Bacteriol. 187:1632-1638, 2005). The distribution exhibits two peaks of higher polymerase density centered within the 16S and 23S rRNA genes. An evaluation of this distribution indicates that RNA polymerase transcribes the 5' leader region at speeds up to or greater than 250 nt/s. Once past the leader, transcription slows down to about 65 nt/s within the 16S gene, speeds up in the spacer region between the 16S and 23S genes, slows again to about 65 nt/s in the 23S region, and finally speeds up to a rate greater than 400 nt/s near the end of the operon. We suggest that the slowing of transcript elongation in the 16S and 23S sections is the result of transcriptional pauses, possibly caused by temporary interactions of the RNA polymerase with secondary structures in the nascent rRNA.