Modeling the Overproduction of Ribosomes when Antibacterial Drugs Act on Cells.

Bacteria that are subjected to ribosome-inhibiting antibiotic drugs show an interesting behavior: Although the drug slows down cell growth, it also paradoxically increases the cell's concentration of ribosomes. We combine our earlier nonlinear model of the energy-biomass balance in undrugged Escherichia coli cells with Michaelis-Menten binding of drugs that inactivate ribosomes. Predictions are in good agreement with experiments on ribosomal concentrations and synthesis rates versus drug concentrations and growth rates. The model indicates that the added drug drives the cell to overproduce ribosomes, keeping roughly constant the level of ribosomes producing ribosomal proteins, an important quantity for cell growth. The model also predicts that ribosomal production rates should increase and then decrease with added drug. This model gives insights into the driving forces in cells and suggests new experiments.

Bacterial growth laws reflect the evolutionary importance of energy efficiency.

We are interested in the balance of energy and protein synthesis in bacterial growth. How has evolution optimized this balance? We describe an analytical model that leverages extensive literature data on growth laws to infer the underlying fitness landscape and to draw inferences about what evolution has optimized in Escherichia coli. Is E. coli optimized for growth speed, energy efficiency, or some other property? Experimental data show that at its replication speed limit, E. coli produces about four mass equivalents of nonribosomal proteins for every mass equivalent of ribosomes. This ratio can be explained if the cell's fitness function is the the energy efficiency of cells under fast growth conditions, indicating a tradeoff between the high energy costs of ribosomes under fast growth and the high energy costs of turning over nonribosomal proteins under slow growth. This model gives insight into some of the complex nonlinear relationships between energy utilization and ribosomal and nonribosomal production as a function of cell growth conditions.

Influence of pulling handles and device stiffness in single-molecule force spectroscopy.

In single-molecule force spectroscopy, individual molecules and complexes are often stretched by pulling devices via intervening molecular handles. Accurate interpretation of measurements from such experiments in terms of the underlying energy landscape, defined by activation barriers and intrinsic rates of transition, relies on our understanding, and proper theoretical treatment, of the effects of the pulling device and handle. Here, we present a framework based on Kramers' theory that elucidates the dependence of measured rupture forces and rates on the pulling device stiffness and attributes of the handle, contour length and persistence length. We also introduce a simple analytic model that improves prediction of activation barriers and intrinsic rates for all device stiffnesses and handle properties, thus allowing for a more reliable interpretation of experiments. Our analyses also suggests intuitive ways of displaying the measured force spectra for proper prognosis of device and handle effects and provides the range of device and handle attributes over which these effects can be neglected.

Model accounting for the effects of pulling-device stiffness in the analyses of single-molecule force measurements.

Single-molecule force spectroscopy provides a powerful approach for investigating molecular transitions along specific reaction coordinates. Here, we present a general analytical model for extracting the intrinsic rates and activation free energies from force measurements on single molecules that is applicable to a broad range of pulling speeds and device stiffnesses. This model relaxes existing limitations to perform force measurements with soft pulling devices for proper theoretical analyses and, in fact, allows experiments to specifically exploit device stiffness as a control parameter in addition to pulling speed for a reliable estimation of energetic and kinetic parameters.

A structural perspective on the where, how, why, and what of nucleosome positioning.

The DNA in eukaryotic chromatin is packed by histones into arrays of repeating units called nucleosomes. Each nucleosome contains a nucleosome core, where the DNA is wrapped around a histone octamer, and a stretch of relatively unconstrained DNA called the linker DNA. Since nucleosome cores occlude the DNA from many DNA-binding factors, their positions provide important clues for understanding chromatin packing and gene regulation. Here we review the recent advances in the genome-wide mapping of nucleosome positions, the molecular and structural determinants of nucleosome positioning, and the importance of nucleosome positioning in chromatin higher order folding and transcriptional regulation.

Understanding correlation effects for ion conduction in polymer electrolytes.

Polymer electrolytes typically exhibit diminished ionic conductivity due to the presence of correlation effects between the cations and anions. Microscopically, transient ionic aggregates, e.g., ion-pairs, ion-triplets, or higher order ionic clusters, engender ionic correlations. Employing all-atom simulation of a model polymer electrolyte comprising of poly(ethylene oxide) and lithium iodide, the ionic correlations are explored through construction of elementary functions between pairs of the ionic species that qualitatively explains the spatio-temporal nature of these correlations. Furthermore, commencing from the exact Einstein-like equation describing the collective diffusivity of the ions in terms of the average diffusivity of the ions (i.e., the self-terms) and the correlations from distinct pairs of ions, several phenomenological parameters are introduced to keep track of the simplification procedure that finally boils down to the recently proposed phenomenological model by Stolwijk and Obeidi (SO) [Stolwijk, N. A.; Obeidi, S. Phys. Rev. Lett. 2004, 93, 125901]. The approximation parameters, which can be retrieved from simulations, point to the necessity of additional information in order to fully describe the correlation effects apart from the mere fraction of ion-pairs that apparently accounts for the correlations originating from only the nearest neighbor structural correlations. These parameters are close to, but are not exactly unity, as assumed in the SO model. Finally, as an application of the extended SO model, one is able to estimate the dynamics of the free and non-free ions as well as their fractions from the knowledge of the single particle diffusivities and the collective diffusivity of the ions.