Identification of organophosphorus simulants for the development of next-generation detection technologies.

Organophosphorus (OP) chemical warfare agents (CWAs) represent an ongoing threat but the understandable widespread prohibition of their use places limitations on the development of technologies to counter the effects of any OP CWA release. Herein, we describe new, accessible methods for the identification of appropriate molecular simulants to mimic the hydrogen bond accepting capacity of the P[double bond, length as m-dash]O moiety, common to every member of this class of CWAs. Using the predictive methodologies developed herein, we have identified OP CWA hydrogen bond acceptor simulants for soman and sarin. It is hoped that the effective use of these physical property specific simulants will aid future countermeasure developments.

Towards the Prediction of Antimicrobial Efficacy for Hydrogen Bonded, Self-Associating Amphiphiles.

Herein we report 50 structurally related supramolecular self-associating amphiphilic (SSA) salts and related compounds. These SSAs are shown to act as antimicrobial agents, active against model Gram-positive (methicillin-resistant Staphylococcus aureus) and/or Gram-negative (Escherichia coli) bacteria of clinical interest. Through a combination of solution-state, gas-phase, solid-state and in silico measurements, we determine 14 different physicochemical parameters for each of these 50 structurally related compounds. These parameter sets are then used to identify molecular structure-physicochemical property-antimicrobial activity relationships for our model Gram-negative and Gram-positive bacteria, while simultaneously providing insight towards the elucidation of SSA mode of antimicrobial action.

In silico evolution of diauxic growth.

BACKGROUND: The glucose effect is a well known phenomenon whereby cells, when presented with two different nutrients, show a diauxic growth pattern, i.e. an episode of exponential growth followed by a lag phase of reduced growth followed by a second phase of exponential growth. Diauxic growth is usually thought of as a an adaptation to maximise biomass production in an environment offering two or more carbon sources. While diauxic growth has been studied widely both experimentally and theoretically, the hypothesis that diauxic growth is a strategy to increase overall growth has remained an unconfirmed conjecture. METHODS: Here, we present a minimal mathematical model of a bacterial nutrient uptake system and metabolism. We subject this model to artificial evolution to test under which conditions diauxic growth evolves. RESULTS: As a result, we find that, indeed, sequential uptake of nutrients emerges if there is competition for nutrients and the metabolism/uptake system is capacity limited. DISCUSSION: However, we also find that diauxic growth is a secondary effect of this system and that the speed-up of nutrient uptake is a much larger effect. Notably, this speed-up of nutrient uptake coincides with an overall reduction of efficiency. CONCLUSIONS: Our two main conclusions are: (i) Cells competing for the same nutrients evolve rapid but inefficient growth dynamics. (ii) In the deterministic models we use here no substantial lag-phase evolves. This suggests that the lag-phase is a consequence of stochastic gene expression.

Optimal parameter settings for information processing in gene regulatory networks.

Gene networks can often be interpreted as computational circuits. This article investigates the computational properties of gene regulatory networks defined in terms of the speed and the accuracy of the output of a gene network. It will be shown that there is no single optimal set of parameters, but instead, there is a trade-off between speed and accuracy. Using the trade-off it will also be shown how systems with various parameters can be ranked with respect to their computational efficiency. Numerical analysis suggests that the trade-off can be improved when the output gene is repressing itself, even though the accuracy or the speed of the auto-regulated system may be worse than the unregulated system.

Computational limits to binary genes.

We analyse the trade-off between the speed with which a gene can propagate information, the noise of its output and its metabolic cost. Our main finding is that for any given level of metabolic cost there is an optimal trade-off between noise and processing speed. Any system with a non-vanishing leak expression rate is suboptimal, i.e. it will exhibit higher noise and/or slower speed than leak-free systems with the same metabolic cost. We also show that there is an optimal Hill coefficient h which minimizes noise and metabolic cost at fixed speeds, and an optimal threshold K which minimizes noise.