Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.

BACKGROUND:The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS:Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION:We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone

Three months journeying of a Hawaiian monk seal

Hawaiian monk seals (Monachus schauinslandi) are endemic to the Hawaiian Islands and are the most endangered species of marine mammal that lives entirely within the jurisdiction of the United States. The species numbers around 1300 and has been declining owing, among other things, to poor juvenile survival which is evidently related to poor foraging success. Consequently, data have been collected recently on the foraging habitats, movements, and behaviors of monk seals throughout the Northwestern and main Hawaiian Islands. Our work here is directed to exploring a data set located in a relatively shallow offshore submerged bank (Penguin Bank) in our search of a model for a seal's journey. The work ends by fitting a stochastic differential equation (SDE) that mimics some aspects of the behavior of seals by working with location data collected for one seal. The SDE is found by developing a time varying potential function with two points of attraction. The times of location are irregularly spaced and not close together geographically, leading to some difficulties of interpretation. Synthetic plots generated using the model are employed to assess its reasonableness spatially and temporally. One aspect is that the animal stays mainly southwest of Molokai. The work led to the estimation of the lengths and locations of the seal's foraging trips.Comment: Published in at http://dx.doi.org/10.1214/193940307000000473 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Invisible design: exploring insights and ideas through ambiguous film scenarios

Invisible Design is a technique for generating insights and ideas with workshop participants in the early stages of concept development. It involves the creation of ambiguous films in which characters discuss a technology that is not directly shown. The technique builds on previous work in HCI on scenarios, persona, theatre, film and ambiguity. The Invisible Design approach is illustrated with three examples from unrelated projects; Biometric Daemon, Panini and Smart Money. The paper presents a qualitative analysis of data from a series of workshops where these Invisible Designs were discussed. The analysis outlines responses to the films in terms of; existing problems, concerns with imagined technologies and design speculation. It is argued that Invisible Design can help to create a space for critical and creative dialogue during participatory concept development

Beta particle energy spectra shift due to self-attenuation effects in environmental sources

In order to predict and control the environmental and health impacts of ionising radiation in environmental sources such as groundwater, it is necessary to identify the radionuclides present. Beta-emitting radionuclides are frequently identified by measuring their characteristic energy spectra. The present work shows that self-attenuation effects from volume sources result in a geometry-dependent shift in the characteristic spectra which needs to be taken into account in order to correctly identify the radionuclides present. These effects are shown to be compounded due to the subsequent shift in the photon spectra produced by the detector, in this case an inorganic solid scintillator (CaF2:Eu) monitored using a Silicon Photomultiplier (SiPM). Using tritiated water as an environmentally relevant, and notoriously difficult to monitor case study, analytical predictions for the shift in the energy spectra as a function of depth of source have been derived. These predictions have been validated using Geant4 simulations and experimental results measured using bespoke instrumentation

Matlab application for fitting progress curves to the Equilibrium Model

The general procedures for carrying out the necessary rate determinations required for accurate determination of the Equilibrium Model parameters, and fitting this data to the mathematical model to generate the parameters, are described in "Peterson, M.E., Daniel, R.M., Danson, M.J. & Eisenthal, R. (2007) The dependence of enzyme activity on temperature: determination and validation of parameters. Biochemical Journal, 402, 331-337". It should be borne in mind that the Equilibrium Model equation contains exponentials of exponentials – quite small deviations from ideal behaviour, or a failure to obtain true Vmax values, may lead to difficulty in obtaining reliable Equilibrium Model parameters

Lyman-alpha absorption around nearby galaxies

We have used STIS aboard HST to search for Lyman-alpha (Lya) absorption lines in the outer regions of eight nearby galaxies using background QSOs and AGN as probes. Lya lines are detected within a few hundred km/s of the systemic velocity of the galaxy in all cases. We conclude that a background line-of-sight which passes within 26-200 h-1 kpc of a foreground galaxy is likely to intercept low column density neutral hydrogen with log N(HI) >~ 13.0. The ubiquity of detections implies a covering factor of ~ 100% for low N(HI) gas around galaxies within 200 h-1 kpc. We discuss the difficulty in trying to associate individual absorption components with the selected galaxies and their neighbors, but show that by degrading our STIS data to lower resolutions, we are able to reproduce the anti-correlation of Lya equivalent width and impact parameter found at higher redshift. We also show that the equivalent width and column density of Lya complexes (when individual components are summed over ~ 1000 km/s) correlate well with a simple estimate of the volume density of galaxies brighter than M(B) = -17.5 at the same redshift as a Lya complex. We do not reject the hypothesis that the selected galaxies are directly responsible for the observed Lya lines, but our analysis indicates that absorption by clumpy intragroup gas is an equally likely explanation. (Abriged)Comment: Accepted for publication in Nov 20, 2002 issue of ApJ. Paper with all figures can be found at http://www.astro.princeton.edu/~dvb/lyapaper.ps (preferable). Minor typos fixe

Identifying dynamical modules from genetic regulatory systems: applications to the segment polarity network

BACKGROUND It is widely accepted that genetic regulatory systems are 'modular', in that the whole system is made up of smaller 'subsystems' corresponding to specific biological functions. Most attempts to identify modules in genetic regulatory systems have relied on the topology of the underlying network. However, it is the temporal activity (dynamics) of genes and proteins that corresponds to biological functions, and hence it is dynamics that we focus on here for identifying subsystems. RESULTS Using Boolean network models as an exemplar, we present a new technique to identify subsystems, based on their dynamical properties. The main part of the method depends only on the stable dynamics (attractors) of the system, thus requiring no prior knowledge of the underlying network. However, knowledge of the logical relationships between the network components can be used to describe how each subsystem is regulated. To demonstrate its applicability to genetic regulatory systems, we apply the method to a model of the Drosophila segment polarity network, providing a detailed breakdown of the system. CONCLUSION We have designed a technique for decomposing any set of discrete-state, discrete-time attractors into subsystems. Having a suitable mathematical model also allows us to describe how each subsystem is regulated and how robust each subsystem is against perturbations. However, since the subsystems are found directly from the attractors, a mathematical model or underlying network topology is not necessarily required to identify them, potentially allowing the method to be applied directly to experimental expression data

Feedback control-based inverse kinematics solvers for a nuclear decommissioning robot

The article develops two novel feedback control-based Inverse Kinematics (IK) solvers. They are evaluated for a dual-manipulator mobile robotic system with application to nuclear decommissioning. The first algorithm has similarities to other feedback control based solvers, and borrows ideas from the Cyclic Coordinate Decent and the Jacobian Transpose methods. This yields a particularly straightforward algorithm with tunable Proportional-Integral-Derivative (PID) gains to determine performance. The second approach utilises a discrete-time state space modelling framework to solve the IK problem. Although the second solver is more complex to implement, preliminary simulation results for the case study example, show that it can converge quicker, and has improved immunity to the kinematic singularities that can occur in Jacobian based methods

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens