Copper homeostasis in Salmonella is atypical and copper-CueP is a major periplasmic metal complex.

Salmonella enterica sv. typhimurium (S. enterica sv. Typhimurium) has two metal-transporting P(1)-type ATPases whose actions largely overlap with respect to growth in elevated copper. Mutants lacking both ATPases over-accumulate copper relative to wild-type or either single mutant. Such duplication of ATPases is unusual in bacterial copper tolerance. Both ATPases are under the control of MerR family metal-responsive transcriptional activators. Analyses of periplasmic copper complexes identified copper-CueP as one of the predominant metal pools. Expression of cueP was recently shown to be controlled by the same metal-responsive activator as one of the P(1)-type ATPase genes (copA), and copper-CueP is a further atypical feature of copper homeostasis in S. enterica sv. Typhimurium. Elevated copper is detected by a reporter construct driven by the promoter of copA in wild-type S. enterica sv. Typhimurium during infection of macrophages. Double mutants missing both ATPases also show reduced survival inside cultured macrophages. It is hypothesized that elevated copper within macrophages may have selected for specialized copper-resistance systems in pathogenic microorganism such as S. enterica sv. Typhimurium.

Simulation in user-centred design: Helping designers to empathise with atypical users

Time and budget constraints frequently prevent designers from consulting with end-users while assessing the ease of use of the products they create. This has resulted in solutions that are difficult to use by a wide range of users, especially the growing older adult population and people with different types of impairments. To help designers with this problem, capability-loss simulators have been developed with the aim of temporarily representing users who are otherwise difficult to access. This paper questions the reliability of existing tools in providing designers with meaningful information about the users' capabilities. Consequently, a new capability-loss simulation toolkit is presented, followed by its empirical evaluation. The new toolkit proved to be significantly helpful for a group of designers identifying real usability problems with everyday devices. © 2012 Copyright Taylor and Francis Group, LLC.

Optimization algorithms and ODE's in MDO

There is a need for a stronger theoretical understanding of Multidisciplinary Design Optimization (MDO) within the field. Having developed a differential geometry framework in response to this need, we consider how standard optimization algorithms can be modeled using systems of ordinary differential equations (ODEs) while also reviewing optimization algorithms which have been derived from ODE solution methods. We then use some of the framework's tools to show how our resultant systems of ODEs can be analyzed and their behaviour quantitatively evaluated. In doing so, we demonstrate the power and scope of our differential geometry framework, we provide new tools for analyzing MDO systems and their behaviour, and we suggest hitherto neglected optimization methods which may prove particularly useful within the MDO context. Copyright © 2013 by ASME.

Reconstruction of arbitrary biochemical reaction networks: A compressive sensing approach

Reconstruction of biochemical reaction networks (BRN) and genetic regulatory networks (GRN) in particular is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Differential Equations (ODEs) that involve polynomial and rational functions are typically used to model biochemical reaction networks. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data quite difficult. In this paper, we present a network reconstruction algorithm that can deal with ODE model descriptions containing polynomial and rational functions. Rather than identifying the parameters of linear or nonlinear ODEs characterised by pre-defined equation structures, our methodology allows us to determine the nonlinear ODEs structure together with their associated parameters. To solve the network reconstruction problem, we cast it as a compressive sensing (CS) problem and use sparse Bayesian learning (SBL) algorithms as a computationally efficient and robust way to obtain its solution. © 2012 IEEE.