Evolutionary History of Arabidopsis thaliana Aminoacyl-tRNA Synthetase Dual-Targeted Proteins

Aminoacyl-transfer RNA (tRNA) synthetases (aaRS) are key players in translation and act early in protein synthesis by mediating the attachment of amino acids to their cognate tRNA molecules. In plants, protein synthesis may occur in three subcellular compartments (cytosol, mitochondria, and chloroplasts), which requires multiple versions of the protein to be correctly delivered to its proper destination. The organellar aaRS are nuclear encoded and equipped with targeting information at the N-terminal sequence, which enables them to be specifically translocated to their final location. Most of the aaRS families present organellar proteins that are dual targeted to mitochondria and chloroplasts. Here, we examine the dual targeting behavior of aaRS from an evolutionary perspective. Our results show that Arabidopsis thaliana aaRS sequences are a result of a horizontal gene transfer event from bacteria. However, there is no evident bias indicating one single ancestor (Cyanobacteria or Proteobacteria). The dual-targeted aaRS phylogenetic relationship was characterized into two different categories (paralogs and homologs) depending on the state recovered for both dual-targeted and cytosolic proteins. Taken together, our results suggest that the dual-targeted condition is a gain-of-function derived from gene duplication. Selection may have maintained the original function in at least one of the copies as the additional copies diverged.

Three-dimensional electrical impedance, tomography: A topology optimization approach

Electrical impedance tomography is a technique to estimate the impedance distribution within a domain, based on measurements on its boundary. In other words, given the mathematical model of the domain, its geometry and boundary conditions, a nonlinear inverse problem of estimating the electric impedance distribution can be solved. Several impedance estimation algorithms have been proposed to solve this problem. In this paper, we present a three-dimensional algorithm, based on the topology optimization method, as an alternative. A sequence of linear programming problems, allowing for constraints, is solved utilizing this method. In each iteration, the finite element method provides the electric potential field within the model of the domain. An electrode model is also proposed (thus, increasing the accuracy of the finite element results). The algorithm is tested using numerically simulated data and also experimental data, and absolute resistivity values are obtained. These results, corresponding to phantoms with two different conductive materials, exhibit relatively well-defined boundaries between them, and show that this is a practical and potentially useful technique to be applied to monitor lung aeration, including the possibility of imaging a pneumothorax.

Structure optimization combining soft-core interaction functions, the diffusion equation method, and molecular dynamics

Smoothing the potential energy surface for structure optimization is a general and commonly applied strategy. We propose a combination of soft-core potential energy functions and a variation of the diffusion equation method to smooth potential energy surfaces, which is applicable to complex systems such as protein structures; The performance of the method was demonstrated by comparison with simulated annealing using the refinement of the undecapeptide Cyclosporin A as a test case. Simulations were repeated many times using different initial conditions and structures since the methods are heuristic and results are only meaningful in a statistical sense.

Genetic algorithms for continuous optimization problems - a concept of parameter-space size adjustment

The concept of parameter-space size adjustment is pn,posed in order to enable successful application of genetic algorithms to continuous optimization problems. Performance of genetic algorithms with six different combinations of selection and reproduction mechanisms, with and without parameter-space size adjustment, were severely tested on eleven multiminima test functions. An algorithm with the best performance was employed for the determination of the model parameters of the optical constants of Pt, Ni and Cr.

An optimization model for antibiotic use

There is a positive correlation between the intensity of use of a given antibiotic and the prevalence of resistant strains. The more you treat, more patients infected with resistant strains appears and, as a consequence, the higher the mortality due to the infection and the longer the hospitalization time. In contrast, the less you treat, the higher the mortality rates and the longer the hospitalization time of patients infected with sensitive strains that could be successfully treated. The hypothesis proposed in this paper is an attempt to solve such a conflict: there must be an optimum treatment intensity that minimizes both the additional mortality and hospitalization time due to the infection by both sensitive and resistant bacteria strains. In order to test this hypothesis we applied a simple mathematical model that allowed us to estimate the optimum proportion of patients to be treated in order to minimize the total number of deaths and hospitalization time due to the infection in a hospital setting. (C) 2007 Elsevier Inc. All rights reserved.