University of Seville

About: University of Seville is a education organization based out in Seville, Andalucía, Spain. It is known for research contribution in the topics: Population & Model predictive control. The organization has 20098 authors who have published 47317 publications receiving 947007 citations. The organization is also known as: Universidad de Sevilla.

A complete set of nascent transcription rates for yeast genes.

Abstract: The amount of mRNA in a cell is the result of two opposite reactions: transcription and mRNA degradation. These reactions are governed by kinetics laws, and the most regulated step for many genes is the transcription rate. The transcription rate, which is assumed to be exercised mainly at the RNA polymerase recruitment level, can be calculated using the RNA polymerase densities determined either by run-on or immunoprecipitation using specific antibodies. The yeast Saccharomyces cerevisiae is the ideal model organism to generate a complete set of nascent transcription rates that will prove useful for many gene regulation studies. By combining genomic data from both the GRO (Genomic Run-on) and the RNA pol ChIP-on-chip methods we generated a new, more accurate nascent transcription rate dataset. By comparing this dataset with the indirect ones obtained from the mRNA stabilities and mRNA amount datasets, we are able to obtain biological information about posttranscriptional regulation processes and a genomic snapshot of the location of the active transcriptional machinery. We have obtained nascent transcription rates for 4,670 yeast genes. The median RNA polymerase II density in the genes is 0.078 molecules/kb, which corresponds to an average of 0.096 molecules/gene. Most genes have transcription rates of between 2 and 30 mRNAs/hour and less than 1% of yeast genes have >1 RNA polymerase molecule/gene. Histone and ribosomal protein genes are the highest transcribed groups of genes and other than these exceptions the transcription of genes is an infrequent phenomenon in a yeast cell.

Salt stress enhances xylem development and expression of S -adenosyl- l -methionine synthase in lignifying tissues of tomato plants

Abstract: S-Adenosyl-l-methionine synthase (SAM; ATP:l-methionine adenosyltransferase, EC 2.5.1.6) catalyzes the biosynthesis of S-adenosyl-l-methionine (AdoMet), a universal methyl-group donor. This enzyme is induced by salinity stress in tomato (Lycopersicon esculentum Mill.). To elucidate the role of SAM and AdoMet in the adaptation of plants to a saline environment, the expression pattern and histological distribution of SAM was investigated in control and salt-stressed tomato plants. Immunohistochemical analysis showed that SAM proteins were expressed in all cell types and plant organs, albeit with preferential accumulation in lignified tissues. Lignin deposition was estimated by histochemical tests and the extent of tissue lignification in response to salinity was quantified by image analysis. The average number of lignified cells in vascular bundles was significantly greater in plants under salt stress, with a maximal expansion of the lignified area found in the root vasculature. Accordingly, the greatest abundance of SAM gene transcripts and proteins occurred in roots. These results indicate that increased SAM activity correlated with a greater deposition of lignin in the vascular tissues of plants under salinity stress. A model is proposed in which an increased number of lignified tracheary elements in tomato roots under salt stress may enhance the cell-to-cell pathway for water transport, which would impart greater selectivity and reduced ion uptake, and compensate for diminished bulk flow of water and solutes along the apoplastic pathway.

Isolation and characterisation of symbiotically effective Rhizobium resistant to arsenic and heavy metals after the toxic spill at the Aznalcóllar pyrite mine

Abstract: After the toxic spill occurred at Aznalcollar pyrite mine (Southern Spain), a wide area of croplands near the Donana Wild Park was contaminated with 4.5 million m3 of slurries composed of acidic waters loaded with toxic metals and metalloids such as As, Sb, Zn, Pb, Cu, Co, Tl, Bi, Cd, Ag, Hg and Se. Today, 6 years after the spill, the concentration of toxic elements in these soils is still very high, in spite of the efforts to clean the zone. However, some plant species have colonised this contaminated area. Legumes possessing N2-fixing nodules on their roots represented a significant proportion of these plants. Our objective was to use the Rhizobium–legume symbiosis as a new tool for bioremediate the affected area. We have isolated about 100 Rhizobium strains, 41 of them being resistant to high concentrations of As (300 mg l−1), Cu (100 mg l−1) and Pb (500 mg l−1). Their phenotypes and bioaccumulation potentials have been characterised by their growth rates in media supplemented with As and heavy metals. The presence of the resistance genes in some strains has been confirmed by PCR and Southern blot hybridisation. Several Rhizobium were symbiotically effective in the contaminated soils. On the other hand, the first steps in nodule establishment seemed to be more affected by heavy metals than N2-fixation.

Sequential and Iterative Architectures for Distributed Model Predictive Control of Nonlinear Process Systems

Abstract: In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one-directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi-directional communication strategy, are evaluated in parallel and iterate to improve closed-loop performance. In the design of the distributed model predictive controllers, Lyapunov-based model predictive control techniques are used. To ensure the stability of the closed-loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov-based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed-loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. V C 2010 American Institute of Chemical Engineers AIChE J, 56: 2137–2149, 2010