Manejo integrado de mosca-branca.

bitstream/CNPH-2009/31447/1/cot_30.pd

Algorithms to infer metabolic flux ratios from fluxomics data

In silico cell simulation approaches based in the use of genome-scale metabolic models (GSMMs) and constraint-based methods such as Flux Balance Analysis are gaining importance, but methods to integrate these approaches with omics data are still greatly needed. In this work, the focus relies on fluxomics data that provide valuable information on the intracellular fluxes, although in many cases in an indirect, incomplete and noisy way. The proposed framework enables the integration of fluxomics data, in the form of 13C labeling distribution for metabolite fragments, with GSMMs enriched with carbon atom transition maps. The algorithms implemented allow to infer labeling distributions for fragments/metabolites not measured and to build expressions for the relevant flux ratios that can be then used to enrich constraint-based methods for flux determination. This approach does not require any assumptions on the metabolic network and reaction reversibility, allowing to compute ratios originating from coupled joint points of the network. Also, when enough data do not exist, the system tries to infer ratio bounds from the measurements

Genome-scale metabolic network of the central carbon metabolism of Enterococcus faecalis

The profound advance in experimental high throughput techniques (generally referred to as omics techniques) has enabled the analysis of a large number of components within a living cell. The vast amount of data obtained from the different omics (genomics, proteomics, fluxomics, metabolomics, transcriptomics) demands the use of bioinformatics tools. These methods comprise the development of comparative tools and maintenance of databases for the analysis of genomics data, in addition to the construction of models for the analysis and integration of data in a system-wide approach. Enterococcus faecalis is a gram-positive bacterium that is getting more attention due to its two-face behavior. This natural inhabitant of the mammalian gastrointestinal tract is also an opportunist pathogen responsible for urinary tract infections, nosocomial infections, bacteremia and infective endocarditis. Besides, its intrinsic physiological properties such as inherent antibiotic resistance and exceptional ability to adapt to harsh conditions provide this organism with an enormous advantage in the infection processes. Here, we propose to reconstruct the genome scale metabolic network of the central carbon metabolism of Enterococcus faecalis using genome sequencing information available on different databases as well as proteomics and metabolomics data. The first metabolic model generated for this bacterium will allow correlating metabolite levels and fluxes which enables identification of key control points in its metabolism. As it has been previously shown for other organisms, the metabolic network reconstruction may serve as a valuable tool to predict the phenotypic behaviour under various genetic and environmental conditions.Supported by a PhD grant from the FCT (Portuguese Science Foundation): SFRH/BD/47016/2008 and funding from HRC (Health Research Council of New Zealand)

Frequency up- and down-conversions in two-mode cavity quantum electrodynamics

In this letter we present a scheme for the implementation of frequency up- and down-conversion operations in two-mode cavity quantum electrodynamics (QED). This protocol for engineering bilinear two-mode interactions could enlarge perspectives for quantum information manipulation and also be employed for fundamental tests of quantum theory in cavity QED. As an application we show how to generate a two-mode squeezed state in cavity QED (the original entangled state of Einstein-Podolsky-Rosen)

Os geminivírus em sistema de produção integrada de tomate indústria.

bitstream/item/75714/1/ct-71.pd

Genome scale metabolic network reconstruction of pathogen – Enterococcus faecalis

Enterococcus faecalis is a Gram-positive bacterium that is getting more attention due to its “two-face” behavior. This natural inhabitant of the gastrointestinal mammalian tract is also an opportunist pathogen responsible for urinary tract infections, nosocomial infections, bacteremia and infective endocarditis (1). Since the metabolic reconstruction of Haemophilus influenzae was published in 1999 (2), many other researchers have focused their attention into the possibilities that the new era of genome-scale metabolic models could bring to the scientific scene, both in prokaryotic and eukaryotic organisms

Metabolic footprint analysis of recombinant Escherichia coli strains during fed-batch fermentations

Metabolic footprinting has become a valuable analytical approach for the characterization of phenotypes and the distinction of specific metabolic states resulting from environmental and/or genetic alterations. The metabolic impact of heterologous protein production in Escherichia coli cells is of particular interest, since there are numerous cellular stresses triggered during this process that limit the overall productivity. Because the knowledge on the metabolic responses in recombinant bioprocesses is still scarce, metabolic footprinting can provide relevant information on the intrinsic metabolic adjustments. Thus, the metabolic footprints generated by Escherichia coli W3110 and the ΔrelA mutant strain during recombinant fed-batch fermentations at different experimental conditions, were measured and interpreted. The IPTG-induction of the heterologous protein expression resulted in the rapid accumulation of inhibitors of the glyoxylate shunt in the culture broth, suggesting the clearance of this anaplerotic route to replenish the TCA intermediaries withdrawn for the additional formation of heterologous protein. Nutritional shifts were also critical in the recombinant cellular metabolism, indicating that cells employ diverse strategies to counteract imbalances in the cellular metabolism, including the secretion of certain metabolites that are, most likely, used as a metabolic relief to survival processes.The authors thank to Raphael Aggio for assisting in the automatic refinement and correction of the GC-MS data. This work was supported in part by the research project Bridging Systems and Synthetic Biology for the development of Improved Microbial Cell Factories (MIT-Pt/BS-BB/0082/2008) and HeliSysBio-Molecular Systems Biology Helicobacter pylori (FCT PTDC/EBB-EBI/104235/2008), both financed by the Portuguese Fundacao para a Ciencia e Tecnologia. Sonia Carneiro was also supported by a PhD grant from the same institution (ref. SFRH/BD/22863/2005)

Applying a metabolic footprinting approach to characterize the impact of the recombinant protein production in Escherichia coli

In this study metabolic footprinting was applied to evaluate the metabolic consequences of protein overproduction at slow growth conditions (μ = 0.1 h-1). The extracellular metabolites detected by gas chromatography-mass spectrometry characterized the metabolic footprints before and after the induction of the recombinant protein production (i.e. pre- and post-induction phases). Metabolic footprinting enabled the discrimination between the two growth phases and ex-posed significant metabolic alterations in the extracellular milieu during the re-combinant processes.This work is partly funded by the Portuguese FCT (Fundacao para a Ciencia e Tecnologia) funded MIT-Portugal Program in Bioengineering (MIT-Pt/BSBB/0082/2008). The work of Sonia Carneiro is supported by a PhD grant from FCT (ref. SFRH/BD/22863/2005)

Metabolic network reconstruction of the central carbon metabolism of Enterococcus faecalis

The profound advance in experimental high throughput techniques (generally referred to as “omics techniques”) has enabled the analysis of a large number of components within a living cell. The vast amount of data obtained from the different “omics” (genomics, proteomics, fluxomics, metabolomics, transcriptomics) demands the use of bioinformatics tools. These methods comprise the development of comparative tools and maintenance of databases for the analysis of genomics data, in addition to the construction of models for the analysis and integration of data in a system-wide approach. Enterococcus faecalis is a Gram-positive bacterium that is getting more attention due to its “two-face” behavior. This natural inhabitant of the mammalian gastrointestinal tract is also an opportunist pathogen responsible for urinary tract infections, nosocomial infections, bacteremia and infective endocarditis. Besides, its intrinsic physiological properties such as inherent antibiotic resistance and exceptional ability to adapt to harsh conditions provide this organism with an enormous advantage in the infection processes. Here, we propose to reconstruct the genome scale metabolic network of the central carbon metabolism of Enterococcus faecalis using genome sequencing information available on different databases as well as proteomics and metabolomics data. The first metabolic model generated for this bacterium will allow correlating metabolite levels and fluxes which enables identification of key control points in its metabolism. As it has been previously shown for other organisms, the metabolic network reconstruction may serve as a valuable tool to predict the phenotypic behaviour under various genetic and environmental conditions