274 research outputs found

    Local Electronic Structure of a Single Magnetic Impurity in a Superconductor

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    The electronic structure near a single classical magnetic impurity in a superconductor is determined using a fully self-consistent Koster-Slater algorithm. Localized excited states are found within the energy gap which are half electron and half hole. Within a jellium model we find the new result that the spatial structure of the positive-frequency (electron-like) spectral weight (or local density of states), can differ strongly from that of the negative frequency (hole-like) spectral weight. The effect of the impurity on the continuum states above the energy gap is calculated with good spectral resolution for the first time. This is also the first three-dimensional self-consistent calculation for a strong magnetic impurity potential.Comment: 13 pages, RevTex, change in heuristic picture, no change in numerical result

    Local Electronic Structure of Defects in Superconductors

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    The electronic structure near defects (such as impurities) in superconductors is explored using a new, fully self-consistent technique. This technique exploits the short-range nature of the impurity potential and the induced change in the superconducting order parameter to calculate features in the electronic structure down to the atomic scale with unprecedented spectral resolution. Magnetic and non-magnetic static impurity potentials are considered, as well as local alterations in the pairing interaction. Extensions to strong-coupling superconductors and superconductors with anisotropic order parameters are formulated.Comment: RevTex source, 20 pages including 22 figures in text with eps

    Advances in ab-initio theory of Multiferroics. Materials and mechanisms: modelling and understanding

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    Within the broad class of multiferroics (compounds showing a coexistence of magnetism and ferroelectricity), we focus on the subclass of "improper electronic ferroelectrics", i.e. correlated materials where electronic degrees of freedom (such as spin, charge or orbital) drive ferroelectricity. In particular, in spin-induced ferroelectrics, there is not only a {\em coexistence} of the two intriguing magnetic and dipolar orders; rather, there is such an intimate link that one drives the other, suggesting a giant magnetoelectric coupling. Via first-principles approaches based on density functional theory, we review the microscopic mechanisms at the basis of multiferroicity in several compounds, ranging from transition metal oxides to organic multiferroics (MFs) to organic-inorganic hybrids (i.e. metal-organic frameworks, MOFs)Comment: 22 pages, 9 figure

    Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks

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    <p>Abstract</p> <p>Background</p> <p>In recent years, constrained optimization – usually referred to as flux balance analysis (FBA) – has become a widely applied method for the computation of stationary fluxes in large-scale metabolic networks. The striking advantage of FBA as compared to kinetic modeling is that it basically requires only knowledge of the stoichiometry of the network. On the other hand, results of FBA are to a large degree hypothetical because the method relies on plausible but hardly provable optimality principles that are thought to govern metabolic flux distributions.</p> <p>Results</p> <p>To augment the reliability of FBA-based flux calculations we propose an additional side constraint which assures thermodynamic realizability, i.e. that the flux directions are consistent with the corresponding changes of Gibb's free energies. The latter depend on metabolite levels for which plausible ranges can be inferred from experimental data. Computationally, our method results in the solution of a mixed integer linear optimization problem with quadratic scoring function. An optimal flux distribution together with a metabolite profile is determined which assures thermodynamic realizability with minimal deviations of metabolite levels from their expected values. We applied our novel approach to two exemplary metabolic networks of different complexity, the metabolic core network of erythrocytes (30 reactions) and the metabolic network iJR904 of <it>Escherichia coli </it>(931 reactions). Our calculations show that increasing network complexity entails increasing sensitivity of predicted flux distributions to variations of standard Gibb's free energy changes and metabolite concentration ranges. We demonstrate the usefulness of our method for assessing critical concentrations of external metabolites preventing attainment of a metabolic steady state.</p> <p>Conclusion</p> <p>Our method incorporates the thermodynamic link between flux directions and metabolite concentrations into a practical computational algorithm. The weakness of conventional FBA to rely on intuitive assumptions about the reversibility of biochemical reactions is overcome. This enables the computation of reliable flux distributions even under extreme conditions of the network (e.g. enzyme inhibition, depletion of substrates or accumulation of end products) where metabolite concentrations may be drastically altered.</p

    A general process for the development of peptide-based immunoassays for monoclonal antibodies

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    Monoclonal antibodies (mAb) are an important and growing class of cancer therapeutics, but pharmacokinetic analyses have in many cases been constrained by the lack of standard and robust pharmacologic assays. The goal of this project was to develop a general method for the production of immunoassays that can measure the levels of therapeutic monoclonal antibodies in biologic samples at relevant concentrations. Alemtuzumab and rituximab are monoclonal approved for the treatment of B-cell malignancies and were used as a model system. Phage-displayed peptide libraries were screened for peptide sequences recognized by alemtuzumab (anti-CD52) or rituximab (anti-CD20). Synthetic biotinylated peptides were used in enzyme-linked immunosorbent assays (ELISA). Peptides directly synthesized on polymer resin beads were used in an immunofluorescent-based assay. Peptide mimetope sequences were recovered for both mAb and confirmed by competitive staining and kinetic measurements. A peptide-based ELISA method was developed for each. The assay for rituximab had a limit of detection of 4 μg/ml, and the assay for alemtuzumab had a limit of detection of 1 μg/ml. Antibody-specific staining of peptide conjugated beads could be seen in a dose-dependent manner. Phage-displayed peptide libraries can be a source of highly specific mimetopes for therapeutic mAb. The biotinylated forms of those peptides are compatible with conventional ELISA methods with sensitivities comparable to other assay methods and sufficient for pharmacological studies of those mAb given at high dose. The process outlined here can be applied to any mAb to enable improved pharmacokinetic analysis during the development and clinical use of this class of therapies

    Incorporation of enzyme concentrations into FBA and identification of optimal metabolic pathways

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    <p>Abstract</p> <p>Background</p> <p>In the present article, we propose a method for determining optimal metabolic pathways in terms of the level of concentration of the enzymes catalyzing various reactions in the entire metabolic network. The method, first of all, generates data on reaction fluxes in a pathway based on steady state condition. A set of constraints is formulated incorporating weighting coefficients corresponding to concentration of enzymes catalyzing reactions in the pathway. Finally, the rate of yield of the target metabolite, starting with a given substrate, is maximized in order to identify an optimal pathway through these weighting coefficients.</p> <p>Results</p> <p>The effectiveness of the present method is demonstrated on two synthetic systems existing in the literature, two pentose phosphate, two glycolytic pathways, core carbon metabolism and a large network of carotenoid biosynthesis pathway of various organisms belonging to different phylogeny. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. Biological relevance and validation of the results are provided. Finally, the impact of the method on metabolic engineering is explained with a few examples.</p> <p>Conclusions</p> <p>The method may be viewed as determining an optimal set of enzymes that is required to get an optimal metabolic pathway. Although it is a simple one, it has been able to identify a carotenoid biosynthesis pathway and the optimal pathway of core carbon metabolic network that is closer to some earlier investigations than that obtained by the extreme pathway analysis. Moreover, the present method has identified correctly optimal pathways for pentose phosphate and glycolytic pathways. It has been mentioned using some examples how the method can suitably be used in the context of metabolic engineering.</p

    Hydrophobicity and Charge Shape Cellular Metabolite Concentrations

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    What governs the concentrations of metabolites within living cells? Beyond specific metabolic and enzymatic considerations, are there global trends that affect their values? We hypothesize that the physico-chemical properties of metabolites considerably affect their in-vivo concentrations. The recently achieved experimental capability to measure the concentrations of many metabolites simultaneously has made the testing of this hypothesis possible. Here, we analyze such recently available data sets of metabolite concentrations within E. coli, S. cerevisiae, B. subtilis and human. Overall, these data sets encompass more than twenty conditions, each containing dozens (28-108) of simultaneously measured metabolites. We test for correlations with various physico-chemical properties and find that the number of charged atoms, non-polar surface area, lipophilicity and solubility consistently correlate with concentration. In most data sets, a change in one of these properties elicits a ∼100 fold increase in metabolite concentrations. We find that the non-polar surface area and number of charged atoms account for almost half of the variation in concentrations in the most reliable and comprehensive data set. Analyzing specific groups of metabolites, such as amino-acids or phosphorylated nucleotides, reveals even a higher dependence of concentration on hydrophobicity. We suggest that these findings can be explained by evolutionary constraints imposed on metabolite concentrations and discuss possible selective pressures that can account for them. These include the reduction of solute leakage through the lipid membrane, avoidance of deleterious aggregates and reduction of non-specific hydrophobic binding. By highlighting the global constraints imposed on metabolic pathways, future research could shed light onto aspects of biochemical evolution and the chemical constraints that bound metabolic engineering efforts

    TRAPPC4-ERK2 Interaction Activates ERK1/2, Modulates Its Nuclear Localization and Regulates Proliferation and Apoptosis of Colorectal Cancer Cells

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    The trafficking protein particle complex 4 (TRAPPC4) is implicated in vesicle-mediated transport, but its association with disease has rarely been reported. We explored its potential interaction with ERK2, part of the ERK1/2 complex in the Extracellular Signal-regulated Kinase/ Mitogen-activated Protein Kinase (ERK-MAPK) pathway, by a yeast two-hybrid screen and confirmed by co-immunoprecipitation (Co-IP) and glutathione S-transferase (GST) pull-down. Further investigation found that when TRAPPC4 was depleted, activated ERK1/2 specifically decreased in the nucleus, which was accompanied with cell growth suppression and apoptosis in colorectal cancer (CRC) cells. Overexpression of TRAPPC4 promoted cell viability and caused activated ERK1/2 to increase overall, but especially in the nucleus. TRAPPC4 was expressed more highly in the nucleus of CRC cells than in normal colonic epithelium or adenoma which corresponded with nuclear staining of pERK1/2. We demonstrate here that TRAPPC4 may regulate cell proliferation and apoptosis in CRC by interaction with ERK2 and subsequently phosphorylating ERK1/2 as well as modulating the subcellular location of pERK1/2 to activate the relevant signaling pathway

    A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient

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    <p>Abstract</p> <p>Background</p> <p>An indirect approach is usually used to estimate the metabolic fluxes of an organism: couple the available measurements with known biological constraints (e.g. stoichiometry). Typically this estimation is done under a static point of view. Therefore, the fluxes so obtained are only valid while the environmental conditions and the cell state remain stable. However, estimating the evolution over time of the metabolic fluxes is valuable to investigate the dynamic behaviour of an organism and also to monitor industrial processes. Although Metabolic Flux Analysis can be successively applied with this aim, this approach has two drawbacks: i) sometimes it cannot be used because there is a lack of measurable fluxes, and ii) the uncertainty of experimental measurements cannot be considered. The Flux Balance Analysis could be used instead, but the assumption of optimal behaviour of the organism brings other difficulties.</p> <p>Results</p> <p>We propose a procedure to estimate the evolution of the metabolic fluxes that is structured as follows: 1) measure the concentrations of extracellular species and biomass, 2) convert this data to measured fluxes and 3) estimate the non-measured fluxes using the Flux Spectrum Approach, a variant of Metabolic Flux Analysis that overcomes the difficulties mentioned above without assuming optimal behaviour. We apply the procedure to a real problem taken from the literature: estimate the metabolic fluxes during a cultivation of CHO cells in batch mode. We show that it provides a reliable and rich estimation of the non-measured fluxes, thanks to considering measurements uncertainty and reversibility constraints. We also demonstrate that this procedure can estimate the non-measured fluxes even when there is a lack of measurable species. In addition, it offers a new method to deal with inconsistency.</p> <p>Conclusion</p> <p>This work introduces a procedure to estimate time-varying metabolic fluxes that copes with the insufficiency of measured species and with its intrinsic uncertainty. The procedure can be used as an off-line analysis of previously collected data, providing an insight into the dynamic behaviour of the organism. It can be also profitable to the on-line monitoring of a running process, mitigating the traditional lack of reliable on-line sensors in industrial environments.</p

    Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

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    The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales
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