63 research outputs found
PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation
International audienceMotivation: Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline , which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. Results: First, we present a novel learning process to compute data-driven distant-dependent pair-wise potentials, adapted from our previous method used for rescoring of putative protein–protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5–15 min on a modern laptop and can easily be extended to other types of interactions. Availability and Implementation: https://team.inria.fr/nano-d/software/PEPSI-Dock. Contact: [email protected]
PHENOPSIS DB: an Information System for Arabidopsis thaliana phenotypic data in an environmental context
<p>Abstract</p> <p>Background</p> <p>Renewed interest in plant × environment interactions has risen in the post-genomic era. In this context, high-throughput phenotyping platforms have been developed to create reproducible environmental scenarios in which the phenotypic responses of multiple genotypes can be analysed in a reproducible way. These platforms benefit hugely from the development of suitable databases for storage, sharing and analysis of the large amount of data collected. In the model plant <it>Arabidopsis thaliana</it>, most databases available to the scientific community contain data related to genetic and molecular biology and are characterised by an inadequacy in the description of plant developmental stages and experimental metadata such as environmental conditions. Our goal was to develop a comprehensive information system for sharing of the data collected in PHENOPSIS, an automated platform for <it>Arabidopsis thaliana </it>phenotyping, with the scientific community.</p> <p>Description</p> <p>PHENOPSIS DB is a publicly available (URL: <url>http://bioweb.supagro.inra.fr/phenopsis/</url>) information system developed for storage, browsing and sharing of online data generated by the PHENOPSIS platform and offline data collected by experimenters and experimental metadata. It provides modules coupled to a Web interface for (i) the visualisation of environmental data of an experiment, (ii) the visualisation and statistical analysis of phenotypic data, and (iii) the analysis of <it>Arabidopsis thaliana </it>plant images.</p> <p>Conclusions</p> <p>Firstly, data stored in the PHENOPSIS DB are of interest to the <it>Arabidopsis thaliana </it>community, particularly in allowing phenotypic meta-analyses directly linked to environmental conditions on which publications are still scarce. Secondly, data or image analysis modules can be downloaded from the Web interface for direct usage or as the basis for modifications according to new requirements. Finally, the structure of PHENOPSIS DB provides a useful template for the development of other similar databases related to genotype × environment interactions.</p
Development of a conditionally immortalized human pancreatic β cell line
International audienceDiabetic patients exhibit a reduction in β cells, which secrete insulin to help regulate glucose homeostasis; however, little is known about the factors that regulate proliferation of these cells in human pancreas. Access to primary human β cells is limited and a challenge for both functional studies and drug discovery progress. We previously reported the generation of a human β cell line (EndoC-βH1) that was generated from human fetal pancreas by targeted oncogenesis followed by in vivo cell differentiation in mice. EndoC-βH1 cells display many functional properties of adult β cells, including expression of β cell markers and insulin secretion following glucose stimulation; however, unlike primary β cells, EndoC-βH1 cells continuously proliferate. Here, we devised a strategy to generate conditionally immortalized human β cell lines based on Cre-mediated excision of the immortalizing transgenes. The resulting cell line (EndoC-βH2) could be massively amplified in vitro. After expansion, transgenes were efficiently excised upon Cre expression, leading to an arrest of cell proliferation and pronounced enhancement of β cell–specific features such as insulin expression, content, and secretion. Our data indicate that excised EndoC-βH2 cells are highly representative of human β cells and should be a valuable tool for further analysis of human β cells
Changements de pratiques dans les produits cosmétiques des lanceurs d'alerte aux impacts sur le milieu aquatique
International audiencePrésentation du projet Cosmet'ea
Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling::A CASP-CAPRI experiment
A New Strategy to Generate Functional Insulin-Producing Cell Lines by Somatic Gene Transfer into Pancreatic Progenitors
BACKGROUND: There is increasing interest in developing human cell lines to be used to better understand cell biology, but also for drug screening, toxicology analysis and future cell therapy. In the endocrine pancreatic field, functional human beta cell lines are extremely scarce. On the other hand, rodent insulin producing beta cells have been generated during the past years with great success. Many of such cell lines were produced by using transgenic mice expressing SV40T antigen under the control of the insulin promoter, an approach clearly inadequate in human. Our objective was to develop and validate in rodent an alternative transgenic-like approach, applicable to human tissue, by performing somatic gene transfer into pancreatic progenitors that will develop into beta cells. METHODS AND FINDINGS: In this study, rat embryonic pancreases were transduced with recombinant lentiviral vector expressing the SV40T antigen under the control of the insulin promoter. Transduced tissues were next transplanted under the kidney capsule of immuno-incompetent mice allowing insulinoma development from which beta cell lines were established. Gene expression profile, insulin content and glucose dependent secretion, normalization of glycemia upon transplantation into diabetic mice validated the approach to generate beta cell lines. CONCLUSIONS: Somatic gene transfer into pancreatic progenitors represents an alternative strategy to generate functional beta cell lines in rodent. Moreover, this approach can be generalized to derive cells lines from various tissues and most importantly from tissues of human origin
Multigrid methods applied to data assimilation for geophysics models
Depuis ces trente dernières années, les systèmes d'observation de la Terre et les modèles numériques se sont perfectionnés et complexifiés pour nous fournir toujours plus de données, réelles et numériques. Ces données, de nature très diverse, forment maintenant un ensemble conséquent d'informations précises mais hétérogènes sur les structures et la dynamique des fluides géophysiques. Dans les années 1980, des méthodes d'optimisation, capables de combiner les informations entre elles, ont permis d'estimer les paramètres des modèles numériques et d'obtenir une meilleure prévision des courants marins et atmosphériques. Ces méthodes puissantes, appelées assimilation variationnelle de données, peinent à tirer profit de la toujours plus grande complexité des informations de par le manque de puissance de calcul disponible. L'approche, que nous développons, s'intéresse à l'utilisation des méthodes multigrilles, jusque là réservées à la résolution de systèmes d'équations différentielles, pour résoudre l'assimilation haute résolution de données. Les méthodes multigrilles sont des méthodes de résolution itératives, améliorées par des corrections calculées sur des grilles de plus basses résolutions. Nous commençons par étudier dans le cas d'un modèle linéaire la robustesse de l'approche multigrille et en particulier l'effet de la correction par grille grossière. Nous dérivons ensuite les algorithmes multigrilles dans le cadre non linéaire. Les deux types d'algorithmes étudiés reposent d'une part sur la méthode de Gauss Newton multigrille et d'autre part sur une méthode sans linéarisation globale : le Full Approximation Scheme (FAS). Ceux-ci sont appliqués au problème de l'assimilation variationnelle de données dans le cadre d'une équation de Burgers 1D puis d'un modèle Shallow-water 2D. Leur comportement est analysé et comparé aux méthodes plus traditionnelles de type incrémentale ou multi-incrémentale.For these last thirty years, earth observation and numerical models improved greatly and provide now a huge amount of accurate, yet heterogeneous, information on geophysics fluids dynamics and structures. Optimization methods from the eighties called variational data assimilation are capable of merging information from different sources. They have been used to estimate the parameters of numerical models and better forecast oceanic and atmospheric flows. Unfortunately, these powerful methods have trouble making benefit of always more complex information, suffering from the lack of available powerful calculators. The approach developed here, focuses on the use of multigrid methods, that are commonly used in the context of differential equations systems, to solve high resolution data assimilation. Multigrid methods are iterative methods improved by the use of feedback corrections evaluated on coarse resolution. First in the case of linear assimilation, we study the robustness of multigrid approach and the efficiency of the coarse grid correction step. We then apply the multigrid algorithms on a non linear 1-D Burgers equation and on a 2-D Shallow-Water model. We study two types of algorithms, the Gauss Newton Multigrid, which lays on global linearization, and the Full Approximation Scheme. Their behavior is compared to more traditional approaches as incremental and multi-incremental ones
Applications des méthodes multigrilles à l'assimilation de données en géophysique
For these last thirty years, earth observation and numerical models improved greatly and provide now a huge amount of accurate, yet heterogeneous, information on geophysics fluids dynamics and structures. Optimization methods from the eighties called variational data assimilation are capable of merging information from different sources. They have been used to estimate the parameters of numerical models and better forecast oceanic and atmospheric flows. Unfortunately, these powerful methods have trouble making benefit of always more complex information, suffering from the lack of available powerful calculators. The approach developed here, focuses on the use of multigrid methods, that are commonly used in the context of differential equations systems, to solve high resolution data assimilation. Multigrid methods are iterative methods improved by the use of feedback corrections evaluated on coarse resolution. First in the case of linear assimilation, we study the robustness of multigrid approach and the efficiency of the coarse grid correction step. We then apply the multigrid algorithms on a non linear 1-D Burgers equation and on a 2-D Shallow-Water model. We study two types of algorithms, the Gauss Newton Multigrid, which lays on global linearization, and the Full Approximation Scheme. Their behavior is compared to more traditional approaches as incremental and multi-incremental ones.Depuis ces trente dernières années, les systèmes d'observation de la Terre et les modèles numériques se sont perfectionnés et complexifiés pour nous fournir toujours plus de données, réelles et numériques. Ces données, de nature très diverse, forment maintenant un ensemble conséquent d'informations précises mais hétérogènes sur les structures et la dynamique des fluides géophysiques. Dans les années 1980, des méthodes d'optimisation, capables de combiner les informations entre elles, ont permis d'estimer les paramètres des modèles numériques et d'obtenir une meilleure prévision des courants marins et atmosphériques. Ces méthodes puissantes, appelées assimilation variationnelle de données, peinent à tirer profit de la toujours plus grande complexité des informations de par le manque de puissance de calcul disponible. L'approche, que nous développons, s'intéresse à l'utilisation des méthodes multigrilles, jusque là réservées à la résolution de systèmes d'équations différentielles, pour résoudre l'assimilation haute résolution de données. Les méthodes multigrilles sont des méthodes de résolution itératives, améliorées par des corrections calculées sur des grilles de plus basses résolutions. Nous commençons par étudier dans le cas d'un modèle linéaire la robustesse de l'approche multigrille et en particulier l'effet de la correction par grille grossière. Nous dérivons ensuite les algorithmes multigrilles dans le cadre non linéaire. Les deux types d'algorithmes étudiés reposent d'une part sur la méthode de Gauss Newton multigrille et d'autre part sur une méthode sans linéarisation globale : le Full Approximation Scheme (FAS). Ceux-ci sont appliqués au problème de l'assimilation variationnelle de données dans le cadre d'une équation de Burgers 1D puis d'un modèle Shallow-water 2D. Leur comportement est analysé et comparé aux méthodes plus traditionnelles de type incrémentale ou multi-incrémentale
Prise en charge de la dermatite atopique (place du tracolimus)
NANCY1-SCD Pharmacie-Odontologie (543952101) / SudocSudocFranceF
Mathématiques et algorithmique pour l’aide à planification territoriale
Article rédigé dans le cadre de l'ARP MathsInTerreL'équipe STEEP est une équipe-projet de recherche interdisciplinaire qui s'intéresse à la modélisation et simulation des interactions entre l'environnement, l'économie, et les facteurs sociaux, en ayant comme objectif une transition vers un mode de vie soutenable via la mise en oeuvre de politiques territoriales permettant de vivre plus en harmonie avec l'environnement et les écosystèmes, ainsi qu'en adéquation avec les ressources et contraintes des territoires. Notre objectif est de développer des outils d'aide à la décision destinés aux collectivités territoriales permettant la mise en place de cette transition. Ces outils doivent permettre d'aider à la compréhension des mécanismes systémiques clés qui sont difficiles à appréhender sans l'aide du numérique, puis à l'identification de levier d'actions. Ils peuvent être statistiques, basés sur de la simulation, de l'optimisation, ou des outils de visualisation. Une expertise en mathématiques et algorithmique est nécessaire pour développer ces outils. Dans ce cadre, il nous pensons qu'il est très important de développer des outils d'analyses sectoriels (qu'est ce qui rentre, sort et est transformé sur un territoire ?) ainsi que des outils permettant de mieux gérer les phénomènes spatiaux des territoires, en particulier pour contrôler l'étalement urbain
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