Fraunhofer Institute for Algorithms and Scientific Computing

Sankt Augustin, Germany

Fraunhofer Institute for Algorithms and Scientific Computing

Sankt Augustin, Germany
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Deyati A.,Knowledge Management | Deyati A.,Fraunhofer Institute for Algorithms and Scientific Computing | Younesi E.,Fraunhofer Institute for Algorithms and Scientific Computing | Hofmann-Apitius M.,Fraunhofer Institute for Algorithms and Scientific Computing | Novac N.,Knowledge Management
Drug Discovery Today | Year: 2013

Recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for drug discovery companies to invest in novel strategies for biomarker discovery. In parallel with the rapid advancement and clinical adoption of high-throughput technologies, a number of knowledge management and systems biology approaches have been developed to analyze an ever increasing collection of OMICs data. This review discusses current biomarker discovery technologies highlighting challenges and opportunities of knowledge capturing and presenting a perspective of the future integrative modeling approaches as an emerging trend in biomarker prediction. © 2013 Elsevier Ltd.


Kirschner K.N.,Fraunhofer Institute for Algorithms and Scientific Computing | Lins R.D.,Federal University of Pernambuco | Maass A.,Fraunhofer Institute for Algorithms and Scientific Computing | Soares T.A.,Federal University of Pernambuco
Journal of Chemical Theory and Computation | Year: 2012

Lipopolysaccharides (LPS) comprise the outermost layer of the Gram-negative bacteria cell envelope. Packed onto a lipid layer, the outer membrane displays remarkable physical-chemical differences compared to cell membranes. The carbohydrate-rich region confers a membrane asymmetry that underlies many biological processes such as endotoxicity, antibiotic resistance, and cell adhesion. Furthermore, unlike membrane proteins from other sources, integral outer-membrane proteins do not consist of transmembrane α helices; instead they consist of antiparallel β-barrels, which highlights the importance of the LPS membrane as a medium. In this work, we present an extension of the GLYCAM06 force field that has been specifically developed for LPS membranes using our Wolf 2Pack program. This new set of parameters for lipopolysaccharide molecules expands the GLYCAM06 repertoire of monosaccharides to include phosphorylated N- and O-acetylglucosamine, 3-deoxy-d-manno-oct-2- ulosonic acid, l-glycero-D-manno-heptose and its O-carbamoylated variant, and N-alanine-d-galactosamine. A total of 1 μs of molecular dynamics simulations of the rough LPS membrane of Pseudomonas aeruginosa PA01 is used to showcase the added parameter set. The equilibration of the LPS membrane is shown to be significantly slower compared to phospholipid membranes, on the order of 500 ns. It is further shown that water molecules penetrate the hydrocarbon region up to the terminal methyl groups, much deeper than commonly observed for phospholipid bilayers, and in agreement with neutron diffraction measurements. A comparison of simulated structural, dynamical, and electrostatic properties against corresponding experimentally available data shows that the present parameter set reproduces well the overall structure and the permeability of LPS membranes in the liquid-crystalline phase. © 2012 American Chemical Society.


Zaretskiy Y.,Heriot - Watt University | Geiger S.,Heriot - Watt University | Sorbie K.,Heriot - Watt University | Forster M.,Fraunhofer Institute for Algorithms and Scientific Computing
Advances in Water Resources | Year: 2010

Upscaling pore-scale processes into macroscopic quantities such as hydrodynamic dispersion is still not a straightforward matter for porous media with complex pore space geometries. Recently it has become possible to obtain very realistic 3D geometries for the pore system of real rocks using either numerical reconstruction or micro-CT measurements. In this work, we present a finite element-finite volume simulation method for modeling single-phase fluid flow and solute transport in experimentally obtained 3D pore geometries. Algebraic multigrid techniques and parallelization allow us to solve the Stokes and advection-diffusion equations on large meshes with several millions of elements. We apply this method in a proof-of-concept study of a digitized Fontainebleau sandstone sample. We use the calculated velocity to simulate pore-scale solute transport and diffusion. From this, we are able to calculate the a priori emergent macroscopic hydrodynamic dispersion coefficient of the porous medium for a given molecular diffusion Dm of the solute species. By performing this calculation at a range of flow rates, we can correctly predict all of the observed flow regimes from diffusion dominated to convection dominated. © 2010 Elsevier Ltd.


Clees T.,Fraunhofer Institute for Algorithms and Scientific Computing | Ganzer L.,Clausthal University of Technology
SPE Journal | Year: 2010

We propose a new, efficient, adaptive algebraic multigrid (AMG) solver strategy for the discrete systems of partial-differential equations (PDEs) arising from structured or unstructured grid models in reservoir simulation. The proposed strategy has been particularly tailored to linear systems of equations arising in adaptive implicit methods (AIMs). The coarsening process of the AMG method designed automatically employs information on the physical structure of the models; as a smoother, an adaptive incomplete LU factorization with thresholding (ILUT) method is employed, taking care of an efficient solution of the hyperbolic parts while providing adequately smooth errors for the elliptic parts. To achieve a good compromise of high efficiency and robustness for a variety of problem classes - ranging from simple, small black-oil to challenging, large compositional models - an automatic, adaptive ILUT parameter and AMG solver switching strategy, α-SAMG, has been developed. Its efficiency is demonstrated for eight industrial benchmark cases by comparison against standard one-level and AMG solvers, including constraint pressure residual (CPR), as well as the pure one-level variant of the proposed new strategy. In addition, very promising results of first parallel runs are shown. Copyright © 2010 Society of Petroleum Engineers.


Fluck J.,Fraunhofer Institute for Algorithms and Scientific Computing | Hofmann-Apitius M.,Fraunhofer Institute for Algorithms and Scientific Computing | Hofmann-Apitius M.,Bonn Aachen International Center for Information Technology
Drug Discovery Today | Year: 2014

Scientific communication in biomedicine is, by and large, still text based. Text mining technologies for the automated extraction of useful biomedical information from unstructured text that can be directly used for systems biology modelling have been substantially improved over the past few years. In this review, we underline the importance of named entity recognition and relationship extraction as fundamental approaches that are relevant to systems biology. Furthermore, we emphasize the role of publicly organized scientific benchmarking challenges that reflect the current status of text-mining technology and are important in moving the entire field forward. Given further interdisciplinary development of systems biology-orientated ontologies and training corpora, we expect a steadily increasing impact of text-mining technology on systems biology in the future. © 2013 Elsevier Ltd.


Hulsmann M.,Fraunhofer Institute for Algorithms and Scientific Computing | Reith D.,Bonn-Rhein-Sieg University of Applied Sciences
Entropy | Year: 2013

Molecular modeling is an important subdomain in the field of computational modeling, regarding both scientific and industrial applications. This is because computer simulations on a molecular level are a virtuous instrument to study the impact of microscopic on macroscopic phenomena. Accurate molecular models are indispensable for such simulations in order to predict physical target observables, like density, pressure, diffusion coefficients or energetic properties, quantitatively over a wide range of temperatures. Thereby, molecular interactions are described mathematically by force fields. The mathematical description includes parameters for both intramolecular and intermolecular interactions. While intramolecular force field parameters can be determined by quantum mechanics, the parameterization of the intermolecular part is often tedious. Recently, an empirical procedure, based on the minimization of a loss function between simulated and experimental physical properties, was published by the authors. Thereby, efficient gradient-based numerical optimization algorithms were used. However, empirical force field optimization is inhibited by the two following central issues appearing in molecular simulations: firstly, they are extremely time-consuming, even on modern and high-performance computer clusters, and secondly, simulation data is affected by statistical noise. The latter provokes the fact that an accurate computation of gradients or Hessians is nearly impossible close to a local or global minimum, mainly because the loss function is flat. Therefore, the question arises of whether to apply a derivative-free method approximating the loss function by an appropriate model function. In this paper, a new Sparse Grid-based Optimization Workflow (SpaGrOW) is presented, which accomplishes this task robustly and, at the same time, keeps the number of time-consuming simulations relatively small. This is achieved by an efficient sampling procedure for the approximation based on sparse grids, which is described in full detail: in order to counteract the fact that sparse grids are fully occupied on their boundaries, a mathematical transformation is applied to generate homogeneous Dirichlet boundary conditions. As the main drawback of sparse grids methods is the assumption that the function to be modeled exhibits certain smoothness properties, it has to be approximated by smooth functions first. Radial basis functions turned out to be very suitable to solve this task. The smoothing procedure and the subsequent interpolation on sparse grids are performed within sufficiently large compact trust regions of the parameter space. It is shown and explained how the combination of the three ingredients leads to a new efficient derivative-free algorithm, which has the additional advantage that it is capable of reducing the overall number of simulations by a factor of about two in comparison to gradient-based optimization methods. At the same time, the robustness with respect to statistical noise is maintained. This assertion is proven by both theoretical considerations and practical evaluations for molecular simulations on chemical example substances. © 2013 by the authors.


Reith D.,Fraunhofer Institute for Algorithms and Scientific Computing | Kirschner K.N.,Fraunhofer Institute for Algorithms and Scientific Computing
Computer Physics Communications | Year: 2011

In this article we present our recent efforts in designing a comprehensive consistent scientific workflow, nicknamed Wolf2 Pack, for force-field optimization in the field of computational chemistry. Atomistic force fields represent a multiscale bridge that connects high-resolution quantum mechanics knowledge to coarser molecular mechanics-based models. Force-field optimization has so far been a time-consuming and error-prone process, and is a topic where the use of a scientific workflow can provide obvious great benefits. As a case study we generate a gas-phase force field for methanol using Wolf2 Pack, with special attention given toward deriving partial atomic charges. © 2011 Elsevier B.V. All rights reserved.


Steffes-Lai D.,Fraunhofer Institute for Algorithms and Scientific Computing
Key Engineering Materials | Year: 2014

This paper presents a fully automatic parameter classification procedure in order to identify the most influencing parameters together with locally interesting parts of the component considered in a certain processing step. The results of this classification approach are used for a parameter space reduction in order to minimize the computational effort for subsequent analysis and optimization tasks based on forecast models. In particular, an outlook on the evaluation of radial basis function metamodels for a robust parameter identification is given. We demonstrate the classification procedure and its benefits by an industrially relevant deep drawing process of a pan with secondary design elements. © 2014 Trans Tech Publications, Switzerland.


Griebel M.,University of Bonn | Hamaekers J.,Fraunhofer Institute for Algorithms and Scientific Computing
Lecture Notes in Computational Science and Engineering | Year: 2014

In this paper, we present an algorithm for trigonometric interpolation of multivariate functions on generalized sparse grids and study its application for the approximation of functions in periodic Sobolev spaces of dominating mixed smoothness. In particular, we derive estimates for the error and the cost. We construct interpolants with a computational cost complexity which is substantially lower than for the standard full grid case. The associated generalized sparse grid interpolants have the same approximation order as the standard full grid interpolants, provided that certain additional regularity assumptions on the considered functions are fulfilled. Numerical results validate our theoretical findings. © Springer International Publishing Switzerland 2014.


Smith E.,Fraunhofer Institute for Algorithms and Scientific Computing
Software and Systems Modeling | Year: 2015

This article is a short summary and explanation of the scientific work of Carl Adam Petri. The very basics of net theory are sufficient to understand it. © 2014, Springer-Verlag Berlin Heidelberg.

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