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  • 2015-2019  (7)
  • 2010-2014  (17)
  • 1
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    Fast integration-based prediction bands for ordinary differential equation models (2016)
    Oxford University Press
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    Publication Date: 2016-04-08
    Description: Motivation: To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model’s parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality , classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models. Results: In this article, reliable and smooth point-wise prediction and confidence bands to assess the model’s uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared with repeated profile likelihood calculations at multiple time points. Availability and implementation: The integration framework and the examples used in this article are provided with the software package Data2Dynamics, which is based on MATLAB and freely available at http://www.data2dynamics.org . Contact: helge.hass@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 2
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    Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems (2015)
    Oxford University Press
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    Publication Date: 2015-10-21
    Description: : Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of systems biology. Two of the most critical steps in this approach are to construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions and to perform efficient and reliable parameter estimation for model fitting. We present a modeling environment for MATLAB that pioneers these challenges. The numerically expensive parts of the calculations such as the solving of the differential equations and of the associated sensitivity system are parallelized and automatically compiled into efficient C code. A variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented and used on a range of applications that lead to publications. Availability and implementation: The Data2Dynamics modeling environment is MATLAB based, open source and freely available at http://www.data2dynamics.org . Contact: andreas.raue@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 3
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    Comprehensive estimation of input signals and dynamics in biochemical reaction networks (2012)
    Oxford University Press
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    Publication Date: 2012-09-08
    Description: Motivation: Cellular information processing can be described mathematically using differential equations. Often, external stimulation of cells by compounds such as drugs or hormones leading to activation has to be considered. Mathematically, the stimulus is represented by a time-dependent input function. Parameters such as rate constants of the molecular interactions are often unknown and need to be estimated from experimental data, e.g. by maximum likelihood estimation . For this purpose, the input function has to be defined for all times of the integration interval. This is usually achieved by approximating the input by interpolation or smoothing of the measured data. This procedure is suboptimal since the input uncertainties are not considered in the estimation process which often leads to overoptimistic confidence intervals of the inferred parameters and the model dynamics. Results: This article presents a new approach which includes the input estimation into the estimation process of the dynamical model parameters by minimizing an objective function containing all parameters simultaneously. We applied this comprehensive approach to an illustrative model with simulated data and compared it to alternative methods. Statistical analyses revealed that our method improves the prediction of the model dynamics and the confidence intervals leading to a proper coverage of the confidence intervals of the dynamic parameters. The method was applied to the JAK-STAT signaling pathway . Availability: MATLAB code is available on the authors' website http://www.fdmold.uni-freiburg.de/~schelker/ . Contact: max.schelker@fdm.uni-freiburg.de Supplementary Information: Additional information is available at Bioinformatics Online .
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 4
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    Rare-earth silicide thin films on the Si(111) surface (2016)
    American Physical Society (APS)
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    Publication Date: 2016-05-07
    Description: Author(s): S. Sanna, C. Dues, W. G. Schmidt, F. Timmer, J. Wollschläger, M. Franz, S. Appelfeller, and M. Dähne Rare-earth induced layered structures on the Si(111) surface are investigated by a combined approach consisting of ab initio thermodynamics, electron and x-ray diffraction experiments, angle-resolved photoelectron spectroscopy, and scanning tunneling microscopy. Our density functional theory calcula… [Phys. Rev. B 93, 195407] Published Fri May 06, 2016
    Keywords: Surface physics, nanoscale physics, low-dimensional systems
    Print ISSN: 1098-0121
    Electronic ISSN: 1095-3795
    Topics: Physics
    Published by American Physical Society (APS)
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  • 5
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    Integration of Boolean models exemplified on hepatocyte signal transduction (2012)
    Oxford University Press
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    Publication Date: 2012-05-23
    Description: The number of mathematical models for biological pathways is rapidly growing. In particular, Boolean modelling proved to be suited to describe large cellular signalling networks. Systems biology is at the threshold to holistic understanding of comprehensive networks. In order to reach this goal, connection and integration of existing models of parts of cellular networks into more comprehensive network models is necessary. We discuss model combination approaches for Boolean models. Boolean modelling is qualitative rather than quantitative and does not require detailed kinetic information. We show that these models are useful precursors for large-scale quantitative models and that they are comparatively easy to combine. We propose modelling standards for Boolean models as a prerequisite for smooth model integration. Using these standards, we demonstrate the coupling of two logical models on two different examples concerning cellular interactions in the liver. In the first example, we show the integration of two Boolean models of two cell types in order to describe their interaction. In the second example, we demonstrate the combination of two models describing different parts of the network of a single cell type. Combination of partial models into comprehensive network models will take systems biology to the next level of understanding. The combination of logical models facilitated by modelling standards is a valuable example for the next step towards this goal.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
    Published by Oxford University Press
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  • 6
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    TSSi--an R package for transcription start site identification from 5' mRNA tag data (2012)
    Oxford University Press
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    Publication Date: 2012-06-12
    Description: : High-throughput sequencing has become an essential experimental approach for the investigation of transcriptional mechanisms. For some applications like ChIP-seq, several approaches for the prediction of peak locations exist. However, these methods are not designed for the identification of transcription start sites (TSSs) because such datasets contain qualitatively different noise. In this application note, the R package TSSi is presented which provides a heuristic framework for the identification of TSSs based on 5' mRNA tag data. Probabilistic assumptions for the distribution of the data, i.e. for the observed positions of the mapped reads, as well as for systematic errors, i.e. for reads which map closely but not exactly to a real TSS, are made and can be adapted by the user. The framework also comprises a regularization procedure which can be applied as a preprocessing step to decrease the noise and thereby reduce the number of false predictions. Availability: The R package TSSi is available from the Bioconductor web site: www.bioconductor.org/packages/release/bioc/html/TSSi.html . Contact: ckreutz@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 7
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    Importance of the Gulf of Mexico as a Climate Driver for US Severe Thunderstorm Activity (2016)
    Wiley
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    Publication Date: 2016-11-25
    Description: Different features of the Gulf of Mexico (GOM), such as the Loop Current and warm-core rings, are found to influence monthly-to-seasonal severe weather occurrence in different regions of the United States (US). The warmer (cooler) the GOM sea-surface temperatures (SSTs), the more (less) hail and tornadoes occur during March-May over the southern US. This pattern is reflected physically in boundary layer specific humidity and mixed-layer convective available potential energy, two large-scale atmospheric conditions favorable for severe weather occurrence. This relationship is complicated by interactions between the GOM and El Niño Southern Oscillation (ENSO), but persists when analyzing ENSO neutral conditions. This suggests that the GOM can influence hail and tornado occurrence and provides another source of regional predictability for seasonal severe weather.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 8
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    Comparison of approaches for parameter identifiability analysis of biological systems (2014)
    Oxford University Press
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    Publication Date: 2014-05-11
    Description: Motivation: Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of Systems Biology. The amount of experimental data that are used to build and calibrate these models is often limited. In this setting, the model parameters may not be uniquely determinable. Structural or a priori identifiability is a property of the system equations that indicates whether, in principle, the unknown model parameters can be determined from the available data. Results: We performed a case study using three current approaches for structural identifiability analysis for an application from cell biology. The approaches are conceptually different and are developed independently. The results of the three approaches are in agreement. We discuss strength and weaknesses of each of them and illustrate how they can be applied to real world problems. Availability and implementation: For application of the approaches to further applications, code representations (DAISY, Mathematica and MATLAB) for benchmark model and data are provided on the authors webpage. Contact: andreas.raue@fdm.uni-freiburg.de
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 9
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    Multi-chromatic control of mammalian gene expression and signaling (2013)
    Oxford University Press
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    Publication Date: 2013-06-28
    Description: The emergence and future of mammalian synthetic biology depends on technologies for orchestrating and custom tailoring complementary gene expression and signaling processes in a predictable manner. Here, we demonstrate for the first time multi-chromatic expression control in mammalian cells by differentially inducing up to three genes in a single cell culture in response to light of different wavelengths. To this end, we developed an ultraviolet B (UVB)-inducible expression system by designing a UVB-responsive split transcription factor based on the Arabidopsis thaliana UVB receptor UVR8 and the WD40 domain of COP1. The system allowed high (up to 800-fold) UVB-induced gene expression in human, monkey, hamster and mouse cells. Based on a quantitative model, we determined critical system parameters. By combining this UVB-responsive system with blue and red light-inducible gene control technology, we demonstrate multi-chromatic multi-gene control by differentially expressing three genes in a single cell culture in mammalian cells, and we apply this system for the multi-chromatic control of angiogenic signaling processes. This portfolio of optogenetic tools enables the design and implementation of synthetic biological networks showing unmatched spatiotemporal precision for future research and biomedical applications.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 10
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    A red/far-red light-responsive bi-stable toggle switch to control gene expression in mammalian cells (2013)
    Oxford University Press
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    Publication Date: 2013-04-14
    Description: Growth and differentiation of multicellular systems is orchestrated by spatially restricted gene expression programs in specialized subpopulations. The targeted manipulation of such processes by synthetic tools with high-spatiotemporal resolution could, therefore, enable a deepened understanding of developmental processes and open new opportunities in tissue engineering. Here, we describe the first red/far-red light-triggered gene switch for mammalian cells for achieving gene expression control in time and space. We show that the system can reversibly be toggled between stable on- and off-states using short light pulses at 660 or 740 nm. Red light-induced gene expression was shown to correlate with the applied photon number and was compatible with different mammalian cell lines, including human primary cells. The light-induced expression kinetics were quantitatively analyzed by a mathematical model. We apply the system for the spatially controlled engineering of angiogenesis in chicken embryos. The system’s performance combined with cell- and tissue-compatible regulating red light will enable unprecedented spatiotemporally controlled molecular interventions in mammalian cells, tissues and organisms.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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