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  • 2015-2019  (4)
  • 2016  (4)
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  • 2015-2019  (4)
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  • 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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    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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  • 3
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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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  • 4
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    L1 regularization facilitates detection of cell type-specific parameters in dynamical systems (2016)
    Oxford University Press
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    Publication Date: 2016-09-02
    Description: Motivation: A major goal of drug development is to selectively target certain cell types. Cellular decisions influenced by drugs are often dependent on the dynamic processing of information. Selective responses can be achieved by differences between the involved cell types at levels of receptor, signaling, gene regulation or further downstream. Therefore, a systematic approach to detect and quantify cell type-specific parameters in dynamical systems becomes necessary. Results: Here, we demonstrate that a combination of nonlinear modeling with L 1 regularization is capable of detecting cell type-specific parameters. To adapt the least-squares numerical optimization routine to L 1 regularization, sub-gradient strategies as well as truncation of proposed optimization steps were implemented. Likelihood-ratio tests were used to determine the optimal regularization strength resulting in a sparse solution in terms of a minimal number of cell type-specific parameters that is in agreement with the data. By applying our implementation to a realistic dynamical benchmark model of the DREAM6 challenge we were able to recover parameter differences with an accuracy of 78%. Within the subset of detected differences, 91% were in agreement with their true value. Furthermore, we found that the results could be improved using the profile likelihood. In conclusion, the approach constitutes a general method to infer an overarching model with a minimum number of individual parameters for the particular models. Availability and Implementation: A MATLAB implementation is provided within the freely available, open-source modeling environment Data2Dynamics. Source code for all examples is provided online at http://www.data2dynamics.org/ . Contact: bernhard.steiert@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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