Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems.

Abstract:

UNLABELLED: 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.

SEEK ID: https://fairdomhub.org/publications/276

PubMed ID: 26142188

Projects: SBEpo - Systems Biology of Erythropoietin

Publication type: Journal

Journal: Bioinformatics

Citation: Bioinformatics. 2015 Nov 1;31(21):3558-60. doi: 10.1093/bioinformatics/btv405. Epub 2015 Jul 3.

Date Published: 1st Nov 2015

Registered Mode: Not specified

Authors: A. Raue, B. Steiert, M. Schelker, C. Kreutz, T. Maiwald, H. Hass, J. Vanlier, C. Tonsing, L. Adlung, R. Engesser, W. Mader, T. Heinemann, J. Hasenauer, M. Schilling, T. Hofer, E. Klipp, F. Theis, U. Klingmuller, B. Schoberl, J. Timmer

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Created: 13th Oct 2016 at 12:03

Last updated: 3rd Aug 2020 at 18:37

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