A Raue1, B Steiert2, M Schelker3, C Kreutz2, T Maiwald2, H Hass2, J Vanlier2, C Tönsing2, L Adlung4, R Engesser2, W Mader2, T Heinemann5, J Hasenauer6, M Schilling4, T Höfer5, E Klipp3, F Theis6, U Klingmüller4, B Schöberl1, J Timmer7. 1. Merrimack Pharmaceuticals Inc., Discovery Devision, Cambridge, MA 02139, USA. 2. University of Freiburg, Institute for Physics, 79104 Freiburg, Germany. 3. Humboldt-Universität zu Berlin, Theoretical Biophysics, 10115 Berlin, Germany. 4. Systems Biology of Signal Transduction and. 5. Divison of Theoretical Systems Biology, German Cancer Research Center, 69120 Heidelberg, Germany, BioQuant, University of Heidelberg, 69120 Heidelberg, Germany. 6. Helmholtz Center Munich, Institute of Computational Biology, 85764 Neuherberg, Germany, Technische Universität München, Department of Mathematics, 85748 Garching, Germany and. 7. University of Freiburg, Institute for Physics, 79104 Freiburg, Germany, BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany.
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.
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.
Authors: Sabrina Vullo; Gaetano Bonifacio; Sophie Roy; Niklaus Johner; Simon Bernèche; Stephan Kellenberger Journal: Proc Natl Acad Sci U S A Date: 2017-03-20 Impact factor: 11.205
Authors: Jessica B Casaletto; Melissa L Geddie; Adnan O Abu-Yousif; Kristina Masson; Aaron Fulgham; Antoine Boudot; Tim Maiwald; Jeffrey D Kearns; Neeraj Kohli; Stephen Su; Maja Razlog; Andreas Raue; Ashish Kalra; Maria Håkansson; Derek T Logan; Martin Welin; Shrikanta Chattopadhyay; Brian D Harms; Ulrik B Nielsen; Birgit Schoeberl; Alexey A Lugovskoy; Gavin MacBeath Journal: Proc Natl Acad Sci U S A Date: 2019-03-21 Impact factor: 11.205
Authors: Laura Marie Helleckes; Michael Osthege; Wolfgang Wiechert; Eric von Lieres; Marco Oldiges Journal: PLoS Comput Biol Date: 2022-03-07 Impact factor: 4.475
Authors: Joanneke E Jansen; Dominik Aschenbrenner; Holm H Uhlig; Mark C Coles; Eamonn A Gaffney Journal: PLoS Comput Biol Date: 2022-06-22 Impact factor: 4.779
Authors: Veronica L Porubsky; Arthur P Goldberg; Anand K Rampadarath; David P Nickerson; Jonathan R Karr; Herbert M Sauro Journal: Cell Syst Date: 2020-08-26 Impact factor: 10.304