Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems
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Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems. / Raue, A; Steiert, B; Schelker, M; Kreutz, C; Maiwald, T; Hass, H; Vanlier, J; Tönsing, C; Adlung, Lorenz; Engesser, R; Mader, W; Heinemann, T; Hasenauer, J; Schilling, M; Höfer, T; Klipp, E; Theis, F; Klingmüller, U; Schöberl, B; Timmer, J.
In: BIOINFORMATICS, Vol. 31, No. 21, 01.11.2015, p. 3558-60.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems
AU - Raue, A
AU - Steiert, B
AU - Schelker, M
AU - Kreutz, C
AU - Maiwald, T
AU - Hass, H
AU - Vanlier, J
AU - Tönsing, C
AU - Adlung, Lorenz
AU - Engesser, R
AU - Mader, W
AU - Heinemann, T
AU - Hasenauer, J
AU - Schilling, M
AU - Höfer, T
AU - Klipp, E
AU - Theis, F
AU - Klingmüller, U
AU - Schöberl, B
AU - Timmer, J
N1 - © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - 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.deSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - 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.deSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
KW - Algorithms
KW - Bayes Theorem
KW - Models, Biological
KW - Software
KW - Systems Biology/methods
U2 - 10.1093/bioinformatics/btv405
DO - 10.1093/bioinformatics/btv405
M3 - SCORING: Journal article
C2 - 26142188
VL - 31
SP - 3558
EP - 3560
JO - BIOINFORMATICS
JF - BIOINFORMATICS
SN - 1367-4803
IS - 21
ER -
