%0 Journal Article %1 Raue2015Data2Dynamics %A Raue, A. %A Steiert, B. %A Schelker, M. %A Kreutz, C. %A Maiwald, T. %A Hass, H. %A Vanlier, J. %A Tönsing, C. %A Adlung, L. %A Engesser, R. %A Mader, W. %A Heinemann, T. %A Hasenauer, J. %A Schilling, M. %A Höfer, T. %A Klipp, E. %A Theis, F. %A Klingmüller, U. %A Schöberl, B. %A Timmer, J. %D 2015 %I Oxford University Press %J Bioinformatics %K parameter-estimation %N 21 %P 3558--3560 %R 10.1093/bioinformatics/btv405 %T Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems %U http://dx.doi.org/10.1093/bioinformatics/btv405 %V 31 %X Summary: 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.

@article{Raue2015Data2Dynamics, abstract = {Summary: 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.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Raue, A. and Steiert, B. and Schelker, M. and Kreutz, C. and Maiwald, T. and Hass, H. and Vanlier, J. and T\"{o}nsing, C. and Adlung, L. and Engesser, R. and Mader, W. and Heinemann, T. and Hasenauer, J. and Schilling, M. and H\"{o}fer, T. and Klipp, E. and Theis, F. and Klingm\"{u}ller, U. and Sch\"{o}berl, B. and Timmer, J.}, biburl = {https://www.bibsonomy.org/bibtex/262b8f41499aeef84b09c452d96515689/karthikraman}, citeulike-article-id = {13666209}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btv405}, citeulike-linkout-1 = {http://dx.doi.org/10.1093/bioinformatics/btv405}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/31/21/3558.abstract}, citeulike-linkout-3 = {http://bioinformatics.oxfordjournals.org/content/31/21/3558.full.pdf}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/26142188}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=26142188}, day = 01, doi = {10.1093/bioinformatics/btv405}, interhash = {524a9d684526b4367ccf35bddb05e557}, intrahash = {62b8f41499aeef84b09c452d96515689}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {parameter-estimation}, month = nov, number = 21, pages = {3558--3560}, pmid = {26142188}, posted-at = {2015-07-07 06:21:27}, priority = {2}, publisher = {Oxford University Press}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {{Data2Dynamics}: a modeling environment tailored to parameter estimation in dynamical systems}, url = {http://dx.doi.org/10.1093/bioinformatics/btv405}, volume = 31, year = 2015 }