Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany.
Institute of Computational Biology, Helmholtz Zentrum München, 85764 München, Germany.
Bioinformatics. 2018 Apr 15;34(8):1421-1423. doi: 10.1093/bioinformatics/btx735.
Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed.
We introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models.
GenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI.
thomas.ligon@physik.uni-muenchen.de or jan.hasenauer@helmholtz-muenchen.de.
Supplementary data are available at Bioinformatics online.
使用常微分方程的数学建模被用于系统生物学,以提高对动态生物过程的理解。常微分方程模型的参数通常是根据实验数据来估计的。为了先验地分析估计问题的解的唯一性,已经开发了结构可识别性分析方法。
我们引入了 GenSSI 2.0,这是软件工具 GenSSI(用于测试结构可识别性的生成序列)的一个升级版本。GenSSI 2.0 是第一个用于结构可识别性分析的工具箱,它实现了系统生物学标记语言的导入、状态/参数转换以及多实验结构可识别性分析。此外,GenSSI 2.0 支持多种 MATLAB 版本,并且比其以前的版本在计算上更有效率,能够分析更复杂的模型。
GenSSI 2.0 是一个开源的 MATLAB 工具箱,可以在 https://github.com/genssi-developer/GenSSI 上获得。
thomas.ligon@physik.uni-muenchen.de 或 jan.hasenauer@helmholtz-muenchen.de。
补充数据可在 Bioinformatics 在线获得。