(Bio)Process Engineering Group, IIM-CSIC C/Eduardo Cabello 6, 36208 Vigo, Spain.
Bioinformatics. 2010 Jul 1;26(13):1675-6. doi: 10.1093/bioinformatics/btq242. Epub 2010 May 5.
SensSB (Sensitivity Analysis for Systems Biology) is an easy to use, MATLAB-based software toolbox, which integrates several local and global sensitivity methods that can be applied to a wide variety of biological models. In addition to addressing the sensitivity analysis problem, SensSB aims to cover all the steps involved during the modeling process. The main features of SensSB are: (i) derivative and variance-based global sensitivity analysis, (ii) pseudo-global identifiability analysis, (iii) optimal experimental design (OED) based on global sensitivities, (iv) robust parameter estimation, (v) local sensitivity and identifiability analysis, (vi) confidence intervals of the estimated parameters and (vii) OED based on the Fisher Information Matrix (FIM). SensSB is also able to import models in the Systems Biology Mark-up Language (SBML) format. Several examples from simple analytical functions to more complex biological pathways have been implemented and can be downloaded together with the toolbox. The importance of using sensitivity analysis techniques for identifying unessential parameters and designing new experiments is quantified by increased identifiability metrics of the models and decreased confidence intervals of the estimated parameters.
SensSB is a software toolbox freely downloadable from http://www.iim.csic.es/ approximately gingproc/SensSB.html. The web site also contains several examples and an extensive documentation.
Supplementary data are available at Bioinformatics online.
SensSB(系统生物学敏感性分析)是一个易于使用的基于 MATLAB 的软件工具箱,它集成了几种局部和全局敏感性方法,可以应用于各种生物模型。除了解决敏感性分析问题外,SensSB 还旨在涵盖建模过程中涉及的所有步骤。SensSB 的主要特点是:(i)基于导数和方差的全局敏感性分析,(ii)伪全局可识别性分析,(iii)基于全局敏感性的最优实验设计(OED),(iv)鲁棒参数估计,(v)局部敏感性和可识别性分析,(vi)估计参数的置信区间,以及(vii)基于 Fisher 信息矩阵(FIM)的 OED。SensSB 还能够导入系统生物学标记语言(SBML)格式的模型。已经实现了几个从简单分析函数到更复杂生物途径的示例,并且可以与工具箱一起下载。通过提高模型的可识别性度量和降低估计参数的置信区间,定量说明了使用敏感性分析技术识别非必要参数和设计新实验的重要性。
SensSB 是一个免费的软件工具箱,可从 http://www.iim.csic.es/ 上下载,大约 gingproc/SensSB.html。该网站还包含几个示例和广泛的文档。
补充数据可在 Bioinformatics 在线获得。