(Bio)Process Engineering Group, IIM-CSIC, Spanish National Research Council, C/Eduardo Cabello 6, Vigo, Spain.
Bioinformatics. 2012 Jun 1;28(11):1549-50. doi: 10.1093/bioinformatics/bts171. Epub 2012 Apr 6.
Chemical reaction network theory is widely used in modeling and analyzing complex biochemical systems such as metabolic networks and cell signalling pathways. Being able to produce all the biologically and chemically important qualitative dynamical features, chemical reaction networks (CRNs) have attracted significant attention in the systems biology community. It is well-known that the reliable inference of CRN models generally requires thorough identifiability and distinguishability analysis together with carefully selected prior modeling assumptions. Here, we present a software toolbox CRNreals that supports the distinguishability and identifiability analysis of CRN models using recently published optimization-based procedures.
The CRNreals toolbox and the associated documentation are available at http://www.iim.csic.es/~gingproc/CRNreals/. The toolbox runs under the popular MATLAB computational environment and supports several free and commercial linear programming and mixed integer linear programming solvers.
化学反应网络理论被广泛应用于代谢网络和细胞信号通路等复杂生化系统的建模和分析。化学反应网络（CRN）能够产生所有具有生物学和化学重要性的定性动态特征，因此在系统生物学领域引起了广泛关注。众所周知，CRN 模型的可靠推断通常需要彻底的可识别性和可区分性分析，以及精心选择的先验建模假设。在这里，我们提出了一个软件工具箱 CRNreals，它使用最近发布的基于优化的程序支持 CRN 模型的可区分性和可识别性分析。
CRNreals 工具箱及相关文档可在 http://www.iim.csic.es/~gingproc/CRNreals/ 获得。该工具箱在流行的 MATLAB 计算环境下运行，支持几种免费和商业的线性规划和混合整数线性规划求解器。
