Bioinformatics Program, University of California San Diego, La Jolla, California, USA.
Nat Protoc. 2011 Aug 4;6(9):1290-307. doi: 10.1038/nprot.2011.308.
Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
在过去的十年中,围绕着使用基于约束的重建和分析(COBRA)方法的研究人员群体不断壮大,这些方法可用于使用基因组规模模型模拟、分析和预测各种代谢表型。之前已经介绍了用于实现 COBRA 方法的 MATLAB 软件包 COBRA Toolbox。在这里,我们对这个计算机模拟工具包进行了重大更新。COBRA Toolbox 的 2.0 版本通过包含自最初发布以来开发的计算机分析方法扩展了计算范围。新功能包括(i)网络间隙填充,(ii)(13)C 分析,(iii)代谢工程,(iv)基于组学的分析和(v)可视化。与第一个版本一样,COBRA Toolbox 可以读取和写入系统生物学标记语言格式的模型。在 2.0 版本中,我们提高了性能、可用性和文档水平。现在可以使用一套测试脚本学习工具包的核心功能并验证结果。该工具包降低了使用强大的 COBRA 方法的门槛。