Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox
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DOI：
10.1038/nprot.2007.99
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发表时间：
2007-01-01
期刊：
影响因子：
14.8
通讯作者：
Herrgard, Markus J.
中科院分区：
文献类型：
--
作者：
Becker, Scott A.;Feist, Adam M.;Herrgard, Markus J.
微生物利用其代谢过程的方式可以通过基于约束的基因组规模代谢网络分析来预测。在此，我们介绍了基于约束的重建和分析工具箱，这是一个运行在MatLab环境中的软件包，它允许使用基于约束的方法对细胞行为进行定量预测。具体地说，该软件允许预测稳态和动态最佳生长行为、基因缺失的影响、全面的稳健性分析、对可能的细胞代谢状态范围进行采样以及确定网络模块。工具箱中包括支持这些计算的功能，允许用户输入以系统生物学标记语言格式分发的基因组规模的代谢模型，并只需几行代码就可以执行这些计算。这些结果是对细胞行为的预测，已经被越来越多的研究证实是准确的。软件安装后，计算时间最短，允许用户专注于对计算结果的解释。
The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.