Schellenberger Jan, Palsson Bernhard Ø
Bioinformatics Program, University of California, San Diego, La Jolla, CA 92093-0412, USA.
J Biol Chem. 2009 Feb 27;284(9):5457-61. doi: 10.1074/jbc.R800048200. Epub 2008 Oct 20.
Genome-scale metabolic network reconstructions in microorganisms have been formulated and studied for about 8 years. The constraint-based approach has shown great promise in analyzing the systemic properties of these network reconstructions. Notably, constraint-based models have been used successfully to predict the phenotypic effects of knock-outs and for metabolic engineering. The inherent uncertainty in both parameters and variables of large-scale models is significant and is well suited to study by Monte Carlo sampling of the solution space. These techniques have been applied extensively to the reaction rate (flux) space of networks, with more recent work focusing on dynamic/kinetic properties. Monte Carlo sampling as an analysis tool has many advantages, including the ability to work with missing data, the ability to apply post-processing techniques, and the ability to quantify uncertainty and to optimize experiments to reduce uncertainty. We present an overview of this emerging area of research in systems biology.
微生物中的基因组规模代谢网络重建已被构建和研究约8年。基于约束的方法在分析这些网络重建的系统特性方面显示出巨大潜力。值得注意的是，基于约束的模型已成功用于预测基因敲除的表型效应和代谢工程。大规模模型的参数和变量中固有的不确定性很大，非常适合通过对解空间进行蒙特卡罗采样来研究。这些技术已广泛应用于网络的反应速率（通量）空间，最近的工作集中在动态/动力学特性上。蒙特卡罗采样作为一种分析工具具有许多优点，包括能够处理缺失数据、能够应用后处理技术以及能够量化不确定性和优化实验以减少不确定性。我们概述了系统生物学这一新兴研究领域。
