Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma-shi, Nara 630-0192 Japan.
Plant Cell Physiol. 2013 May;54(5):728-39. doi: 10.1093/pcp/pct052. Epub 2013 Apr 9.
Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.
代谢组学分析工具可以提供生物体中代谢物浓度的定量信息。在本文中，我们提出了最小路径模型生成工具 SS-mPMG 来模拟代谢物浓度的动态，以及使用遗传算法进行参数估计的工具 SS-GA。SS-mPMG 可以从基因组规模的途径图谱中提取代谢网络的子系统，以降低模拟模型的复杂性，并自动构建动态模拟器来评估代谢物的实验观察到的行为。使用这个工具，我们表明随机模拟可以再现拟南芥中天冬氨酸生物合成的实验观察到的动力学。在这个模拟中，SS-mPMG 从已发表的数据库中提取代谢网络子系统。使用遗传算法确定模拟所需的参数，以将模拟结果拟合到实验数据。我们期望 SS-mPMG 和 SS-GA 将帮助研究人员创建相关的代谢网络，并根据代谢组学数据进行代谢反应的模拟。
