Identification of a metabolic reaction network from time-series data of metabolite concentrations.
Identification of a metabolic reaction network from time-series data of metabolite concentrations.
复制标题
DOI：
10.1371/journal.pone.0051212
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Hirai MY
中科院分区：
文献类型：
--
作者：
Sriyudthsak K;Shiraishi F;Hirai MY
最近高通量分析技术的发展使得同时定性鉴定一些代谢物成为可能。相关分析和多变量分析，如主成分分析，已被广泛用于分析这些数据和评估代谢谱之间的相关性。然而，这些分析不能同时识别代谢反应网络和预测网络中代谢物的动态行为。因此，本研究提出了一种由统计技术和数学建模方法相结合的新方法，从代谢物浓度的时间序列数据中识别和预测可能的代谢反应网络，并同时建立其数学模型。首先，用局部估计散点图平滑方法对回归函数与实验数据进行拟合。其次，用双变量格兰杰因果检验对拟合结果进行分析，以确定哪些代谢物引起其他代谢物浓度的变化，并去除相关较少的代谢物。第三，在生化系统理论框架内，利用剩余代谢物建立S系统方程。最后，用Lvenberg-MarQuardt算法估计了包括速率常数和动力学级数在内的参数。通过将无关紧要的动力学级数设置为零，即去除无关紧要的代谢物来迭代估计。从而对反应网络结构进行了辨识，得到了其数学模型。我们的方法使用通用的抑制和激活模型进行了验证，并使用乳酸乳球菌MG1363糖酵解的简化模型测试了其实际应用，该模型提供了代谢物浓度的实际时间序列数据。结果表明，我们的方法是有用的，并提出了一条生产乳酸和醋酸盐的可能途径。结果还表明，该方法准确地指出了乳酸对糖酵解途径可能的强烈抑制。
Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg–Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.
登录
查看更多内容
糖酵解和乳酸乳酸菌属葡萄糖转运的调节。乳酸乳酸和批处理培养物。
影响因子：
6.4
作者：
Papagianni, Maria;Avramidis, Nicholaos;Filiousis, George
通讯作者：
Filiousis, George
影响因子：
4.4
作者：
Neves, AR;Ramos, A;Santos, H
通讯作者：
Santos, H
影响因子：
14.8
作者：
Bujara, Matthias;Schuemperli, Michael;Panke, Sven
通讯作者：
Panke, Sven
影响因子：
4.8
作者：
Neves, AR;Ventura, R;Santos, H
通讯作者：
Santos, H
DOI：
10.1137/0111030
发表时间：
1963-01-01
期刊：
JOURNAL OF THE SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS
影响因子：
--
作者：
MARQUARDT, DW
通讯作者：
MARQUARDT, DW