Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data.
Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data.
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DOI：
10.1093/bioinformatics/btx250
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发表时间：
2017-07-15
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影响因子：
--
通讯作者：
Ropers D
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文献类型：
--
作者：
Cinquemani E;Laroute V;Cocaign-Bousquet M;de Jong H;Ropers D
代谢组学的技术进步使得监测细胞外代谢物随时间的浓度成为可能。从这些数据中，可以计算出生长中的细胞群体对代谢物的吸收和排泄速率，从而提供关于细胞内代谢功能的宝贵信息。然而，由于许多原因，这些交换反应的速率的计算在实践中难以实现，特别是噪声测量、不同细胞外代谢物的浓度分布之间的相关性以及由于代谢状态的突然变化而导致的分布的不连续性。我们提出了一种方法，用于精确估计随时间变化的摄取和排泄率从时间序列测量的细胞外代谢物浓度，特别是解决所有上述问题。估计问题制定在一个正规化贝叶斯框架和解决扩展卡尔曼滤波和平滑相结合。所示的方法，以改善基于样条平滑的数据的方法。此外，当应用于两个实际的数据集，该方法恢复了已知的溢出代谢在大肠杆菌和乳酸乳球菌的特征，并提供了证据，乙酸吸收的L。葡萄糖耗尽后的乳酸。结果提出了有趣的观点，为进一步的工作率估计从细胞内代谢物的测量。 估计方法的Matlab代码可与数据集一起在https：//team.inria.fr/ibis/rate-estimation-software/下载。 补充数据可在Bioinformatics在线获得。
Technological advances in metabolomics have made it possible to monitor the concentration of extracellular metabolites over time. From these data, it is possible to compute the rates of uptake and excretion of the metabolites by a growing cell population, providing precious information on the functioning of intracellular metabolism. The computation of the rate of these exchange reactions, however, is difficult to achieve in practice for a number of reasons, notably noisy measurements, correlations between the concentration profiles of the different extracellular metabolites, and discontinuties in the profiles due to sudden changes in metabolic regime. We present a method for precisely estimating time-varying uptake and excretion rates from time-series measurements of extracellular metabolite concentrations, specifically addressing all of the above issues. The estimation problem is formulated in a regularized Bayesian framework and solved by a combination of extended Kalman filtering and smoothing. The method is shown to improve upon methods based on spline smoothing of the data. Moreover, when applied to two actual datasets, the method recovers known features of overflow metabolism in Escherichia coli and Lactococcus lactis, and provides evidence for acetate uptake by L. lactis after glucose exhaustion. The results raise interesting perspectives for further work on rate estimation from measurements of intracellular metabolites. The Matlab code for the estimation method is available for download at https://team.inria.fr/ibis/rate-estimation-software/, together with the datasets. Supplementary data are available at Bioinformatics online.
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