Hendrickx Diana M, Hoefsloot Huub C J, Hendriks Margriet M W B, Vis Daniël J, Canelas André B, Teusink Bas, Smilde Age K
Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands.
Mol Biosyst. 2012 Sep;8(9):2415-23. doi: 10.1039/c2mb25015b. Epub 2012 Jul 11.
Elucidating changes in the distribution of reaction rates in metabolic pathways under different conditions is a central challenge in systems biology. Here we present a method for inferring regulation mechanisms responsible for changes in the distribution of reaction rates across conditions from correlations in time-resolved data. A reversal of correlations between conditions reveals information about regulation mechanisms. With the use of a small in silico hypothetical network, based on only the topology and directionality of a known pathway, several regulation scenarios can be formulated. Confronting these scenarios with experimental data results in a short list of possible pathway regulation mechanisms associated with the reversal of correlations between conditions. This procedure allows for the formulation of regulation scenarios without detailed prior knowledge of kinetics and for the inference of reaction rate changes without rate information. The method was applied to experimental time-resolved metabolomics data from multiple short-term perturbation-response experiments in S. cerevisiae across aerobic and anaerobic conditions. The method's output was validated against a detailed kinetic model of glycolysis in S. cerevisiae, which showed that the method can indeed infer the correct regulation scenario.
阐明不同条件下代谢途径中反应速率分布的变化是系统生物学的核心挑战。在此，我们提出了一种方法，用于从时间分辨数据中的相关性推断导致不同条件下反应速率分布变化的调控机制。条件之间相关性的反转揭示了有关调控机制的信息。通过使用一个仅基于已知途径的拓扑结构和方向性构建的小型计算机模拟假设网络，可以制定几种调控方案。将这些方案与实验数据进行对比，可得到与条件之间相关性反转相关的可能途径调控机制的简短列表。此过程无需详细的动力学先验知识即可制定调控方案，也无需速率信息即可推断反应速率变化。该方法应用于来自酿酒酵母在有氧和无氧条件下的多个短期扰动 - 响应实验的实验时间分辨代谢组学数据。该方法的输出结果通过与酿酒酵母糖酵解的详细动力学模型进行验证，结果表明该方法确实可以推断出正确的调控方案。
