Childs Dorothee, Grimbs Sergio, Selbig Joachim
Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
Bioinformatics. 2015 Jun 15;31(12):i214-20. doi: 10.1093/bioinformatics/btv243.
Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system's Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling.
Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase.
结构动力学建模（SKM）是一个用于分析代谢稳态在扰动下是否保持稳定的框架，无需关于各个速率方程的详细知识。它提供了系统雅可比矩阵的一种表示，该表示仅取决于网络结构、稳态测量值以及稳态下的弹性系数。对于一个测量得到的稳态，可以通过生成大量具有随机采样弹性系数的SKM并评估所得的雅可比矩阵来推导稳定性标准。可以对弹性系数空间进行统计分析，以检测对扰动响应有显著贡献的网络位置。在此，我们通过检查蒙特卡罗采样过程中创建的弹性系数组合的动力学可行性来扩展这种方法。
使用一组小型示例系统，我们表明，如果将大多数采样得到的SKM转换回动力学模型，它们会产生负的动力学参数。为克服这个问题，制定了一个简单的标准来减轻此类不可行模型的影响。在评估了小型示例途径后，该方法被用于研究神经元三羧酸循环的两个稳态以及负责其稳定性或不稳定性的内在机制。统计弹性系数分析结果证实，几个弹性系数共同协调以控制稳定性，并且潜在不稳定性的主要来源是α-酮戊二酸脱氢酶的突变。
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