MOTIVATION In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady-state data to estimate parameters in kinetic models. RESULTS Here, we present a scalable methodology to structurally identify parameters for each flux in a kinetic model of metabolism based on the availability of steady-state data. In doing so, we also address the issue of determining the number and nature of experiments for generating steady-state data to estimate these parameters. By using a small metabolic network as an example, we show that most parameters in fluxes expressed by mechanistic enzyme kinetic rate laws can be identified using steady-state data, and the steady-state data required for their estimation can be obtained from selective experiments involving both substrate and enzyme level perturbations. The methodology can be used in combination with other identifiability and experimental design algorithms that use dynamic data to determine the most informative experiments requiring the least resources to perform. AVAILABILITY AND IMPLEMENTATION https://github.com/LMSE/ident. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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使用稳态数据在代谢动力学模型中进行参数识别的可扩展方法。

动力在新陈代谢的动力模型中，参数值确定这些模型预测的动力行为。从体内实验数据估计参数需要参数在结构上可识别，并且数据必须具有足够的信息以估计这些参数。现有的确定代谢动力学模型中参数结构可识别性的方法由于其计算复杂性而只能应用于小型代谢网络模型。另外，先验实验设计是获得用于参数估计的信息性数据的必要性，也没有考虑使用稳态数据来估计动力学模型中的参数。结果在这里，我们提供了一种可扩展的方法，可以根据稳态数据的可用性在结构上确定新陈代谢动力学模型中每个通量的参数。通过这样做，我们还解决了确定用于生成稳态数据以估计这些参数的实验的数量和性质的问题。通过一个小的代谢网络为例，我们表明，可以使用稳态数据来识别机械酶动力学速率定律表示的通量中的大多数参数，并且可以从涉及以下内容的选择性实验中获得估算所需的稳态数据：底物和酶水平的扰动。该方法可以与其他可识别性和实验设计算法结合使用，这些算法使用动态数据来确定需要最少资源来执行的信息最多的实验。可用性和实现https://github.com/LMSE/ident。补充信息补充数据可从Bioinformatics在线获得。