Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at .

中文翻译：

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KETCHUP：使用具有不同参考状态的多个数据集对大规模动力学模型进行参数化

大规模动力学模型提供了计算手段，将代谢反应通量与代谢物浓度和酶水平动态联系起来，同时也符合底物水平调节。然而，开发广泛适用的框架来高效、稳健地参数化模型仍然是一个挑战。由于获取通量和/或浓度数据的异质性、缺乏性和困难，而且由于潜在参数识别问题的计算困难，出现了挑战。迄今为止，参数化的计算需求、所获得的参数解的简并性以及结果的可解释性都限制了大规模动力学模型的广泛采用，尽管它们具有潜力。在此，我们介绍使用 Pyomo 捕获异质数据集的动力学估计工具 (KETCHUP)，这是一种灵活的参数估计工具，它利用原对偶内点算法来解决非线性规划 (NLP) 问题，该问题识别一组能够概括的参数野生型和扰动代谢网络中的（非）稳态通量和浓度。KETCHUP 以先前参数化的大规模动力学模型为基准，证明其收敛速度比工具 K-FIT 至少快一个数量级，同时获得更好的数据拟合。该多功能工具箱在稳态或静态条件下接受不同的动力学描述、代谢通量、酶水平和代谢物浓度，以实现稳健的动力学模型构建和参数化。KETCHUP 支持 SBML 格式，可以通过 访问。