Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
Metab Eng. 2024 Mar;82:123-133. doi: 10.1016/j.ymben.2024.02.002. Epub 2024 Feb 7.
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 https://github.com/maranasgroup/KETCHUP.
大规模动力学模型提供了一种计算手段，可以将代谢反应通量与代谢物浓度和酶水平动态联系起来，同时符合底物水平的调节。然而，开发广泛适用的框架来有效地和稳健地对模型进行参数化仍然是一个挑战。挑战源于通量和/或浓度数据的异质性、稀缺性和难以获得，也源于基础参数识别问题的计算困难。参数化的计算需求、获得的参数解决方案的退化以及结果的可解释性迄今为止限制了大规模动力学模型的广泛应用，尽管它们具有潜力。在这里，我们引入了使用 Pyomo 捕获异质数据集的动力学估计工具 (KETCHUP)，这是一个灵活的参数估计工具，利用原始对偶内点算法来解决一个非线性规划 (NLP) 问题，该问题确定了一组能够再现野生型和扰动代谢网络中非稳态通量和浓度的参数。KETCHUP 与以前参数化的大规模动力学模型进行了基准测试，与 K-FIT 工具相比，收敛速度至少快一个数量级，同时获得更好的数据拟合。这个多功能的工具箱接受不同的动力学描述、代谢通量、酶水平和代谢物浓度，无论是在稳态还是非稳态条件下，以实现稳健的动力学模型构建和参数化。KETCHUP 支持 SBML 格式，可以在 https://github.com/maranasgroup/KETCHUP 上访问。
