Dataset series Z1.

Kinetic models mechanistically link enzyme levels, metabolite concentrations, and allosteric regulation to metabolic reaction fluxes. This coupling allows for the quantitative elucidation of the dynamics of the evolution of metabolite concentrations and metabolic fluxes as a function of time. So far, most large-scale kinetic model parameterizations are carried out using mostly steady-state flux measurements supplemented with metabolomics and/or proteomics data when available. Even though the parameterized kinetic model can trace a temporal evolution of the system, lack of anchoring to temporal data reduces confidence in the dynamics predictions. Notably, the simulation of enzymatic cascade reactions requires a full description of the dynamics of the system as a steady-state is not applicable given that all measured metabolite concentrations vary with time. Here we describe how kinetic parameters fitted to the dynamics of single-enzyme assays remain accurate for the simulation of multi-enzyme cell-free systems. Herein, we demonstrate two extensions for the Kinetic Estimation Tool Capturing Heterogeneous datasets Using Pyomo (KETCHUP) software tool for parameterizing a kinetic model of the cell-free kinetics of formate dehydrogenase (FDH) and 2,3-butanediol dehydrogenase (BDH) through the use of time-course data across various initial conditions. An implemented extension of KETCHUP allowing for the reconciliation of measurement time-lag errors present in datasets was used to parameterize kinetic models using multiple datasets. By combining the kinetic parameters identified by the FDH and BDH assays, accurate simulation of the binary FDH-BDH system was achieved.动力学模型（kinetic model）可从机制层面将酶水平、代谢物浓度、别构调控与代谢反应通量建立关联。这种关联使得我们能够定量阐明随时间演化的代谢物浓度与代谢反应通量的动力学过程。迄今为止，绝大多数大规模动力学模型的参数化工作，主要依赖稳态通量测量数据，并在可获取时辅以代谢组学和/或蛋白质组学数据。尽管已完成参数化的动力学模型能够追踪系统的时序演化过程，但缺乏时序数据锚定会降低其动力学预测的可信度。值得注意的是，酶级联反应的模拟需要对系统动力学进行完整描述，因为此时稳态假设不再适用——所有被测代谢物浓度均随时间发生变化。本研究阐明了适配单酶测定动力学所得的动力学参数，何以仍可准确用于多酶无细胞系统的模拟。本研究展示了基于Pyomo的异质数据集捕获动力学估计工具（Kinetic Estimation Tool Capturing Heterogeneous datasets Using Pyomo，以下简称KETCHUP）的两项扩展功能：通过跨多种初始条件的时序过程数据，为甲酸脱氢酶（FDH）与2,3-丁二醇脱氢酶（BDH）的无细胞动力学模型完成参数化。本研究实现了KETCHUP的一项扩展功能，可校正数据集中存在的测量时滞误差，并借助多组数据集完成动力学模型的参数化工作。通过整合FDH与BDH酶活测定得到的动力学参数，本研究成功实现了FDH-BDH二元系统的精准模拟。