Kinetic models predict the metabolic flows by directly linking metabolite concentrations and enzyme levels to reaction fluxes. Robust parameterization of organism-level kinetic models that faithfully reproduce the effect of different genetic or environmental perturbations remains an open challenge due to the intractability of existing algorithms. This paper introduces Kinetics-based Fluxomics Integration Tool (K-FIT), a robust kinetic parameterization workflow that leverages a novel decomposition approach to identify steady-state fluxes in response to genetic perturbations followed by a gradient-based update of kinetic parameters until predictions simultaneously agree with the fluxomic data in all perturbed metabolic networks. The applicability of K-FIT to large-scale models is demonstrated by parameterizing an expanded kinetic model for E. coli (307 reactions and 258 metabolites) using fluxomic data from six mutants. The achieved thousand-fold speed-up afforded by K-FIT over meta-heuristic approaches is transformational enabling follow-up robustness of inference analyses and optimal design of experiments to inform metabolic engineering strategies.

动力学模型通过将代谢物浓度和酶水平与反应通量直接联系起来来预测代谢流。由于现有算法的难以处理，忠实再现不同遗传或环境扰动影响的生物体级动力学模型的稳健参数化仍然是一个开放的挑战。本文介绍了基于动力学的通量组学集成工具 (K-FIT)，这是一种强大的动力学参数化工作流程，它利用一种新颖的分解方法来识别响应遗传扰动的稳态通量，然后是基于梯度的动力学参数更新，直到同时进行预测同意所有扰动代谢网络中的通量组学数据。K-FIT 对大规模模型的适用性通过参数化扩展的动力学模型来证明使用来自六个突变体的通量组学数据的大肠杆菌（307 个反应和 258 个代谢物）。K-FIT 在元启发式方法上实现的千倍加速具有变革性，能够实现推理分析的后续稳健性和实验的优化设计，以告知代谢工程策略。