Generating large omics datasets has become routine for gaining insights into cellular processes, yet deciphering these datasets to determine metabolic states remains challenging. Kinetic models can help integrate omics data by explicitly linking metabolite concentrations, metabolic fluxes and enzyme levels. Nevertheless, determining the kinetic parameters that underlie cellular physiology poses notable obstacles to the widespread use of these mathematical representations of metabolism. Here we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration of diverse omics data and other relevant information, including extracellular medium composition, physicochemical data and expertise of domain specialists, RENAISSANCE accurately characterizes intracellular metabolic states in Escherichia coli. It also estimates missing kinetic parameters and reconciles them with sparse experimental data, substantially reducing parameter uncertainty and improving accuracy. This framework will be valuable for researchers studying metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnology.

生成大型组学数据集已成为深入了解细胞过程的常规数据，但破译这些数据集以确定代谢状态仍然具有挑战性。动力学模型可以通过明确链接代谢物浓度、代谢通量和酶水平来帮助整合组学数据。然而，确定细胞生理学基础的动力学参数对这些新陈代谢的数学表示的广泛使用构成了明显的障碍。在这里，我们介绍了 RENAISSANCE，这是一个生成式机器学习框架，用于有效地参数化具有与实验观察相匹配的动力学特性的大规模动力学模型。通过无缝集成各种组学数据和其他相关信息，包括细胞外培养基组成、物理化学数据和领域专家的专业知识，RENAISSANCE 准确表征了大肠杆菌的细胞内代谢状态。它还可以估计缺失的动力学参数，并将其与稀疏的实验数据进行协调，从而大大降低参数不确定性并提高准确性。该框架对于研究代谢变化的研究人员来说很有价值，这些变化涉及健康和生物技术中代谢物和酶水平以及酶活性的变化。