Choudhury Subham, Moret Michael, Salvy Pierre, Weilandt Daniel, Hatzimanikatis Vassily, Miskovic Ljubisa
Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Present Address: Cambrium GmBH, Berlin, Germany.
Nat Mach Intell. 2022;4(8):710-719. doi: 10.1038/s42256-022-00519-y. Epub 2022 Aug 30.
Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the lack of kinetic data, traditional kinetic modelling often yields only a few or no kinetic models with desirable dynamical properties, making the analysis unreliable and computationally inefficient. We present REKINDLE (Reconstruction of Kinetic Models using Deep Learning), a deep-learning-based framework for efficiently generating kinetic models with dynamic properties matching the ones observed in cells. We showcase REKINDLE's capabilities to navigate through the physiological states of metabolism using small numbers of data with significantly lower computational requirements. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in biotechnology and health.
代谢动力学模型通过机理关系将代谢通量、代谢物浓度和酶水平联系起来，使其对于理解、预测和优化生物体的行为至关重要。然而，由于缺乏动力学数据，传统的动力学建模通常只能产生少数几个或根本没有具有理想动力学特性的动力学模型，从而使分析变得不可靠且计算效率低下。我们提出了REKINDLE（使用深度学习重建动力学模型），这是一个基于深度学习的框架，用于高效生成具有与细胞中观察到的动态特性相匹配的动力学模型。我们展示了REKINDLE利用少量数据以显著更低的计算需求来遍历代谢生理状态的能力。结果表明，数据驱动的神经网络吸收了代谢网络的隐含动力学知识和结构，并生成了具有定制特性和统计多样性的动力学模型。我们预计我们的框架将推进我们对代谢的理解，并加速未来在生物技术和健康领域的研究。
