Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.
Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.
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通过同位素标记模式的深度学习确定代谢通量。
DOI：
10.1101/2023.11.06.565907
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Park,JunyoungO
中科院分区：
文献类型：
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作者：
Law,RichardC;O'Keeffe,Samantha;Nurwono,Glenn;Ki,Rachel;Lakhani,Aliya;Lai,Pin-Kuang;Park,JunyoungO
流动组学提供代谢状态的直接读数，但依赖于间接测量。稳定的同位素示踪剂在我们测量的代谢物上印记依赖于通量的同位素标记模式；然而，标记模式和通量之间的关系仍然难以捉摸。在这里，我们创新了一个名为ML-Flux的两阶段机器学习框架，它简化了从同位素跟踪到代谢流量的量化。我们通过模拟从26个13C-葡萄糖、2H-葡萄糖和13C-谷氨酰胺示踪剂开始的五个通用代谢模型在可行的流量空间内的原子跃迁来训练机器学习模型。ML-Flux使用基于深度学习的归因法，将标记模式的不同测量作为输入，并使用连续的神经网络将随后的全面标记信息转换为代谢通量。使用ML-Flux和多同位素示踪，我们通过中心碳代谢获得的通量与最小二乘法相当，但速度快了几个数量级。ML-Flux被部署为一个网络工具，以扩大代谢通量定量的可及性，并提供关于代谢的可操作信息。
Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.