Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.

ABSTRACT: 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 c

SUBMITTER: Law RC 

PROVIDER: S-EPMC10659294 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature