Abstract
Metabolic fluxes are a cornerstone of cellular physiology that emerge from a complex interplay of enzymes, carriers, and nutrients. The experimental assessment of in vivo intracellular fluxes using stable isotopic tracers is essential if we are to understand metabolic function and regulation. Flux estimation based on 13C or 2H labeling relies on complex simulation and iterative fitting; processes that necessitate a level of expertise that ordinarily preclude the non-expert user. To overcome this, we have developed SUMOFLUX, a methodology that is broadly applicable to the targeted analysis of 13C-metabolic fluxes. By combining surrogate modeling and machine learning, we trained a predictor to specialize in estimating flux ratios from measurable 13C-data. SUMOFLUX targets specific flux features individually, which makes it fast, user-friendly, applicable to experimental design and robust in terms of experimental noise and exchange flux magnitude. Collectively, we predict that SUMOFLUX's properties realistically pave the way to high-throughput flux analyses.
Highlights
Metabolic fluxes describe the in vivo flow of organic matter through the biochemical reaction network, as defined by enzymes and transporters
We considered the study of glucose metabolism in E. coli as described by flux ratio analyses using manually derived analytic equations [15]
Beyond the speed and ease with which predictors can be generated for calculating metabolic flux ratios from 13C data, SUMOFLUX offers the additional benefits of robust prediction, the option to vary and optimize experimental design, and the estimation of novel ratios that we explore
Summary
Metabolic fluxes describe the in vivo flow of organic matter through the biochemical reaction network, as defined by enzymes and transporters. Cells grown in the presence of 13C-enriched substrates incorporate heavy isotopes throughout their metabolic networks according to carbon fluxes and produce characteristic 13C patterns in metabolites and products. Some of these can be measured by mass spectrometry or nuclear magnetic resonance and can be used to deduce fluxes using two basic approaches. The first is global isotopomer balancing, which seeks to estimate all metabolic fluxes by iterative fitting [5,6,7,8,9,10]. Troubleshooting heavily relies on expert knowledge [8]
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