SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis

Maria Kogadeeva and Nicola Zamboni

PLOS Computational Biology, 2016, vol. 12, issue 9, 1-19

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.Author Summary: Living cells adapt to ever-changing environments by regulating metabolic fluxes, the rates of nutrient flow through the metabolic network, to produce metabolites that are currently in demand. 13C-labeling techniques coupled with metabolic flux analyses are widely used to estimate metabolic fluxes and provide insights into cellular physiology and adaptation relevant in biological, biomedical and biotechnological applications. However, the existing methods are either computationally costly, or applicable to a limited amount of biological systems. Here, we combined surrogate modeling with machine learning to present SUMOFLUX, a generalized method for 13C flux ratio analysis. We validated our method by resolving canonical flux ratios in eight Escherichia coli mutants with known metabolic phenotypes and estimated a novel flux ratio for this dataset. We demonstrated scalability of SUMOFLUX and its application for experimental design by applying it to a cohort of 121 Bacillus subtilis mutants. SUMOFLUX, alone or in combination with global flux analysis methods, can be applied to resolve flux ratios in virtually any biological setup, and paves the way to high-throughput flux profiling.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005109

DOI: 10.1371/journal.pcbi.1005109

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