BackgroundThe estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying “gross measurement error”. The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit.ResultsIn this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5–10 % range).ConclusionsThe proposed validation method goes beyond traditional detection of “gross measurement error” to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0335-7) contains supplementary material, which is available to authorized users.
Yeast extract, or autolysate, is a required component for many cell-culture media, but its exact constituents and benefits are unknown. Yeast extract contains a diverse assortment of metabolites, often present in complex forms (eg, polypeptides and polynucleotides). This study employs one-dimensional proton nuclear magnetic resonance (1D-1H NMR) spectroscopy to analyze free (ie, readily available) components present in commercially available yeast autolysate. The product is monitored while further subjected to acid hydrolysis, allowing for a more robust understanding of the exact components present, particularly those contained in complex forms. The amino acids and glucose compounds behaved as expected based on other acid hydrolysis studies, and were modelled similarly. Parameter estimation was in strong agreement with pre-hydrolysis targeted 1D-1H NMR profiling. This analysis was expanded to components not as thoroughly investigated and was especially applicable to nucleic compounds. Acid hydrolysis revealed that the yeast extract was approximately 5.4% nucleic material by weight, mostly composed of adenosine, and largely provided by RNA due to the presence of uracil and lack of thymidine. Choline-containing compounds were also positively identified with an observable increase of free choline during hydrolysis. 1D-1H NMR spectroscopy allowed for the simultaneous monitoring of a significant number of yeastextract metabolites, some of which were previously unreported. Utilizing 1D-1H NMR spectroscopy in conjunction with acid hydrolysis led to a more complete view of the compounds present, and accounted for an additional 24% of the yeast-extract mass.
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