Abstract
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.
Highlights
The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering
In lieu of in vivo observation, the inference of intracellular fluxes is commonly accomplished through metabolic flux analysis (MFA)
MFA refers to the process of modeling intracellular flux via a stoichiometric balance of metabolic reaction and transport rates [2]
Summary
The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit. In lieu of in vivo observation, the inference of intracellular fluxes is commonly accomplished through metabolic flux analysis (MFA). MFA refers to the process of modeling intracellular flux via a stoichiometric balance of metabolic reaction and transport rates (assuming a “pseudo steady-state” in the form of negligible molecule accumulation) [2]. As cellular reaction networks generally have more reactions than species, the resulting stoichiometry matrix is typically underdetermined. The estimation of a single flux profile requires that the number of unknown reaction rates be equal to or less than the number of molecular species, and this has traditionally been accomplished by observing as many extracellular transport rates as possible. The growing availability of genomic data has opened the door to developing models that may contain thousands of reactions, complicating the calculation of a unique flux profile
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