Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors

Abstract

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ2-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

One‐shot 13C15N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

“…Also, low intracellular metabolite concentrations often limit the precise measurements of multivariate MIDs. Therefore, we opted to measure univariate MIDs from the co‐labeling experiment using a single‐quadrupole GC–MS system, which is a robust and often used analytical device for MID analysis (Zamboni et al , 2009; Sundqvist et al , 2022). Although we focus on MS as mainstream analytics, our workflow is transferable and equally applicable to other analytical platforms such as for NMR delivering heteronuclear NMR moieties (Borkum et al , 2017), or a combination of MS and NMR measurements.…”

Integrative teaching of metabolic modeling and flux analysis with interactive python modules

“…By doing so, the learners can observe the difference in 13C‐MFA fits when using the correct or incorrect model specification and how this difference can be obscured even by low levels of experimental noise. This allows instructors to highlight important issues concerning data quality and to discuss model selection, which is rarely addressed but crucially important to metabolic modeling, as discussed in Reference 36.…”

Isotopically non-stationary metabolic flux analysis of heterotrophic Arabidopsis thaliana cell cultures

“…The deduced fluxes and their confidence intervals are critically dependent on the quality of the input labelling data. However, determining the reliability of these data can be problematic ( Sundqvist et al., 2022 ). Here the use of standard deviations calculated from the three replicate samples did not provide statistically acceptable fits.…”