Abstract
BackgroundSeveral computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values.ResultsHere, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes.ConclusionWe showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.
Highlights
Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accu‐ racy of inferences of intracellular metabolic flux distributions
In order to evaluate the impact of the proportionality constant (PC) after integration of transcriptomics data, we have selected four studies: Ishii et al [21], Holm et al [22] and Gerosa et al [23] for E. coli; and Rintala et al [24] for S. cerevisiae
For the selected datasets and models, we have applied the E-Flux algorithm with varying values of the PC and used the integrated model to predict a selected phenotype measurement
Summary
Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accu‐ racy of inferences of intracellular metabolic flux distributions. Constraint-based modelling has become one of the most successful and widely adopted approaches for modelling cellular metabolic networks [3, 4]. This approach relies on mass balance over intracellular metabolites and the assumption of pseudo-steady-state conditions to determine intracellular metabolic fluxes. Constraint-based genome-scale metabolic models (GEMs) contain the associations between genes and the corresponding reactions through the so-called gene-proteinreaction (GPR) relationships, expressed through logical (or Boolean) functions [5, 6]
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