Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

Background:
Several computational methods have been developed that integrate
transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic fux 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.
Results:
Here, 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 fuxes.
Conclusion:
We 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.