Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

Abstract

Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.

Inference and prediction of metabolic network fluxes

“…The existing methods for constructing contextspecific models have been classified into three main groups (Robaina Estévez and Nikoloski, 2015) and have been comprehensively compared on a common data set (Machado and Herrgård, 2014). The main aim of these methods is to determine the set of active reactions (i.e.…”

“…The inclusion of flux bounds based on transcript and protein levels has proved to be a useful tool to assess and interpret metabolic behavior between conditions (Töpfer et al, 2013). However, the coordination of several regulatory levels between gene transcription and reaction rates may not result in a direct correspondence between gene expression and fluxes, and this can affect the usefulness of these methods (Machado and Herrgård, 2014). The correspondence between changes in flux and changes in transcript levels in plant tissues was recently investigated (Schwender et al, 2014).…”

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

“…However, their results are still not convincing (62), and probably for that reason the number of CSOMs exploring these capabilities is still scarce, leaving room for the emergence of new branches from previously proposed CSOMs or even entirely new ones.…”

“…Prominent examples are iMAT (59), GIMME (60), and RELATCH (61), which provide alternative objective functions and optimization approaches, combining the principles of constraint-based modeling with the consistency of fluxes with known data. In a recent study (62), these methods have been systematically evaluated, and the results obtained have been far from the ones expected, thus shedding some doubts on their applicability.…”

Bioinformatics computation of metabolic models from sequenced genomes

“…One reason these models have limited accuracy is that they assume every metabolic reaction in the organism is potentially active at a given time, but that is not the case because regulatory processes shut down significant numbers of reactions under a given growth condition. Including regulation within these models is an active area of investigation (Machado and Herrgard, 2014). These models can also predict the ability of the organism to grow under different collections of substrates, growth rate and nutrient uptake rate of an organism.…”