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

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. Author Summary Constraint-based modeling has become one of the most successful approaches for modeling large-scale biochemical networks. There are nowadays hundreds of genome-scale reconstructions of metabolic networks available for a wide variety of organisms ranging from bacteria to human cells. One of the limitations of the constraint-based approach is that it describes the cellular phenotype simply in terms of biochemical reaction rates, in a way that is disconnected from other biological processes such as genetic regulation. In order to overcome this limitation, different approaches for integration of gene expression data into constraint-based models have been developed during the past few years. However, all the methods developed so far have only been tested using isolated case studies. In this work, we elaborate a detailed survey of these methods, and perform a critical and quantitative evaluation of a selected subset of methods, using experimental datasets that include different organisms and conditions. This study highlights some of the current limitations in many of these methods, and reveals that no method published so far systematically outperforms the others.