Article,
Stoichiometric Representation of Gene–Protein–Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction
PLOS Computational Biology, 12 (10): e1005140+ (Oct 6, 2016)
DOI: 10.1371/journal.pcbi.1005140
Abstract
Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models. Genome-scale models of metabolism enable the exploration of the phenotypic landscape of an organism. Unlike probabilistic approaches such as genome-wide association studies, these models describe the mechanistic link between genotype and phenotype, predicting the response to genetic and environmental perturbations. However, this connection is hampered by the complexity of gene-protein-reaction associations. In this work, we implement a model transformation method that untangles this complexity by allowing gene-wise phenotype predictions using genome-scale models. The transformed model explicitly accounts for the individual flux carried by the enzyme or subunit encoded by each gene. Previously published simulation methods are automatically leveraged by this transformation, enabling new features such as the formulation of objectives and constraints at the gene/protein level. We demonstrate the application of different kinds of analysis and simulation methods, showing in each case how the gene-wise formulation can result in higher prediction accuracy in comparison to experimental data and improve the biological insight that can be obtained from available models.
