Inferring Metabolic States in Uncharacterized Environments Using Gene-Expression Measurements

Abstract

The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.

Multi‐scale modeling of Arabidopsis thaliana response to different CO2 conditions: From gene expression to metabolic flux

“…For instance, using Gene Inactivity Moderated by Metabolism and Expression (GIMME) method, the context‐specific networks for Escherichia coli have been constructed for three types of strains (wild‐type strains, strains evolved to growth on glycerol, strains evolved to growth on lactate) (Becker and Palsson ). For Saccharomy cescerevisiae grown on YPD (yeast‐extract, peptone, dextrose) or YPEtOH (yeast‐extract, peptone, ethanol), the environment‐specific models have been built based on the Exploration of Alternative Metabolic Optima (EXAMO) method (Rossell et al ). For humans, tissue‐specific models by the integrative Metabolic Analysis Tool (iMAT) have been used to predict the tissue‐specific metabolism (Shlomi et al ).…”

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

“…This tool enables the definition of a biological objective to be dependent on the requirements of each cell rather than the entire organism. An extension of iMAT known as the exploration of alternative metabolic optima (EXAMO) enables the design of condition-specific metabolic models for human tissues [103].…”

Applications of genome-scale metabolic network model in metabolic engineering

“…OptStrain [67], OptReg [68], OptForce [72], k-OptForce [16], OptORF [44], CosMos [20] Omics data integration Transcriptome GIMME [5], iMAT [82], GIM 3 E [76], E-Flux [18], PROM [13], MADE [38], tFBA [90], RELATCH [45], TEAM [19], AdaM [89], GX-FBA [60], mCADRE [92], FCGs [43], EXAMO [75], TIGER [37] Proteome GIMMEp [6] Pathway prediction BNICE [29], Cho et al [14], RetroPath [11], PathPred [59], DESHARKY [74], BioPath [94], XTMS [12], GEM-Path [56] phenotype and gene essentiality [24]. Even further, taking advantage of a large set of genome sequences available for various E. coli strains, the GEMs for 55 E. coli strains were used to investigate the variations in gene, reaction and metabolite contents, and the capabilities to adapt to different nutritional environments among the strains [40].…”