TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

Abstract

BackgroundSeveral methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model.ResultsWe present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae.ConclusionThe TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

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“…For example, 13 C-labeling experiments provide experimentally measured fluxes as inputs for the model simulations [84,85]. Several FBA-based methods also facilitate the integration of transcriptomic, proteomic, and metabolomic data with metabolic models to constrain reactions based on measured RNA or protein levels [86,87]. Thereby, flux distributions are identified which are most consistent with the expression data [88].…”

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“…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].…”

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“…The more experimentally measured fl uxes are available, the better the model prediction will perform to determine the metabolic fl ux vector r . The OMICS data can also be used to develop gene regulatory constraints on r to improve the model prediction (Chandrasekaran and Price 2010 ;Jensen et al 2011 ;Park et al 2007 ) . Figure 2.1b demonstrates the concept of FBA, which determines the fl ux vector r in the example network with the objective function of maximizing the formation of the product P from only the substrate A.…”