Context-Specific Metabolic Networks Are Consistent with Experiments

By Scott A. Becker, Bernhard O. Palsson. PLoS Comput. Biol., 2008

624 citations recorded by the source corpus.

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are “genome-scale” and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3a14af1df5694e3e0147623700914b41e853b4d7

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Citations in the corpus

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, methodology): “This was changed to use continuous values as in the original publication [19].”
Constraint-based models predict metabolic and associated cellular functions (2014, methodology): “…reaction bounds on the basis of mRNA and protein expression data, either by setting the bounds to zero for reactions that correspond to absent transcripts and proteins 34,35 (see the figure, part a ) or by linearly adjusting the bounds on the basis of transcript and protein abundances 103 .”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “| Dynamic multispecies metabolic modelling (DMMM) 105 and model-building algorithm (MBA) 107 use high-throughput data and model simulations to identify which pathways were likely to be expressed and active in the cells when they were sampled.”
Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis (2010, methodology): “For determining on/off states of a gene, the data were binarized with respect to a fixed low value threshold for all genes similar to the method of Becker and Palsson (41).”
iMAT: an integrative metabolic analysis tool (2010, neutral): “The methods of (Åkesson, Förster et al. 2004; Becker and Palsson 2008) use gene expression data to identify genes that are absent or likely to be absent in certain contexts and search for metabolic states that prevent (or minimize) the flux through the associated metabolic reactions.”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “In the same spirit, a method was recently introduced to evaluate the consistency of gene expression datasets with metabolic objectives, and identify subsets of active reactions that best correlate with expressed genes and metabolic objectives (Becker & Palsson, 2008).”
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 (2007, neutral): “More recently, transcriptome data have been used to reduce the size of the set of computed feasible state”
Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT (2012, methodology): “The first of these algorithms was the Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm [43], which uses mRNA expression data as Figure 4.”
Itaconate Links Inhibition of Succinate Dehydrogenase with Macrophage Metabolic Remodeling and Regulation of Inflammation (2016, methodology): “, 2012) and an algorithm similar to GIMME (Becker and Palsson, 2008) and MADE (Jensen and Papin, 2011), we simulated fluxes in untreated and DI-treated conditions based on their consistency with the RNA-seq data for each condition.”
Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “, 2010) and GIMME (Becker and Palsson, 2008).”
Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production (2009, methodology): “Application to Genome Scale M. tuberculosis Metabolic Model Two genome-scale metabolic models are available for M. tuberculosis, namely those of Beste et al. [6] and Jamshidi and Palsson [19].”