ArticleBioinformatics (Oxford, England)2013
GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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61 citing papers in PubMed.
- Selecting methods for draft GEM generation in multicellular eukaryotes: a comparative analysis.BMC bioinformatics · 2026Article
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- Multi-omic data integration and exploiting metabolic models using systems biology approach increase precision in subtyping and early diagnosis of cancer.Quantitative biology (Beijing, China) · 2025Article
- Glutaminase as a metabolic target of choice to counter acquired resistance to Palbociclib by colorectal cancer cells.Oncogene · 2025Article
- Metabolic reaction fluxes as amplifiers and buffers of risk alleles for coronary artery disease.Molecular systems biology · 2025Article
- Integration of metatranscriptomics data improves the predictive capacity of microbial community metabolic models.The ISME journal · 2025Article
- Immunometabolic Effect of Nitric Oxide on Human Macrophages Challenged With the SARS-CoV2-Induced Cytokine Storm. A Fluxomic Approach.Advanced healthcare materials · 2025Article
- Metabolic modeling of host-microbe interactions.Computational and structural biotechnology journal · 2025Review
- State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs.Microorganisms · 2023Review
- Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations.Briefings in bioinformatics · 2023Review
- A High-Quality Genome-Scale Model forACS synthetic biology · 2023Article
- Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.NPJ systems biology and applications · 2023Article
- Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.Metabolites · 2023Review
- Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications.Computational and structural biotechnology journal · 2023Review
- Immunometabolic actions of trabectedin and lurbinectedin on human macrophages: relevance for their anti-tumor activity.Frontiers in immunology · 2023Article
- Genetically personalised organ-specific metabolic models in health and disease.Nature communications · 2022Article
- Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges.Biomolecules · 2022Review
- Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective.Current opinion in systems biology · 2022Article
- Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions.PLoS computational biology · 2022Article
1 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
motivationGenome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed.
resultsGIM(3)E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM(3)E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM(3)E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. AVAILABILITY: GIM(3)E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/ CONTACTS: brianjamesschmidt@gmail.com
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