ArticleBioinformatics (Oxford, England)2018
Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Predictive multispecies constraint-based metabolic modeling: case studies and best practices.Briefings in bioinformatics · 2026Review
- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
- Metabolite coupling analysis and metabolite-flux coupling analysis of genome-scale metabolic models.Frontiers in bioinformatics · 2026Article
- IGM: Integrated Gene-expression Modeling for multi-condition flux-preserving genome-scale metabolic models.PloS one · 2026Article
- Enhancing genome-scale metabolic models with kinetic data: resolving growth and citramalate production trade-offs inBioinformatics advances · 2025Article
- Integration of proteomic data with genome-scale metabolic models: A methodological overview.Protein science : a publication of the Protein Society · 2024Review
- Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.Molecular systems biology · 2024Article
- Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024Review
- ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology.BMC bioinformatics · 2023Article
- COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.Briefings in bioinformatics · 2023Article
- Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling.Bioinformatics (Oxford, England) · 2023Article
- Article
- Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.Computational and structural biotechnology journal · 2023Article
- All Driven by Energy Demand? Integrative Comparison of Metabolism of Enterococcus faecalis Wildtype and a Glutamine Synthase Mutant.Microbiology spectrum · 2022Article
- Integration of Aspergillus niger transcriptomic profile with metabolic model identifies potential targets to optimise citric acid production from lignocellulosic hydrolysate.Biotechnology for biofuels and bioproducts · 2022Article
- A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Exploring synergies between plant metabolic modelling and machine learning.Computational and structural biotechnology journal · 2022Review
- Elucidating Plant-Microbe-Environment Interactions Through Omics-Enabled Metabolic Modelling Using Synthetic Communities.Frontiers in plant science · 2022Article
- GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction.PLoS computational biology · 2021Article
- Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment.Cancers · 2021Review
Corrections and comments
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2 authors.
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Abstract
Motivation: Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models. Results: In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA. Availability and implementation: Code is available in the Supplementary data available at Bioinformatics online. Supplementary information: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.