ArticlePLoS computational biology2021
ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 38 citations in OpenAlex.
- Atomic elementary flux modes explain the steady state flow of metabolites in large-scale flux networks.Cell reports methods · 2026Article
- Flux sampling and graph neural networks for improved gene essentiality prediction in mammalian genome-scale metabolic models.NPJ systems biology and applications · 2026Article
- 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
- Modelling reliable metabolic phenotypes by analysing the context-specific transcriptomics data.NPJ systems biology and applications · 2025Article
- Systems biology and microbiome innovations for personalized diabetic retinopathy management.NPJ systems biology and applications · 2025Review
- In vivo and in silico models of Drosophila for Parkinson's disease.The FEBS journal · 2025Review
- RSEA: A Web Server for Pathway Enrichment Analysis of Metabolic Reaction Sets.Biotechnology and bioengineering · 2025Article
- CFSA: Comparative flux sampling analysis as a guide for strain design.Metabolic engineering communications · 2024Article
- Comparative Proteomics Highlights that GenX Exposure Leads to Metabolic Defects and Inflammation in Astrocytes.Environmental science & technology · 2024Article
- Understanding flux switching in metabolic networks through an analysis of synthetic lethals.NPJ systems biology and applications · 2024Article
- Article
- ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology.BMC bioinformatics · 2023Article
- State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs.Microorganisms · 2023Review
- COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.Briefings in bioinformatics · 2023Article
- A new metabolic model ofLife science alliance · 2023Article
- Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling.Bioinformatics (Oxford, England) · 2023Article
- Article
- Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.Metabolites · 2023Review
- Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.
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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.