ArticleBMC bioinformatics2024
Predicting microbial interactions with approaches based on flux balance analysis: an evaluation.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Describing the persistence landscape for introducing microbes into complex communities.Applied and environmental microbiology · 2026Review
- Predictive multispecies constraint-based metabolic modeling: case studies and best practices.Briefings in bioinformatics · 2026Review
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- Characterization and Correlation of Microbial Communities and Volatile Flavor Compounds in Cabbage Kimchi.Food science & nutrition · 2025Article
- Unveiling gut microbiome divergence in sellar-parasellar masses and brain tumors: A link beyond the skull.Neurosurgical review · 2025Article
- Biophysical metabolic modeling of complex bacterial colony morphology.Cell systems · 2025Article
- Designing synthetic microbial communities for enhanced anaerobic waste treatment.Applied and environmental microbiology · 2025Review
- Influence of Amino Acids on Quorum Sensing-Related Pathways inMetabolites · 2025Article
- Deciphering respiratory viral infections by harnessing organ-on-chip technology to explore the gut-lung axis.Open biology · 2025Review
- Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin-degrading consortium.Nature communications · 2025Article
- Mathematical models of the colonic microbiota: an evaluation of accuracy usingFrontiers in nutrition · 2025Article
- Article
- GEM-based computational modeling for exploring metabolic interactions in a microbial community.PLoS computational biology · 2024Article
- Top-down and bottom-up microbiome engineering approaches to enable biomanufacturing from waste biomass.Journal of industrial microbiology & biotechnology · 2024Review
- Resource sharing of an infant gut microbiota synthetic community in combinations of human milk oligosaccharides.The ISME journal · 2024Article
- Transporter annotations are holding up progress in metabolic modeling.Frontiers in systems biology · 2024Article
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Authors and funding
4 authors.
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Abstract
backgroundGiven a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed.
resultsHere, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data.
conclusionsPrediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.
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