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Predicting microbial interactions with approaches based on flux balance analysis: an evaluation
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Predicting microbial interactions with approaches based on flux balance analysis: an evaluation
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Journal Article
Predicting microbial interactions with approaches based on flux balance analysis: an evaluation
2024
Overview
Background
Given 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.
Results
Here, 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.
Conclusions
Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Animals
/ Big Data and Machine Learning in Bioinformatics and Medical Informatics
/ Biomass
/ Biomedical and Life Sciences
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ Ecology
/ Enzymes
/ Genomes
/ Growth
/ Humans
/ Methods
/ Mice
/ Microbiota (Symbiotic organisms)
/ Proteins
