Predicting microbial interactions with approaches based on flux balance analysis: an evaluation.

ABSTRACT:

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 publi

SUBMITTER: Joseph C 

PROVIDER: S-EPMC10804772 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature