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 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.

GEM-based computational modeling for exploring metabolic interactions in a microbial community

“…Among species, there can be competitive relationships concerning some metabolites, while for others, interactions may involve parasitic, mutualistic, or non-interactions. Additionally, some of these types of algorithms generally make an overall decision about the positivity or negativity of the relationship [ 34 ]. We compare our proposed algorithms with MRO and the competition score proposed by [ 54 ], which provides us with a comparable metric, as well as OptCom and MICOM, both of which are well-known static methods.…”

“…where v biomass represents the growth rate of an individual in monoculture (mono) or co-culture (co) [34]. When an interaction partner has a positive effect on a species, it leads to an interaction strength ratio above one.…”

“…In genetic engineering of microorganisms with goal of achieving maximum efficiency, it is essential to understand inter-organism interactions and how they compete for resources within the microbial community [ 34 , 35 ]. The exchange of metabolites plays a crucial role in microbial community assembly.…”

Biophysical metabolic modeling of complex bacterial colony morphology

“…We thus clearly see a need to improve automated procedures for model building and gap-filling from microbial genomes. 82,83 Second, in our current model of biomass propagation, the two strains also have identical biophysical properties. In a system with non-identical species, the model of biomass propagation must take into account the differences in cell shape, cell-substrate and cell-cell interaction among different strains.…”

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“…Among species, there can be competitive relationships concerning some metabolites, while for others, interactions may involve parasitic, mutualistic, or non-interactions. Additionally, some of these types of algorithms generally make an overall decision about the positivity or negativity of the relationship [34]. We compare our proposed algorithms with MRO and the competition score proposed by [54], which provides us with a comparable metric, as well as OptCom and MICOM, both of which are…”