A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

Abstract

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

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

“…In agreement with this, Magnusdottir et al [ 51 ] compared the relationship between a standard and condition-specific GEM to the one between the genome of a species and its transcriptome. In another FBA tool evaluation, Scott Jr et al [ 52 ] also highlighted the need for tool improvement regarding quantitative predictions.…”

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

“…In an IBM model, each individual can be provided with a GEM, which can be used to predict its metabolism with FBA. Such a framework has been already included in platforms like BacArena (Bauer et al., 2017 ), ACBM (Karimian & Motamedian, 2020 ), IndiMeSH (Borer et al., 2019 ) or CROMICS (Angeles‐Martinez & Hatzimanikatis, 2021 ), that were recently compared (Scott et al., 2023 ). These frameworks allow spatialisation: the IBM can include movement functions modelling microbial motility, and complex multi‐physics features, for example, the complex diffusive pattern in the rhizospheric area for IndiMesh.…”

Microbiome Modeling: A Beginner’s Guide

“…Analysis methods for constraint-based microbiome models comprise graph-based analyses, optimization-based approaches, dynamic analyses, and spatiotemporal analyses [18,195,206]. Optimization-based approaches typically extend the linear optimization problem from FBA (Section 6.3.4) by additional constraints for microbiome composition and microbiome growth.…”

“…The resulting optimization problem becomes linear and can be analyzed by FBA [76]. Optimization-based approaches are generally used to simulate metabolic fluxes, microbiome interactions, microbiome composition, or growth rates using compartmentalized microbiome models [206]. Overviews on all analysis methods are given in [18,195] and an evaluation of methods and software can be found in [206].…”