ArticlePLoS computational biology2023
A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- The cervicovaginal gut microbiota axis as a key determinant of systemic physiology.Gut microbes · 2026Review
- MOFA: microbial optimization without forced altruism.BMC bioinformatics · 2026Article
- Predictive multispecies constraint-based metabolic modeling: case studies and best practices.Briefings in bioinformatics · 2026Review
- Virtual Colon: spatiotemporal modeling of metabolic interactions in a computational colonic environment.mSystems · 2026Article
- Personalized Constraint-Based Modeling of Microbial Communities from Metagenomic Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Metabolic modeling unveils potential probiotic roles ofISME communications · 2026Article
- Dynamic Simulation of Growth and Cross-Feeding in Microbiomes with μbialSim.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Synthetic Microbial Cocultivation for Targeted Production of Odd-Chain Carboxylates and Alcohols from Carbon Monoxide.Environmental science & technology · 2025Article
- Controlling metabolic stability of food microbiome for stable indigenous liquor fermentation.NPJ biofilms and microbiomes · 2025Article
- Moving from genome-scale to community-scale metabolic models for the human gut microbiome.Nature microbiology · 2025Review
- Review
- Mathematical models of the colonic microbiota: an evaluation of accuracy usingFrontiers in nutrition · 2025Article
- Genome-scale metabolic modelling reveals interactions and key roles of symbiont clades in a sponge holobiont.Nature communications · 2024Article
- Metabolic modelling uncovers the complex interplay between fungal probiotics, poultry microbiomes, and diet.Microbiome · 2024Article
- Deciphering and designing microbial communities by genome-scale metabolic modelling.Computational and structural biotechnology journal · 2024Review
- Auxotrophy-based curation improves the consensus genome-scale metabolic model of yeast.Synthetic and systems biotechnology · 2024Article
- A Study of the Community Relationships Between Methanotrophs and Their Satellites Using Constraint-Based Modeling Approach.International journal of molecular sciences · 2024Article
- Bacterial community dynamics as a result of growth-yield trade-off and multispecies metabolic interactions toward understanding the gut biofilm niche.BMC microbiology · 2024Article
- Article
- Emerging tools and best practices for studying gut microbial community metabolism.Nature metabolism · 2024Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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