ArticleNPJ systems biology and applications2024
Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches.
Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Nutrient control enables metabolic reconstruction ofbioRxiv : the preprint server for biology · 2026Article
- Evolutionary signals in metabolic networks of insect endosymbionts revealed through comparative topological modeling.BMC genomics · 2026Article
- Selecting methods for draft GEM generation in multicellular eukaryotes: a comparative analysis.BMC bioinformatics · 2026Article
- Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies.bioRxiv : the preprint server for biology · 2026Article
- Engineering synthetic microbial communities for soil restoration: from rational design to multi-scale biogeochemical applications.Frontiers in microbiology · 2026Review
- Synthetic microbiomes in bioengineered rhizospheres: new frontiers for climate-resilient agriculture.Frontiers in microbiology · 2026Review
- Systems biology and microbiome innovations for personalized diabetic retinopathy management.NPJ systems biology and applications · 2025Review
- Constraint-based metabolic modeling reveals metabolic properties underpinning the unprecedented growth of Chlorella ohadii.The New phytologist · 2025Article
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- A computational framework for inferring species dynamics and interactions with applications in microbiota ecology.NPJ systems biology and applications · 2025Article
- Screening of Potential Drug Targets Based on the Genome-Scale Metabolic Network Model ofCurrent issues in molecular biology · 2025Article
- Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions.Briefings in bioinformatics · 2025Review
- Reducing redundancy and enhancing accuracy through a phylogenetically-informed microbial community metabolic modeling approach.Bioinformatics (Oxford, England) · 2025Article
- A survey of computational approaches for characterizing microbial interactions in microbial mats.Genome biology · 2025Review
- Integration of metatranscriptomics data improves the predictive capacity of microbial community metabolic models.The ISME journal · 2025Article
- Engineered bacteria as living therapeutics: Next-generation precision tools for health, industry, environment, and agriculture.AIMS microbiology · 2025Review
- Genome-Scale Metabolic Models in Fungal Pathogens: Past, Present, and Future.International journal of molecular sciences · 2024Review
- Article
- Integrating biological knowledge for mechanistic inference in the host-associated microbiome.Frontiers in microbiology · 2024Review
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4 authors.
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Abstract
Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.
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