ArticleMicrobial biotechnology2021
MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes.
Article in Microbial biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 27 citations in OpenAlex.
- Synthetic microbial consortia based on quorum-sensing for disease therapy.Bioresources and bioprocessing · 2025Review
- Enabling next-generation anaerobic cultivation through biotechnology to advance functional microbiome research.Nature biotechnology · 2025Review
- Vaccine-enhanced competition permits rational bacterial strain replacement in the gut.Science (New York, N.Y.) · 2025Article
- Function-based selection of synthetic communities enables mechanistic microbiome studies.The ISME journal · 2025Article
- Rationally designed microbial communities in agri-food production systems: from research to market.ISME communications · 2025Article
- Diagnosing and engineering gut microbiomes.EMBO molecular medicine · 2024Review
- A cross-systems primer for synthetic microbial communities.Nature microbiology · 2024Review
- Designing function-specific minimal microbiomes from large microbial communities.NPJ systems biology and applications · 2024Article
- Strategies for tailoring functional microbial synthetic communities.The ISME journal · 2024Review
- Synthetic microbial communities (SynComs) of the human gut: design, assembly, and applications.FEMS microbiology reviews · 2023Article
- Toward FAIR Representations of Microbial Interactions.mSystems · 2022Article
- Microbiome-based interventions to modulate gut ecology and the immune system.Mucosal immunology · 2022Review
- Synthetic biology beyond borders.Microbial biotechnology · 2021Article
- MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes.Microbial biotechnology · 2021Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Environmental and host-associated microbial communities are complex ecosystems, of which many members are still unknown. Hence, it is challenging to study community dynamics and important to create model systems of reduced complexity that mimic major community functions. Therefore, we developed MiMiC, a computational approach for data-driven design of simplified communities from shotgun metagenomes. We first built a comprehensive database of species-level bacterial and archaeal genomes (n = 22 627) consisting of binary (presence/absence) vectors of protein families (Pfam = 17 929). MiMiC predicts the composition of minimal consortia using an iterative scoring system based on maximal match-to-mismatch ratios between this database and the Pfam binary vector of any input metagenome. Pfam vectorization retained enough resolution to distinguish metagenomic profiles between six environmental and host-derived microbial communities (n = 937). The calculated number of species per minimal community ranged between 5 and 11, with MiMiC selected communities better recapitulating the functional repertoire of the original samples than randomly selected species. The inferred minimal communities retained habitat-specific features and were substantially different from communities consisting of most abundant members. The use of a mixture of known microbes revealed the ability to select 23 of 25 target species from the entire genome database. MiMiC is open source and available at https://github.com/ClavelLab/MiMiC.
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Registered trials
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