ReviewArchives of microbiology2026
Advances, opportunities, and challenges in microbial community construction: from environmental remediation to biomanufacturing.
Review in Archives of microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The construction of artificial microbial communities entails the rational design and assembly of multiple microorganisms into tailored consortia, enabling the execution of specific biological functions under controlled conditions. Propelled by advances in synthetic biology, systems biology, and multi-omics technologies, this strategy has emerged as a promising solution to overcome the inherent limitations of monocultures and natural microbial communities—such as narrow metabolic versatility, poor environmental adaptability, and low controllability—thus exhibiting considerable potential in environmental remediation and biomanufacturing for the sustainable production of high-value compounds. To optimize consortium performance, cross-disciplinary approaches have been developed, including integrating the Design-Build-Test-Learn (DBTL) framework with advanced analytical tools to decipher microbial spatiotemporal interactions, and using AI-driven dynamic modeling to integrate multi-omics data. Future efforts combining dynamic modeling, AI optimization, and cross-disciplinary collaboration will be key to addressing existing challenges. Supplementary measures, such as developing automated equipment and establishing standardized safety frameworks, will further enhance scalability and commercial potential. Ultimately, these advances will fully unlock the capacity of artificial taxa to drive green and sustainable innovations in environmental and industrial fields.
Indexed as
Identifiers
41944847What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.