Evolutionary engineering of synthetic microbial consortia in bioproduction
File(s)
Published version
Author(s)
Ruiz-Sanchis, Diego
Ledesma-Amaro, Rodrigo
Type
Journal Article
Abstract
Microbial consortia offer significant advantages over monocultures in biotechnological applications, including access to a broader metabolic repertoire, functional redundancy and the capacity for division of labour. Adaptive laboratory evolution (ALE) has similarly proven to be a powerful tool in metabolic engineering, uncovering solutions inaccessible through rational design alone. Despite their individual potential, the intersection of ALE and synthetic microbial consortia in bioproduction contexts remains underexplored. This review examines recent advances at this intersection, with a focus on bottom-up synthetic consortia and co-cultures.
Artificial selection operates at organismal and supra-organismal levels in microbial communities, and this not only shapes their productive output, but it may also compromise the evolvability of costly production functions. This effect can be limited by engineering ecological interactions that stabilise community composition. ALE and synthetic consortia mutually expand each other's applicability through a variety of implementation logics. However, current approaches predominantly rely on growth-based selection, and we argue that implementing inter-community artificial selection strategies holds considerable untapped potential.
Artificial selection operates at organismal and supra-organismal levels in microbial communities, and this not only shapes their productive output, but it may also compromise the evolvability of costly production functions. This effect can be limited by engineering ecological interactions that stabilise community composition. ALE and synthetic consortia mutually expand each other's applicability through a variety of implementation logics. However, current approaches predominantly rely on growth-based selection, and we argue that implementing inter-community artificial selection strategies holds considerable untapped potential.
Date Issued
2026-10-01
Date Acceptance
2026-08-01
Citation
Current Opinion in Biotechnology, 2026, 101
ISSN
0958-1669
Publisher
Elsevier
Journal / Book Title
Current Opinion in Biotechnology
Volume
101
Copyright Statement
© 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/42543053
PII: S0958-1669(26)00121-7
Subjects
Microbial Consortia
Metabolic Engineering
Directed Molecular Evolution
Synthetic Biology
Biotechnology
Publication Status
Published
Coverage Spatial
England
Article Number
103556
Date Publish Online
2026-08-02