Towards a mechanistic understanding of microalgae–bacteria interactions: integration of metabolomic analysis and computational models
Abstract
Interactions amongst marine microalgae and heterotrophic bacteria drive processes underlying major biogeochemical cycles and are important for many artificial systems. These dynamic and complex interactions span the range from cooperative to competitive, and it is the diverse and intricate networks of metabolites and chemical mediators that are predicted to principally dictate the nature of the relationship at any point in time. Recent advances in technologies to identify, analyze, and quantify metabolites have allowed for a comprehensive view of the molecules available for exchange and/or reflective of organismal interactions, setting the stage for development of mechanistic understanding of these systems. Here, we (i) review the current knowledge landscape of microalgal–bacterial interactions by focusing on metabolomic studies of selected, simplified model systems; (ii) describe the state of the field of metabolomics, with specific focus on techniques and approaches developed for microalga–bacterial interaction studies; and (iii) outline the main approaches for development of mathematical models of these interacting systems, which collectively have the power to enhance interpretation of experimental data and generate novel testable hypotheses. We share the viewpoint that a comprehensive and integrated series of -omics approaches that include theoretical formulations are necessary to develop predictive and mechanistic understanding of these biological entities.
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These models are validated against experimental data, which increases their accuracy in predicting production yields.
“These models are validated against experimental data, which increases their accuracy in predicting production yields [107].”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [107]
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Flagged first, then by citations
Cited by
Sorted by citations
Dębowski, M., Kazimierowicz, J., & Zieliński, M. (2025) · Applied Sciences · doi:10.3390/app15137324 · Cited by 30 · see citing works
- Authors
- Daly, G., Ghini, V., Adessi, A., Fondi, M., Buchan, A., & Viti, C.
- Affiliations
- University of Florence (IT)
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (IT)
- University of Tennessee at Knoxville (US)
- Corresponding author
- Adessi, Alessandra
- Venue
- FEMS Microbiology Reviews
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-05-14
- Volume
- 46
- Issue
- 5
- ISSN
- 0168-6445 (ISSN-L)
- 1574-6976
- OpenAlex
- W4280544239
- Cited by
- 30
- References
- 163
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 6 · 2023: 6 · 2022: 1
- FWCI
- 2.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- FEMS Microbiology Reviews (publishedVersion)
- PubMed (publishedVersion)
- Florence Research (University of Florence) (submittedVersion)
- Funders
- National Science Foundation (OCE-1357242)
- Simons Foundation (735083)
- Topics
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine and coastal ecosystems
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Keywords
- microalgae-bacteria interactions, heterotrophic bacteria, marine microalgae, metabolomics, mathematical modeling, chemical mediators, metabolite exchange, competition and cooperation, biogeochemical cycles, model systems
- MeSH terms
- Bacteria, Computer Simulation, Models, Biological, Metabolomics, Microalgae
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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